Childhood dyspraxia predicts adult-onset nonaffective-psychosis-spectrum disorder

DEFF Research Database (Denmark)

Schiffman, Jason; Mittal, Vijay; Kline, Emily

2015-01-01

abnormalities spanning functionally distinct brain networks) specifically predict adult nonaffective-psychosis-spectrum disorders are consistent with a theory of abnormal connectivity, and they highlight a marked early-stage vulnerability in the pathophysiology of nonaffective-psychosis-spectrum disorders.......Several neurological variables have been investigated as premorbid biomarkers of vulnerability for schizophrenia and other related disorders. The current study examined whether childhood dyspraxia predicted later adult nonaffective-psychosis-spectrum disorders. From a standardized neurological...... showed higher scores on the dyspraxia scale predict nonaffective-psychosis-spectrum disorders relative to other psychiatric disorders and no mental illness outcomes, even after controlling for genetic risk, χ2 (4, 244) = 18.61, p

Assessment of protein disorder region predictions in CASP10

KAUST Repository

Monastyrskyy, Bohdan; Kryshtafovych, Andriy; Moult, John; Tramontano, Anna; Fidelis, Krzysztof

2013-01-01

The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

Assessment of protein disorder region predictions in CASP10

KAUST Repository

Monastyrskyy, Bohdan

2013-11-22

The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

Does acute stress disorder predict posttraumatic stress disorder following bank robbery?

DEFF Research Database (Denmark)

Hansen, M.; Elklit, A.

2013-01-01

Unfortunately, the number of bank robberies is increasing and little is known about the subsequent risk of posttraumatic stress disorder (PTSD). Several studies have investigated the prediction of PTSD through the presence of acute stress disorder (ASD). However, there have only been a few studies...... following nonsexual assault. The present study investigated the predictive power of different aspects of the ASD diagnosis and symptom severity on PTSD prevalence and symptom severity in 132 bank employees. The PTSD diagnosis, based on the three core symptom clusters, was best identified using cutoff scores...... on the Acute Stress Disorder scale. ASD severity accounted for 40% and the inclusion of other risk factors accounted for 50% of the PTSD severity variance. In conclusion, results indicated that ASD appears to predict PTSD differently following nonsexual assault than other trauma types. ASD severity...

Does Acute Stress Disorder Predict Posttraumatic Stress Disorder Following Bank Robbery?

Science.gov (United States)

Hansen, Maj; Elklit, Ask

2013-01-01

Unfortunately, the number of bank robberies is increasing and little is known about the subsequent risk of posttraumatic stress disorder (PTSD). Several studies have investigated the prediction of PTSD through the presence of acute stress disorder (ASD). However, there have only been a few studies following nonsexual assault. The present study…

Study on discriminant analysis by military mental disorder prediction scale for mental disorder of new recruits

Directory of Open Access Journals (Sweden)

Li-yi ZHANG

2011-11-01

Full Text Available Objective To examine the predictive role of the Military Mental Disorder Prediction Scale on the mental disorder of new recruits.Methods The present study examined 115 new recruits diagnosed with mental disorder and 115 healthy new recruits.The recruits were tested using the Military Mental Disorder Prediction Scale.The discriminant function was built by discriminant analysis method.The current study analyzed the predictive value of 11 factors(family medical record and past medical record(X1,growth experience(X2,introversion(X3,stressor(X4,poor mental defense(X5,social support(X6,psychosis(X7,depression(X8,mania(X9,neurosis(X10,and personality disorder(X11 aside from lie factor on the mental disorder of new recruits.Results The mental disorder group has higher total score and factor score in family medical record and past medical record,introversion,stressor,poor mental defense,social support,psychosis,depression,mania,neurosis,personality disorder,and lie than those of the contrast group(P < 0.01.For the score of growth experience factor,that of the mental disorder group is higher than the score of the contrast group(P < 0.05.All 11 factors except the lie factor in the Mental Disorder Prediction Scale are taken as independent variables by enforced introduction to obtain the Fisher linear discriminant function as follows: The mental disorder group=-7.014-0.278X1+1.556X2+1.563X3+0.878X4+0.183X5-0.845X6-0.562X7-0.353X8+1.246X9-0.505X10+1.029X11.The contrast group=-2.971+0.056X1+2.194X2+0.707X3+0.592X4-0.086X5-0.888X6-0.133X7-0.360X8+0.654X9-0.467X10+0.308X11.The discriminant function has an accuracy rate of 76.5% on the new recruits with mental disorders and 100% on the healthy new recruits.The total accurate discrimination rate is 88.3% and the total inaccurate discrimination rate is 11.7%.Conclusion The Military Mental Disorder Prediction Scale has a high accuracy rate on the prediction of mental disorder of new recruits and is worthy of

Predicting Attention-Deficit/Hyperactivity Disorder and Oppositional Defiant Disorder from Preschool Diagnostic Assessments

Science.gov (United States)

Harvey, Elizabeth A.; Youngwirth, Sara D.; Thakar, Dhara A.; Errazuriz, Paula A.

2009-01-01

The present study examined the power of measures of early preschool behavior to predict later diagnoses of attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD)/conduct disorder (CD). Participants were 168 children with behavior problems at age 3 who underwent a multimethod assessment of ADHD and ODD symptoms and…

Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

Directory of Open Access Journals (Sweden)

Waqasuddin Khan

Full Text Available Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58.Next, we trained a bidirectional recurrent neural network (BRNN using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72 showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

Patient factors predicting early dropout from psychiatric outpatient care for borderline personality disorder.

Science.gov (United States)

De Panfilis, Chiara; Marchesi, Carlo; Cabrino, Chiara; Monici, Alberto; Politi, Virginia; Rossi, Matteo; Maggini, Carlo

2012-12-30

Despite obvious clinical need, factors underlying early treatment discontinuation among 'real world' borderline personality disorder (BPD) patients are still unknown. This study investigates individual characteristics that can predict early (Disorders, fourth edition (DSM-IV) Personality. Sociodemographic, clinical and personality variables potentially relevant for dropout were assessed for all participants at baseline. Early dropouts (n=54) were compared to continuers (n=108) on all measures. Logistic regression was then used to identify independent predictors of early dropout. A history of suicide attempts predicted early discontinuation, whereas the presence of an eating disorder and of avoidant personality features protected from early dropout. If confirmed, these findings may help clinicians operating in general psychiatric settings with estimating the risk of premature treatment discontinuation, and stress the need to specifically address suicidal behaviours in order to improve treatment retention among borderline outpatients. In this regard, implementing general psychiatric care with specialised, evidence-based psychotherapeutic interventions may be deemed necessary. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Co-occurrence of avoidant personality disorder and child sexual abuse predicts poor outcome in long-standing eating disorder.

Science.gov (United States)

Vrabel, Karianne R; Hoffart, Asle; Rø, Oyvind; Martinsen, Egil W; Rosenvinge, Jan H

2010-08-01

Few consistent predictive factors for eating disorder have been identified across studies. In the current 5-year prospective study, the objective was to examine whether (a) personality disorder and child sexual abuse predict the course of severity of eating disorder symptoms after inpatient treatment and (b) how the predictors interact. A total of 74 patients with long-standing eating disorder and mean age of 30 years were assessed at the beginning and end of inpatient therapy and at 1-, 2-, and 5-year follow-up. A mixed model was used to examine the predictors. Avoidant personality disorder and child sexual abuse interacted in predicting high levels of eating disorder over a long-term course. These results suggest that eating disorder, avoidant personality disorder, and sequelae after child sexual abuse are potential targets for treatment that need further investigation. Copyright 2010 APA, all rights reserved

Development of Antisocial Personality Disorder in Detained Youths: The Predictive Value of Mental Disorders

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Washburn, Jason J.; Romero, Erin Gregory; Welty, Leah J.; Abram, Karen M.; Teplin, Linda A.; McClelland, Gary M.; Paskar, Leah D.

2007-01-01

Antisocial personality disorder (APD) is a serious public and mental health concern. Understanding how well conduct disorder (CD) and other mental disorders predict the development of APD among youths involved in the juvenile justice system is critical for prevention. The authors used a stratified random sample of 1,112 detained youths to examine…

Dissociation predicts poor response to Dialectial Behavioral Therapy in female patients with Borderline Personality Disorder.

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Kleindienst, Nikolaus; Limberger, Matthias F; Ebner-Priemer, Ulrich W; Keibel-Mauchnik, Jana; Dyer, Anne; Berger, Mathias; Schmahl, Christian; Bohus, Martin

2011-08-01

A substantial proportion of Borderline Personality Disorder (BPD) patients respond by a marked decrease of psychopathology when treated with Dialectical Behavioral Therapy (DBT). To further enhance the rate of DBT-response, it is useful to identify characteristics related to unsatisfactory response. As DBT relies on emotional learning, we explored whether dissociation-which is known to interfere with learning- predicts poor response to DBT. Fifty-seven Borderline Personality Disorder (BPD) patients (DSM-IV) were prospectively observed during a three-month inpatient DBT program. Pre-post improvements in general psychopathology (SCL-90-R) were predicted from baseline scores of the Dissociative Experiences Scale (DES) by regression models accounting for baseline psychopathology. High DES-scores were related to poor pre-post improvement (β = -0.017 ± 0.006, p = 0.008). The data yielded no evidence that some facets of dissociation are more important in predicting DBT-response than others. The results suggest that dissociation in borderline-patients should be closely monitored and targeted during DBT. At this stage, research on treatment of dissociation (e.g., specific skills training) is warranted.

Expected packing density allows prediction of both amyloidogenic and disordered regions in protein chains

Energy Technology Data Exchange (ETDEWEB)

Galzitskaya, Oxana V; Garbuzynskiy, Sergiy O; Lobanov, Michail Yu [Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region (Russian Federation)

2007-07-18

The determination of factors that influence conformational changes in proteins is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for 9 of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of formation of amyloid fibrils is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value for predicting both disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding.

Premorbid multivariate prediction of adult psychosis-spectrum disorder

DEFF Research Database (Denmark)

Schiffman, Jason; Kline, Emily; Jameson, Nicole D.

2015-01-01

whose parents had no mental illness, and children with at least one parent with a non-psychotic psychiatric diagnosis). Premorbid neurological factors and an indication of social function, as measured when participants were 10-13years of age, were combined to predict psychosis-spectrum disorders......Premorbid prediction of psychosis-spectrum disorders has implications for both understanding etiology and clinical identification. The current study used a longitudinal high-risk for psychosis design that included children of parents with schizophrenia as well as two groups of controls (children...

A comparison of the capacity of DSM-IV and DSM-5 acute stress disorder definitions to predict posttraumatic stress disorder and related disorders.

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Bryant, Richard A; Creamer, Mark; O'Donnell, Meaghan; Silove, Derrick; McFarlane, Alexander C; Forbes, David

2015-04-01

This study addresses the extent to which DSM-IV and DSM-5 definitions of acute stress disorder (ASD) predict subsequent posttraumatic stress disorder (PTSD) and related psychiatric disorders following trauma. Patients with randomized admissions to 5 hospitals across Australia (N = 596) were assessed in hospital and reassessed for PTSD at 3 (n = 508), 12 (n = 426), 24 (n = 439), and 72 (n = 314) months using the Clinician-Administered PTSD Scale; DSM-IV definition of PTSD was used at each assessment, and DSM-5 definition was used at 72 months. The Mini-International Neuropsychiatric Interview (MINI) was used at each assessment to assess anxiety, mood, and substance use disorders. Forty-five patients (8%) met DSM-IV criteria, and 80 patients (14%) met DSM-5 criteria for ASD. PTSD was diagnosed in 93 patients (9%) at 3, 82 patients (10%) at 12, 100 patients (12%) at 24, and 26 patients (8%) at 72 months; 19 patients (6%) met DSM-5 criteria for PTSD at 72 months. Comparable proportions of those diagnosed with ASD developed PTSD using DSM-IV (3 months = 46%, 12 months = 39%, 24 months = 32%, and 72 months = 25%) and DSM-5 (43%, 42%, 33%, and 24%) ASD definitions. Sensitivity was improved for DSM-5 relative to DSM-IV for depression (0.18 vs 0.30), panic disorder (0.19 vs 0.41), agoraphobia (0.14 vs 0.40), social phobia (0.12 vs 0.44), specific phobia (0.24 vs 0.58), obsessive-compulsive disorder (0.17 vs 0.47), and generalized anxiety disorder (0.20 vs 0.47). More than half of participants with DSM-5-defined ASD had a subsequent disorder. The DSM-5 criteria for ASD results in better identification of people who will subsequently develop PTSD or another psychiatric disorder relative to the DSM-IV criteria. Although prediction is modest, it suggests that the new ASD diagnosis can serve a useful function in acute trauma settings for triaging those who can benefit from either early intervention or subsequent monitoring. © Copyright 2015 Physicians Postgraduate Press, Inc.

Neurobiological stress responses predict aggression in boys with oppositional defiant disorder/conduct disorder: a 1-year follow-up intervention study.

Science.gov (United States)

Schoorl, Jantiene; van Rijn, Sophie; de Wied, Minet; van Goozen, Stephanie H M; Swaab, Hanna

2017-07-01

To improve outcome for children with antisocial and aggressive behavior, it is important to know which individual characteristics contribute to reductions in problem behavior. The predictive value of a parent training (Parent Management Training Oregon; PMTO), parenting practices (monitoring, discipline, and punishment), and child neurobiological function (heart rate, cortisol) on the course of aggression was investigated. 64 boys with oppositional defiant disorder or conduct disorder (8-12 years) participated; parents of 22 boys took part in PMTO. All data were collected before the start of the PMTO, and aggression ratings were collected three times, before PMTO, and at 6 and 12 month follow-up. Parent training predicted a decline in aggression at 6 and 12 months. Child neurobiological variables, i.e., higher cortisol stress reactivity and better cortisol recovery, also predicted a decline in aggression at 6 and 12 months. Heart rate and parenting practices were not related to the course of aggression. These results indicate that child neurobiological factors can predict persistence or reduction of aggression in boys with ODD/CD, and have unique prognostic value on top of the parent training effects.

Predicting post-traumatic stress disorder treatment response in refugees: Multilevel analysis.

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Haagen, Joris F G; Ter Heide, F Jackie June; Mooren, Trudy M; Knipscheer, Jeroen W; Kleber, Rolf J

2017-03-01

Given the recent peak in refugee numbers and refugees' high odds of developing post-traumatic stress disorder (PTSD), finding ways to alleviate PTSD in refugees is of vital importance. However, there are major differences in PTSD treatment response between refugees, the determinants of which are largely unknown. This study aimed at improving PTSD treatment for adult refugees by identifying PTSD treatment response predictors. A prospective longitudinal multilevel modelling design was used to predict PTSD severity scores over time. We analysed data from a randomized controlled trial with pre-, post-, and follow-up measurements of the safety and efficacy of eye movement desensitization and reprocessing and stabilization in asylum seekers and refugees suffering from PTSD. Lack of refugee status, comorbid depression, demographic, trauma-related and treatment-related variables were analysed as potential predictors of PTSD treatment outcome. Treatment outcome data from 72 participants were used. The presence (B = 6.5, p = .03) and severity (B = 6.3, p disorder predicted poor treatment response and explained 39% of the variance between individuals. Refugee patients who suffer from PTSD and severe comorbid depression benefit less from treatment aimed at alleviating PTSD. Results highlight the need for treatment adaptations for PTSD and comorbid severe depression in traumatized refugees, including testing whether initial targeting of severe depressive symptoms increases PTSD treatment effectiveness. There are differences in post-traumatic stress disorder (PTSD) treatment response between traumatized refugees. Comorbid depressive disorder and depression severity predict poor PTSD response. Refugees with PTSD and severe depression may not benefit from PTSD treatment. Targeting comorbid severe depression before PTSD treatment is warranted. This study did not correct for multiple hypothesis testing. Comorbid depression may differentially impact alternative PTSD treatments

Personality disorder symptom severity predicts onset of mood episodes and conversion to bipolar I disorder in individuals with bipolar spectrum disorder.

Science.gov (United States)

Ng, Tommy H; Burke, Taylor A; Stange, Jonathan P; Walshaw, Patricia D; Weiss, Rachel B; Urosevic, Snezana; Abramson, Lyn Y; Alloy, Lauren B

2017-04-01

Although personality disorders (PDs) are highly comorbid with bipolar spectrum disorders (BSDs), little longitudinal research has been conducted to examine the prospective impact of PD symptoms on the course of BSDs. The aim of this study is to examine whether PD symptom severity predicts shorter time to onset of bipolar mood episodes and conversion to bipolar I disorder over time among individuals with less severe BSDs. Participants (n = 166) with bipolar II disorder, cyclothymia, or bipolar disorder not otherwise specified completed diagnostic interview assessments of PD symptoms and self-report measures of mood symptoms at baseline. They were followed prospectively with diagnostic interviews every 4 months for an average of 3.02 years. Cox proportional hazard regression analyses indicated that overall PD symptom severity significantly predicted shorter time to onset of hypomanic (hazard ratio [HR] = 1.42; p conversion to bipolar I disorder (HR = 2.51; p conversion to bipolar I disorder (HR = 2.77; p < .001), whereas cluster C severity (HR = 1.56; p < .001) predicted shorter time to onset of major depressive episodes. These results support predisposition models in suggesting that PD symptoms may act as a risk factor for a more severe course of BSDs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Early Risk and Resiliency Factors Predict Chronic Posttraumatic Stress Disorder in Caregivers of Patients Admitted to a Neuroscience ICU.

Science.gov (United States)

Choi, Karmel W; Shaffer, Kelly M; Zale, Emily L; Funes, Christopher J; Koenen, Karestan C; Tehan, Tara; Rosand, Jonathan; Vranceanu, Ana-Maria

2018-05-01

Informal caregivers-that is, close family and friends providing unpaid emotional or instrumental care-of patients admitted to ICUs are at risk for posttraumatic stress disorder. As a first step toward developing interventions to prevent posttraumatic stress disorder in ICU caregivers, we examined the predictive validity of psychosocial risk screening during admission for caregiver posttraumatic stress disorder at 3 and 6 months post hospitalization. An observational, prospective study. Ninety-nine caregivers were recruited as part of a longitudinal research program of patient-caregiver dyads in a neuroscience ICU. None. Caregiver posttraumatic stress disorder symptoms were assessed during admission (baseline), 3 months, and 6 months post hospitalization. We 1) characterized prevalence of clinically significant symptoms at each time point 2); calculated sensitivity and specificity of baseline posttraumatic stress disorder screening in predicting posttraumatic stress disorder at 3 and 6 months; and 3) used recursive partitioning to select potential baseline factors and examine the extent to which they helped predict clinically significant posttraumatic stress disorder symptoms at each time point. Rates of caregiver posttraumatic stress disorder remained relatively stable over time (16-22%). Screening for posttraumatic stress disorder at baseline predicted posttraumatic stress disorder at 3 and 6 months with moderate sensitivity (75-80%) and high specificity (92-95%). Screening for posttraumatic stress disorder at baseline was associated with caregiver anxiety, mindfulness (i.e., ability to be aware of one's thoughts and feelings in the moment), and bond with patient. Furthermore, baseline posttraumatic stress disorder screening was the single most relevant predictor of posttraumatic stress disorder at 3 and 6 months, such that other baseline factors did not significantly improve predictive ability. Screening neuroscience ICU caregivers for clinically significant

Emotional Dysregulation in Adults With Attention-Deficit/Hyperactivity Disorder-Validity, Predictability, Severity, and Comorbidity.

Science.gov (United States)

Corbisiero, Salvatore; Mörstedt, Beatrice; Bitto, Hannes; Stieglitz, Rolf-Dieter

2017-01-01

Attention-deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity, and impulsivity. However, this triad might not be able to explain the complete spectrum of ADHD symptoms, as emotional dysregulation (ED) frequently seems to accompany the disorder. The aim of this study was to further understand the role of ED in adult ADHD. The sample comprised 393 adults with ADHD without or with comorbidity, and 121 adults without ADHD or any other mental disorder. Additionally, the sample focused on ED. The contribution of core symptoms and the effect of comorbidity on ED were tested and the predictive value of ED for the ADHD diagnosis itself analyzed. Finally, all subjects were categorized into groups-No ADHD, ADHD, and ADHD + ED-to analyze the differences in the severity of ADHD symptomatology in the three groups. ED levels were found to be elevated in patients with ADHD. The core symptoms affected ED, and the ADHD diagnosis was predicted by ED. The addition of ED to a regression model with the core symptoms was shown to improve the predictability of the ADHD diagnosis. The presence of ED proved to be an indicator of the severity of adult ADHD independent of a present comorbidity. ED is a significant symptom in adult patients with ADHD and appears to be associated with ADHD itself. Whilst the presence of other mental disorders intensifies symptoms of ED, ED seems not to manifest solely as a consequence of comorbidity. © 2016 Wiley Periodicals, Inc.

Nonopioid substance use disorders and opioid dose predict therapeutic opioid addiction.

Science.gov (United States)

Huffman, Kelly L; Shella, Elizabeth R; Sweis, Giries; Griffith, Sandra D; Scheman, Judith; Covington, Edward C

2015-02-01

Limited research examines the risk of therapeutic opioid addiction (TOA) in patients with chronic noncancer pain. This study examined TOA among 199 patients undergoing long-term opioid therapy at the time of admission to a pain rehabilitation program. It was hypothesized that nonopioid substance use disorders and opioid dosage would predict TOA. Daily mean opioid dose was 132.85 mg ± 175.39. Patients with nonopioid substance use disorders had 28 times the odds (odds ratio [OR] = 28.58; 95% confidence interval [CI] = 10.86, 75.27) of having TOA. Each 50-mg increase in opioid dose nearly doubled the odds of TOA (OR = 1.73; 95% CI = 1.29, 2.32). A 100-mg increase was associated with a 3-fold increase in odds (OR = 3.00; 95% CI = 1.67, 5.41). Receiver operating characteristic analysis revealed that opioid dose was a moderately accurate predictor (area under the curve = .75; 95% CI = .68, .82) of TOA. The sensitivity (.70) and specificity (.68) of opioid dose in predicting TOA was maximized at 76.10 mg; in addition, 46.00 mg yielded 80% sensitivity in identifying TOA. These results underscore the importance of obtaining a substance use history prior to prescribing and suggest a low screening threshold for TOA in patients who use opioids in the absence of improvement in pain or functional impairment. This article examines TOA in patients with chronic noncancer pain undergoing long-term opioid therapy. Results suggest that patients should be screened for nonopioid substance use disorders prior to prescribing. In the absence of improvement in pain or function, there is a low threshold (∼50 mg daily opioid dose) for addiction screening. Copyright © 2015 American Pain Society. Published by Elsevier Inc. All rights reserved.

Predictive value of work-related self-efficacy change on RTW for employees with common mental disorders

NARCIS (Netherlands)

Lagerveld, S.E.; Brenninkmeijer, V.; Blonk, R.W.B.; Twisk, J.; Schaufeli, W.

2017-01-01

To improve interventions that aim to promote return to work (RTW) of workers with common mental disorders (CMD), insight into modifiable predictors of RTW is needed. This study tested the predictive value of self-efficacy change for RTW in addition to preintervention levels of self-efficacy. RTW

Predictive value of work-related self-efficacy change on RTW for employees with common mental disorders

NARCIS (Netherlands)

Lagerveld, S.E.; Brenninkmeijer, V.; Blonk, R.W.B.; Twisk, J.W.R.; Schaufeli, W.B.

To improve interventions that aim to promote return to work (RTW) of workers with common mental disorders (CMD), insight into modifiable predictors of RTW is needed. This study tested the predictive value of self-efficacy change for RTW in addition to preintervention levels of self-efficacy. RTW

Occupational imbalance and the role of perceived stress in predicting stress-related disorders.

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Håkansson, Carita; Ahlborg, Gunnar

2017-03-02

Stress-related disorders are the main reason for sick leave in many European countries. The aim of the present study was to explore whether perceived occupational imbalance predicts stress-related disorders, potential gender differences, and to explore the mediating role of perceived stress. Longitudinal data on 2223 employees in a public organization in Sweden were collected by surveys, and analyzed by logistic regression. Occupational imbalance predicted stress-related disorders among both women and men. However, what aspects of occupational imbalance which predicted stress-related disorders differ by gender. Perceived stress was not a mediator in these associations. How women and men perceived their occupational balance affected the risk of stress-related disorders. The results may be used to develop effective strategies to decrease stress-related disorders.

Predictive validity of childhood oppositional defiant disorder and conduct disorder: implications for the DSM-V.

Science.gov (United States)

Burke, Jeffrey D; Waldman, Irwin; Lahey, Benjamin B

2010-11-01

Data are presented from 3 studies of children and adolescents to evaluate the predictive validity of childhood oppositional defiant disorder (ODD) and conduct disorder (CD) as defined in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV; American Psychiatric Association, 1994) and the International Classification of Diseases, Version 10 (ICD-10; World Health Organization, 1992). The present analyses strongly support the predictive validity of these diagnoses by showing that they predict both future psychopathology and enduring functional impairment. Furthermore, the present findings generally support the hierarchical developmental hypothesis in DSM-IV that some children with ODD progress to childhood-onset CD, and some youth with CD progress to antisocial personality disorder (APD). Nonetheless, they reveal that CD does not always co-occur with ODD, particularly during adolescence. Importantly, the present findings suggest that ICD-10 diagnostic criteria for ODD, which treat CD symptoms as ODD symptoms when diagnostic criteria for CD are not met, identify more functionally impaired children than the more restrictive DSM-IV definition of ODD. Filling this "hole" in the DSM-IV criteria for ODD should be a priority for the DSM-V. In addition, the present findings suggest that although the psychopathic trait of interpersonal callousness in childhood independently predicts future APD, these findings do not confirm the hypothesis that callousness distinguishes a subset of children with CD with an elevated risk for APD. PsycINFO Database Record (c) 2010 APA, all rights reserved

Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls

Science.gov (United States)

Arbabshirani, Mohammad R.; Plis, Sergey; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there are extensive evidences showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need

Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders

Science.gov (United States)

2015-09-01

early behavioral interventions are the most effective treatment for Autism Spectrum Disorder (ASD), but almost half of the children do not make...behavioral intervention . 2. KEYWORDS Autism Spectrum Disorder , implicit learning, associative learning, individual differences, functional Magnetic...2 AWARD NUMBER: W81XWH-14-1-0261 TITLE: Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders PRINCIPAL

DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

Directory of Open Access Journals (Sweden)

Zhiheng Wang

Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

A clinical prediction rule for detecting major depressive disorder in primary care : the PREDICT-NL study

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Zuithoff, Nicolaas P A; Vergouwe, Yvonne; King, Michael; Nazareth, Irwin; Hak, Eelko; Moons, Karel G M; Geerlings, Mirjam I

BACKGROUND: Major depressive disorder often remains unrecognized in primary care. OBJECTIVE: Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. METHODS: A total of 1046 subjects, aged 18-65 years, were included from

An Examination of Participants Who Develop an Eating Disorder Despite Completing an Eating Disorder Prevention Program: Implications for Improving the Yield of Prevention Efforts

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Stice, Eric; Rohde, Paul

2014-01-01

Numerous trials provide support for the Body Project, an eating disorder prevention program wherein young women with body image concerns critique the thin ideal. Despite medium to large effects, some participants subsequently develop an eating disorder, suggesting that intervention or recruitment procedures could be improved. This study investigated baseline and acute intervention predictors of DSM-5 eating disorder development during a 3-year follow-up among Body Project participants. Combined data from two trials compare participants who experienced eating disorder onset during follow-up (n=20) to those who did not (n=216). Participants who did versus did not develop an eating disorder started the intervention with higher eating disorder symptoms (η2=0.08), negative affect (η2=0.06), thin-ideal internalization (η2=0.02), and body dissatisfaction (η2=0.02); the same baseline predictors of eating disorder onset emerged in controls. Attenuated pre–post reductions in eating disorder symptoms (η2=0.01) predicted eating disorder onset but not after controlling for baseline levels. Given that Body Project and control participants who later developed an eating disorder started with initial elevations in risk factors and eating disorder symptoms, it might be useful to develop a more intensive variant of this program for those exhibiting greater risk at baseline and to deliver the prevention program earlier to prevent initial escalation of risk. The fact that nonresponders also showed greater negative affect and eating disorder symptoms suggests that it might be useful to add activities to improve affect and increase dissonance about disordered eating. PMID:25342026

Subclinical bulimia predicts conduct disorder in middle adolescent girls.

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Viinamäki, Anni; Marttunen, Mauri; Fröjd, Sari; Ruuska, Jaana; Kaltiala-Heino, Riittakerttu

2013-01-01

This study investigates the comorbidity and longitudinal associations between self-reported conduct disorder and subclinical bulimia in a community-based sample of Finnish adolescents in a 2-year prospective follow-up study. There are 2070 adolescents who participated in the survey as ninth graders (mean age 15.5) and followed-up 2 years later. The Youth Self-Report Externalizing scale was used to measure conduct disorder and DSM-IV-based questionnaire to measure bulimia. Co-occurrence of female conduct disorder and subclinical bulimia was found at ages 15 and 17. Subclinical bulimia among girls at age 15 was a risk factor for conduct disorder at age 17, but conduct disorder at age 15 was not predictive of subclinical bulimia at age 17. The pathway from bulimia to conduct disorder may be suggestive of an association with future borderline personality disorder among girls. Copyright © 2012 John Wiley & Sons, Ltd and Eating Disorders Association.

Predicting Substance Abuse from Attention Deficit/Hyperactivity Disorder Symptoms in Adult

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Jaber Alizadeh G

2013-04-01

Full Text Available Objective: This study is aimed to predict substance abuse from child and adult attention deficit/hyperactivity disorder symptoms. Method: To this purpose 361 students were selected via stratified random sampling from different faculties of Tabriz University and completed Canners Adult ADHD Rating scale-self report Form & Subscale Questionnaire, Wender Utah Rating Scale, Addiction Acknowledgment Scale & Mac Andrew Alcoholism-Revised Scale. Findings: To analyze the data Pearson correlation and multiple regressions (step by step were used. Results indicated that there is significant relation between addiction acknowledgment and alcoholism via child and adult attention deficit hyperactivity disorder symptoms. Also, results indicated that child and adult attention deficit hyperactivity disorder symptoms predicted addiction acknowledgment and alcoholism. Conclusion: According to this results these can be explained that behavioral disorders, especially ADHD have effect in tendency to drug and therefore primary treatments of behavioral disorders can prevent drug abuse.

DSM-5 antisocial personality disorder: predictive validity in a prison sample.

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Edens, John F; Kelley, Shannon E; Lilienfeld, Scott O; Skeem, Jennifer L; Douglas, Kevin S

2015-04-01

Symptoms of antisocial personality disorder (ASPD), particularly remorselessness, are frequently introduced in legal settings as a risk factor for future violence in prison, despite a paucity of research on the predictive validity of this disorder. We examined whether an ASPD diagnosis or symptom-criteria counts could prospectively predict any form of institutional misconduct, as well as aggressive and violent infractions among newly admitted prisoners. Adult male (n = 298) and female (n = 55) offenders were recruited from 4 prison systems across the United States. At the time of study enrollment, diagnostic information was collected using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997) supplemented by a detailed review of official records. Disciplinary records were obtained from inmates' respective prisons covering a 1-year period following study enrollment and misconduct was categorized hierarchically as any (general), aggressive (verbal/physical), or violent (physical). Dichotomous ASPD diagnoses and adult symptom-criteria counts did not significantly predict institutional misconduct across our 3 outcome variables, with effect sizes being close to 0 in magnitude. The symptom of remorselessness in particular showed no relation to future misconduct in prison. Childhood symptom counts of conduct disorder demonstrated modest predictive utility. Our results offer essentially no support for the claim that ASPD diagnoses can predict institutional misconduct in prison, regardless of the number of adult symptoms present. In forensic contexts, testimony that an ASPD diagnosis identifies defendants who will pose a serious threat while incarcerated in prison presently lacks any substantial scientific foundation. (c) 2015 APA, all rights reserved).

A Cerebellar Framework for Predictive Coding and Homeostatic Regulation in Depressive Disorder.

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Schutter, Dennis J L G

2016-02-01

Depressive disorder is associated with abnormalities in the processing of reward and punishment signals and disturbances in homeostatic regulation. These abnormalities are proposed to impair error minimization routines for reducing uncertainty. Several lines of research point towards a role of the cerebellum in reward- and punishment-related predictive coding and homeostatic regulatory function in depressive disorder. Available functional and anatomical evidence suggests that in addition to the cortico-limbic networks, the cerebellum is part of the dysfunctional brain circuit in depressive disorder as well. It is proposed that impaired cerebellar function contributes to abnormalities in predictive coding and homeostatic dysregulation in depressive disorder. Further research on the role of the cerebellum in depressive disorder may further extend our knowledge on the functional and neural mechanisms of depressive disorder and development of novel antidepressant treatments strategies targeting the cerebellum.

The Ising model for prediction of disordered residues from protein sequence alone

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Lobanov, Michail Yu; Galzitskaya, Oxana V

2011-01-01

Intrinsically disordered regions serve as molecular recognition elements, which play an important role in the control of many cellular processes and signaling pathways. It is useful to be able to predict positions of disordered residues and disordered regions in protein chains using protein sequence alone. A new method (IsUnstruct) based on the Ising model for prediction of disordered residues from protein sequence alone has been developed. According to this model, each residue can be in one of two states: ordered or disordered. The model is an approximation of the Ising model in which the interaction term between neighbors has been replaced by a penalty for changing between states (the energy of border). The IsUnstruct has been compared with other available methods and found to perform well. The method correctly finds 77% of disordered residues as well as 87% of ordered residues in the CASP8 database, and 72% of disordered residues as well as 85% of ordered residues in the DisProt database

Genome-scale prediction of proteins with long intrinsically disordered regions.

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Peng, Zhenling; Mizianty, Marcin J; Kurgan, Lukasz

2014-01-01

Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/. Copyright © 2013 Wiley Periodicals, Inc.

Personality, emotion-related variables, and media pressure predict eating disorders via disordered eating in Lebanese university students.

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Sanchez-Ruiz, Maria Jose; El-Jor, Claire; Abi Kharma, Joelle; Bassil, Maya; Zeeni, Nadine

2017-04-18

Disordered eating behaviors are on the rise among youth. The present study investigates psychosocial and weight-related variables as predictors of eating disorders (ED) through disordered eating (DE) dimensions (namely restrained, external, and emotional eating) in Lebanese university students. The sample consisted of 244 undergraduates (143 female) aged from 18 to 31 years (M = 20.06; SD = 1.67). Using path analysis, two statistical models were built separately with restrained and emotional eating as dependent variables, and all possible direct and indirect pathways were tested for mediating effects. The variables tested for were media influence, perfectionism, trait emotional intelligence, and the Big Five dimensions. In the first model, media pressure, self-control, and extraversion predicted eating disorders via emotional eating. In the second model, media pressure and perfectionism predicted eating disorders via restrained eating. Findings from this study provide an understanding of the dynamics between DE, ED, and key personality, emotion-related, and social factors in youth. Lastly, implications and recommendations for future studies are advanced.

Audiovisual biofeedback improves motion prediction accuracy.

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Pollock, Sean; Lee, Danny; Keall, Paul; Kim, Taeho

2013-04-01

The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

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Jennifer D. Atkins

2015-08-01

Full Text Available The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

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Maier, Robert; Moser, Gerhard; Chen, Guo-Bo

2015-01-01

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk...... number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low...

Does diagnosis affect the predictive accuracy of risk assessment tools for juvenile offenders: Conduct Disorder and Attention Deficit Hyperactivity Disorder.

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Khanna, Dinesh; Shaw, Jenny; Dolan, Mairead; Lennox, Charlotte

2014-10-01

Studies have suggested an increased risk of criminality in juveniles if they suffer from co-morbid Attention Deficit Hyperactivity Disorder (ADHD) along with Conduct Disorder. The Structured Assessment of Violence Risk in Youth (SAVRY), the Psychopathy Checklist Youth Version (PCL:YV), and Youth Level of Service/Case Management Inventory (YLS/CMI) have been shown to be good predictors of violent and non-violent re-offending. The aim was to compare the accuracy of these tools to predict violent and non-violent re-offending in young people with co-morbid ADHD and Conduct Disorder and Conduct Disorder only. The sample included 109 White-British adolescent males in secure settings. Results revealed no significant differences between the groups for re-offending. SAVRY factors had better predictive values than PCL:YV or YLS/CMI. Tools generally had better predictive values for the Conduct Disorder only group than the co-morbid group. Possible reasons for these findings have been discussed along with limitations of the study. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Source-Specific Oppositional Defiant Disorder among Inner-City Children: Prospective Prediction and Moderation

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Drabick, Deborah A. G.; Bubier, Jennifer; Chen, Diane; Price, Julia; Lanza, H. Isabella

2011-01-01

We examined prospective prediction from parent- and teacher-reported oppositional defiant disorder (ODD) symptoms to parent-reported ODD, conduct disorder (CD), major depressive disorder (MDD), and generalized anxiety disorder symptoms and whether child executive functioning abilities moderated these relations among an urban, low-income sample of…

Associations in the longitudinal course of body dysmorphic disorder with major depression, obsessive-compulsive disorder, and social phobia.

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Phillips, Katharine A; Stout, Robert L

2006-06-01

Body dysmorphic disorder (BDD) is an impairing and relatively common disorder that has high comorbidity with certain Axis I disorders. However, the longitudinal associations between BDD and comorbid disorders have not previously been examined. Such information may shed light on the nature of BDD's relationship to putative "near-neighbor" disorders, such as major depression, obsessive-compulsive disorder (OCD), and social phobia. This study examined time-varying associations between BDD and these comorbid disorders in 161 participants over 1-3 years of follow-up in the first prospective longitudinal study of the course of BDD. We found that BDD had significant longitudinal associations with major depression--that is, change in the status of BDD and major depression was closely linked in time, with improvement in major depression predicting BDD remission, and, conversely, improvement in BDD predicting depression remission. We also found that improvement in OCD predicted BDD remission, but that BDD improvement did not predict OCD remission. No significant longitudinal associations were found for BDD and social phobia (although the results for analyses of OCD and social phobia were less numerically stable). These findings suggest (but do not prove) that BDD may be etiologically linked to major depression and OCD, i.e., that BDD may be a member of both the putative OCD spectrum and the affective spectrum. However, BDD does not appear to simply be a symptom of these comorbid disorders, as BDD symptoms persisted in a sizable proportion of subjects who remitted from these comorbid disorders. Additional studies are needed to elucidate the nature of BDD's relationship to commonly co-occurring disorders, as this issue has important theoretical and clinical implications.

Predicting Future Antisocial Personality Disorder in Males from a Clinical Assessment in Childhood

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Lahey, Benjamin B.; Loeber, Rolf; Burke, Jeffrey D.; Applegate, Brooks

2005-01-01

It is essential to identify childhood predictors of adult antisocial personality disorder (APD) to target early prevention. It has variously been hypothesized that APD is predicted by childhood conduct disorder (CD), attention-deficit/hyperactivity disorder (ADHD), or both disorders. To test these competing hypotheses, the authors used data from a…

Predictive Utility of Personality Disorder in Depression: Comparison of Outcomes and Taxonomic Approach.

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Newton-Howes, Giles; Mulder, Roger; Ellis, Pete M; Boden, Joseph M; Joyce, Peter

2017-09-19

There is debate around the best model for diagnosing personality disorder, both in terms of its relationship to the empirical data and clinical utility. Four randomized controlled trials examining various treatments for depression were analyzed at an individual patient level. Three different approaches to the diagnosis of personality disorder were analyzed in these patients. A total of 578 depressed patients were included in the analysis. Personality disorder, however measured, was of little predictive utility in the short term but added significantly to predictive modelling of medium-term outcomes, accounting for more than twice as much of the variance in social functioning outcome as depression psychopathology. Personality disorder assessment is of predictive utility with longer timeframes and when considering social outcomes as opposed to symptom counts. This utility is sufficiently great that there appears to be value in assessing personality; however, no particular approach outperforms any other.

Changes in predicted protein disorder tendency may contribute to disease risk

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Hu Yang

2011-12-01

Full Text Available Abstract Background Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort. Results Using the exonic single nucleotide variations (SNVs identified in the 1,000 Genomes Project and distributed by the Genetic Analysis Workshop 17, we systematically analysed the genetic and predicted disorder potential features of the non-synonymous variations. The result of experiments suggests that a significant change in the tendency of a protein region to be structured or disordered caused by SNVs may lead to malfunction of such a protein and contribute to disease risk. Conclusions After validation with functional SNVs on the traits distributed by GAW17, we conclude that it is valuable to consider structure/disorder tendencies while prioritizing and predicting mechanistic effects arising from novel genetic variations.

Interactions between risk factors in the prediction of onset of eating disorders: Exploratory hypothesis generating analyses.

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Stice, Eric; Desjardins, Christopher D

2018-06-01

Because no study has tested for interactions between risk factors in the prediction of future onset of each eating disorder, this exploratory study addressed this lacuna to generate hypotheses to be tested in future confirmatory studies. Data from three prevention trials that targeted young women at high risk for eating disorders due to body dissatisfaction (N = 1271; M age 18.5, SD 4.2) and collected diagnostic interview data over 3-year follow-up were combined to permit sufficient power to predict onset of anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and purging disorder (PD) using classification tree analyses, an analytic technique uniquely suited to detecting interactions. Low BMI was the most potent predictor of AN onset, and body dissatisfaction amplified this relation. Overeating was the most potent predictor of BN onset, and positive expectancies for thinness and body dissatisfaction amplified this relation. Body dissatisfaction was the most potent predictor of BED onset, and overeating, low dieting, and thin-ideal internalization amplified this relation. Dieting was the most potent predictor of PD onset, and negative affect and positive expectancies for thinness amplified this relation. Results provided evidence of amplifying interactions between risk factors suggestive of cumulative risk processes that were distinct for each disorder; future confirmatory studies should test the interactive hypotheses generated by these analyses. If hypotheses are confirmed, results may allow interventionists to target ultra high-risk subpopulations with more intensive prevention programs that are uniquely tailored for each eating disorder, potentially improving the yield of prevention efforts. Copyright © 2018 Elsevier Ltd. All rights reserved.

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

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Daniël B van Schalkwijk

Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

Predicting Developmental Disorder in Infants Using an Artificial Neural Network

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Farin Soleimani

2013-06-01

Full Text Available Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother–child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants. The validated Infant Neurological International Battery test was employed to assess the infant’s development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.

Adolescent Eating Disorders Predict Psychiatric, High-Risk Behaviors and Weight Outcomes in Young Adulthood

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Micali, Nadia; Solmi, Francesca; Horton, Nicholas J.; Crosby, Ross D.; Eddy, Kamryn T.; Calzo, Jerel P.; Sonneville, Kendrin R.; Swanson, Sonja A.; Field, Alison E.

2015-01-01

Objective To investigate whether anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and other specified feeding and eating disorders (OSFED), including purging disorder (PD), subthreshold BN, and BED at ages 14 and 16, are prospectively associated with later depression, anxiety disorders, alcohol and substance use, and self-harm. Method Eating disorders were ascertained at 14 and 16 years of age in 6,140 youth at age 14 (58% of those eligible) and 5,069 at age 16 (52% of those eligible) as part of the prospective Avon Longitudinal Study of Parents and Children (ALSPAC). Outcomes (depression, anxiety disorders, binge drinking, drug use, deliberate self-harm, weight status) were measured using interviews and questionnaires about 2 years following predictors. Generalized estimating equation models adjusting for gender, socio-demographic variables, and prior outcome were used to examine prospective associations between eating disorders and each outcome. Results All eating disorders were predictive of later anxiety disorders. AN, BN, BED, PD, and OSFED were prospectively associated with depression (respectively AN: odds ratio [OR]=1.39 [95% CIs: 1.00-1.94]; BN: OR=3.39[1.25-9.20]; BED: OR=2.00 [1.06-3.75]; PD: OR=2.56 [1.38-4.74]). All eating disorders but AN predicted drug use and deliberate self-harm (BN: OR=5.72[2.22-14.72], PD: OR=4.88[2.78-8.57], subthreshold BN: OR=3.97[1.44-10.98], subthreshold BED: OR=2.32[1.43-3.75]). Whilst BED and BN predicted obesity (respectively OR=3.58 [1.06-12.14] and OR=6.42 [1.69-24.30]), AN was prospectively associated with underweight. Conclusions Adolescent eating disorders, including subthreshold presentations, predict negative outcomes, including mental health disorders, substance use, deliberate self-harm, and weight outcomes. This study highlights the high public health and clinical burden of eating disorders among adolescents. PMID:26210334

Threat Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

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Legerstee, Jeroen S.; Tulen, Joke H. M.; Kallen, Victor L.; Dieleman, Gwen C.; Treffers, Philip D. A.; Verhulst, Frank C.; Utens, Elisabeth M. W. J.

2009-01-01

Threat-related selective attention was found to predict the success of the treatment of childhood anxiety disorders through administering a pictorial dot-probe task to 131 children with anxiety disorders prior to cognitive behavioral therapy. The diagnostic status of the subjects was evaluated with a semistructured clinical interview at both pre-…

Sleep Quality Improvement During Cognitive Behavioral Therapy for Anxiety Disorders.

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Ramsawh, Holly J; Bomyea, Jessica; Stein, Murray B; Cissell, Shadha H; Lang, Ariel J

2016-01-01

Despite the ubiquity of sleep complaints among individuals with anxiety disorders, few prior studies have examined whether sleep quality improves during anxiety treatment. The current study examined pre- to posttreatment sleep quality improvement during cognitive behavioral therapy (CBT) for panic disorder (PD; n = 26) or generalized anxiety disorder (GAD; n = 24). Among sleep quality indices, only global sleep quality and sleep latency improved significantly (but modestly) during CBT. Sleep quality improvement was greater for treatment responders, but did not vary by diagnosis. Additionally, poor baseline sleep quality was independently associated with worse anxiety treatment outcome, as measured by higher intolerance of uncertainty. Additional intervention targeting sleep prior to or during CBT for anxiety may be beneficial for poor sleepers.

Predictors of outcome at 1 year in adolescents with DSM-5 restrictive eating disorders: report of the national eating disorders quality improvement collaborative.

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Forman, Sara F; McKenzie, Nicole; Hehn, Rebecca; Monge, Maria C; Kapphahn, Cynthia J; Mammel, Kathleen A; Callahan, S Todd; Sigel, Eric J; Bravender, Terrill; Romano, Mary; Rome, Ellen S; Robinson, Kelly A; Fisher, Martin; Malizio, Joan B; Rosen, David S; Hergenroeder, Albert C; Buckelew, Sara M; Jay, M Susan; Lindenbaum, Jeffrey; Rickert, Vaughn I; Garber, Andrea; Golden, Neville H; Woods, Elizabeth R

2014-12-01

The National Eating Disorders Quality Improvement Collaborative evaluated data of patients with restrictive eating disorders to analyze demographics of diagnostic categories and predictors of weight restoration at 1 year. Fourteen Adolescent Medicine eating disorder programs participated in a retrospective review of 700 adolescents aged 9-21 years with three visits, with DSM-5 categories of restrictive eating disorders including anorexia nervosa (AN), atypical AN, and avoidant/restrictive food intake disorder (ARFID). Data including demographics, weight and height at intake and follow-up, treatment before intake, and treatment during the year of follow-up were analyzed. At intake, 53.6% met criteria for AN, 33.9% for atypical AN, and 12.4% for ARFID. Adolescents with ARFID were more likely to be male, younger, and had a longer duration of illness before presentation. All sites had a positive change in mean percentage median body mass index (%MBMI) for their population at 1-year follow-up. Controlling for age, gender, duration of illness, diagnosis, and prior higher level of care, only %MBMI at intake was a significant predictor of weight recovery. In the model, there was a 12.7% change in %MBMI (interquartile range, 6.5-19.3). Type of treatment was not predictive, and there were no significant differences between programs in terms of weight restoration. The National Eating Disorders Quality Improvement Collaborative provides a description of the patient population presenting to a national cross-section of 14 Adolescent Medicine eating disorder programs and categorized by DSM-5. Treatment modalities need to be further evaluated to assess for more global aspects of recovery. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

A diagnosis of bipolar spectrum disorder predicts diagnostic conversion from unipolar depression to bipolar disorder: a 5-year retrospective study.

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Woo, Young Sup; Shim, In Hee; Wang, Hee-Ryung; Song, Hoo Rim; Jun, Tae-Youn; Bahk, Won-Myong

2015-03-15

The major aims of this study were to identify factors that may predict the diagnostic conversion from major depressive disorder (MDD) to bipolar disorder (BP) and to evaluate the predictive performance of the bipolar spectrum disorder (BPSD) diagnostic criteria. The medical records of 250 patients with a diagnosis of MDD for at least 5 years were retrospectively reviewed for this study. The diagnostic conversion from MDD to BP was observed in 18.4% of 250 MDD patients, and the diagnostic criteria for BPSD predicted this conversion with high sensitivity (0.870) and specificity (0.917). A family history of BP, antidepressant-induced mania/hypomania, brief major depressive episodes, early age of onset, antidepressant wear-off, and antidepressant resistance were also independent predictors of this conversion. This study was conducted using a retrospective design and did not include structured diagnostic interviews. The diagnostic criteria for BPSD were highly predictive of the conversion from MDD to BP, and conversion was associated with several clinical features of BPSD. Thus, the BPSD diagnostic criteria may be useful for the prediction of bipolar diathesis in MDD patients. Copyright © 2014 Elsevier B.V. All rights reserved.

Predicting mental disorders from hypothalamic-pituitary-adrenal axis functioning: a 3-year follow-up in the TRAILS study.

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Nederhof, E; van Oort, F V A; Bouma, E M C; Laceulle, O M; Oldehinkel, A J; Ormel, J

2015-08-01

Hypothalamic-pituitary-adrenal axis functioning, with cortisol as its major output hormone, has been presumed to play a key role in the development of psychopathology. Predicting affective disorders from diurnal cortisol levels has been inconclusive, whereas the predictive value of stress-induced cortisol concentrations has not been studied before. The aim of this study was to predict mental disorders over a 3-year follow-up from awakening and stress-induced cortisol concentrations. Data were used from 561 TRAILS (TRacking Adolescents' Individual Lives Survey) participants, a prospective cohort study of Dutch adolescents. Saliva samples were collected at awakening and half an hour later and during a social stress test at age 16. Mental disorders were assessed 3 years later with the Composite International Diagnostic Interview (CIDI). A lower cortisol awakening response (CAR) marginally significantly predicted new disorders [odds ratio (OR) 0.77, p = 0.06]. A flat recovery slope predicted disorders with a first onset after the experimental session (OR 1.27, p = 0.04). Recovery revealed smaller, non-significant ORs when predicting new onset affective or anxiety disorders, major depressive disorder, or dependence disorders in three separate models, corrected for all other new onsets. Our results suggest that delayed recovery and possibly reduced CAR are indicators of a more general risk status and may be part of a common pathway to psychopathology. Delayed recovery suggests that individuals at risk for mental disorders perceived the social stress test as less controllable and less predictable.

Adolescent Eating Disorders Predict Psychiatric, High-Risk Behaviors and Weight Outcomes in Young Adulthood.

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Micali, Nadia; Solmi, Francesca; Horton, Nicholas J; Crosby, Ross D; Eddy, Kamryn T; Calzo, Jerel P; Sonneville, Kendrin R; Swanson, Sonja A; Field, Alison E

2015-08-01

To investigate whether anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and other specified feeding and eating disorders (OSFED), including purging disorder (PD), subthreshold BN, and BED at ages 14 and 16 years, are prospectively associated with later depression, anxiety disorders, alcohol and substance use, and self-harm. Eating disorders were ascertained at ages 14 and 16 years in 6,140 youth at age 14 (58% of those eligible) and 5,069 at age 16 (52% of those eligible) as part of the prospective Avon Longitudinal Study of Parents and Children (ALSPAC). Outcomes (depression, anxiety disorders, binge drinking, drug use, deliberate self-harm, weight status) were measured using interviews and questionnaires about 2 years after predictors. Generalized estimating equation models adjusting for gender, socio-demographic variables, and prior outcome were used to examine prospective associations between eating disorders and each outcome. All eating disorders were predictive of later anxiety disorders. AN, BN, BED, PD, and OSFED were prospectively associated with depression (respectively AN: odds ratio [OR] = 1.39, 95% CI = 1.00-1.94; BN: OR = 3.39, 95% CI = 1.25-9.20; BED: OR = 2.00, 95% CI = 1.06-3.75; and PD: OR = 2.56, 95% CI = 1.38-4.74). All eating disorders but AN predicted drug use and deliberate self-harm (BN: OR = 5.72, 95% CI = 2.22-14.72; PD: OR = 4.88, 95% CI = 2.78-8.57; subthreshold BN: OR = 3.97, 95% CI = 1.44-10.98; and subthreshold BED: OR = 2.32, 95% CI = 1.43-3.75). Although BED and BN predicted obesity (respectively OR = 3.58, 95% CI = 1.06-12.14 and OR = 6.42, 95% CI = 1.69-24.30), AN was prospectively associated with underweight. Adolescent eating disorders, including subthreshold presentations, predict negative outcomes, including mental health disorders, substance use, deliberate self-harm, and weight outcomes. This study highlights the high public health and clinical burden of eating disorders

Intrinsic disorder in Viral Proteins Genome-Linked: experimental and predictive analyses

Directory of Open Access Journals (Sweden)

Van Dorsselaer Alain

2009-02-01

Full Text Available Abstract Background VPgs are viral proteins linked to the 5' end of some viral genomes. Interactions between several VPgs and eukaryotic translation initiation factors eIF4Es are critical for plant infection. However, VPgs are not restricted to phytoviruses, being also involved in genome replication and protein translation of several animal viruses. To date, structural data are still limited to small picornaviral VPgs. Recently three phytoviral VPgs were shown to be natively unfolded proteins. Results In this paper, we report the bacterial expression, purification and biochemical characterization of two phytoviral VPgs, namely the VPgs of Rice yellow mottle virus (RYMV, genus Sobemovirus and Lettuce mosaic virus (LMV, genus Potyvirus. Using far-UV circular dichroism and size exclusion chromatography, we show that RYMV and LMV VPgs are predominantly or partly unstructured in solution, respectively. Using several disorder predictors, we show that both proteins are predicted to possess disordered regions. We next extend theses results to 14 VPgs representative of the viral diversity. Disordered regions were predicted in all VPg sequences whatever the genus and the family. Conclusion Based on these results, we propose that intrinsic disorder is a common feature of VPgs. The functional role of intrinsic disorder is discussed in light of the biological roles of VPgs.

Impairment of executive function and attention predicts onset of affective disorder in healthy high-risk twins

DEFF Research Database (Denmark)

Vinberg, Maj; Miskowiak, Kamilla W; Kessing, Lars Vedel

2013-01-01

To investigate whether measures of cognitive function can predict onset of affective disorder in individuals at heritable risk.......To investigate whether measures of cognitive function can predict onset of affective disorder in individuals at heritable risk....

Improving Treatment Response for Paediatric Anxiety Disorders

DEFF Research Database (Denmark)

Ege, Sarah; Reinholdt-Dunne, Marie Louise

2016-01-01

Cognitive behavioural therapy (CBT) is considered the treatment of choice for paediatric anxiety disorders, yet there remains substantial room for improvement in treatment outcomes. This paper examines whether theory and research into the role of information-processing in the underlying psychopat......Cognitive behavioural therapy (CBT) is considered the treatment of choice for paediatric anxiety disorders, yet there remains substantial room for improvement in treatment outcomes. This paper examines whether theory and research into the role of information-processing in the underlying...... interpretational biases, evidence regarding the effects of CBT on attentional biases is mixed. Novel treatment methods including attention bias modification training, attention feedback awareness and control training, and mindfulness-based therapy may hold potential in targeting attentional biases, and thereby...

Threat-Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

OpenAIRE

Legerstee, Jeroen; Tulen, Joke; Kallen, Victor; Dieleman, Gwen; Treffers, Philip; Verhulst, Frank; Utens, Elisabeth

2009-01-01

textabstractAbstract OBJECTIVE: The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. METHOD: Participants consisted of 131 children with anxiety disorders (aged 8-16 years), who received standardized cognitive-behavioral therapy. At pretreatment, a pictorial dot-pr...

e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD).

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Bourla, Alexis; Mouchabac, Stephane; El Hage, Wissam; Ferreri, Florian

2018-01-01

Background : New technologies may profoundly change our way of understanding psychiatric disorders including posttraumatic stress disorder (PTSD). Imaging and biomarkers, along with technological and medical informatics developments, might provide an answer regarding at-risk patient's identification. Recent advances in the concept of 'digital phenotype', which refers to the capture of characteristics of a psychiatric disorder by computerized measurement tools, is one paradigmatic example. Objective : The impact of the new technologies on health professionals practice in PTSD care remains to be determined. The recent evolutions could disrupt the clinical practices and practitioners in their beliefs, ethics and representations, going as far as questioning their professional culture. In the present paper, we conducted an extensive search to highlight the articles which reflect the potential of these new technologies. Method : We conducted an overview by querying PubMed database with the terms [PTSD] [Posttraumatic stress disorder] AND [Computer] OR [Computerized] OR [Mobile] OR [Automatic] OR [Automated] OR [Machine learning] OR [Sensor] OR [Heart rate variability] OR [HRV] OR [actigraphy] OR [actimetry] OR [digital] OR [motion] OR [temperature] OR [virtual reality]. Results : We summarized the synthesized literature in two categories: prediction and assessment (including diagnostic, screening and monitoring). Two independent reviewers screened, extracted data and quality appraised the sources. Results were synthesized narratively. Conclusions : This overview shows that many studies are underway allowing researchers to start building a PTSD digital phenotype using passive data obtained by biometric sensors. Active data obtained from Ecological Momentary Assessment (EMA) could allow clinicians to assess PTSD patients. The place of connected objects, Artificial Intelligence and remote monitoring of patients with psychiatric pathology remains to be defined. These tools

Do personality traits predict first onset in depressive and bipolar disorder?

DEFF Research Database (Denmark)

Christensen, Maj Vinberg; Kessing, Lars Vedel

2006-01-01

The aim was to investigate whether personality traits predict onset of the first depressive or manic episode (the vulnerability hypothesis) and whether personality might be altered by the mood disorder (the scar hypothesis). A systematic review of population-based and high-risk studies concerning...... personality traits and affective disorder in adults was conducted. Nine cross-sectional high-risk studies, seven longitudinal high-risk studies and nine longitudinal population-based studies were found. Most studies support the vulnerability hypothesis and there is evidence that neuroticism is a premorbid...... risk factor for developing depressive disorder. The evidence for the scar hypothesis is sparse, but the studies with the strongest design showed evidence for both hypotheses. Only few studies of bipolar disorder were found and the association between personality traits and bipolar disorder is unclear...

Brain activation predicts treatment improvement in patients with major depressive disorder.

LENUS (Irish Health Repository)

Samson, Andrea C

2012-02-01

Major depressive disorder (MDD) is associated with alterations in brain function that might be useful for therapy evaluation. The current study aimed to identify predictors for therapy improvement and to track functional brain changes during therapy. Twenty-one drug-free patients with MDD underwent functional MRI twice during performance of an emotional perception task: once before and once after 4 weeks of antidepressant treatment (mirtazapine or venlafaxine). Twelve healthy controls were investigated once with the same methods. A significant difference between groups was a relative greater activation of the right dorsolateral prefrontal cortex (dlPFC) in the patients vs. controls. Before treatment, patients responding better to pharmacological treatment showed greater activation in the dorsomedial PFC (dmPFC), posterior cingulate cortex (pCC) and superior frontal gyrus (SFG) when viewing of negative emotional pictures was compared with the resting condition. Activations in the caudate nucleus and insula contrasted for emotional compared to neutral stimuli were also associated with successful treatment. Responders had also significantly higher levels of activation, compared to non-responders, in a range of other brain regions. Brain activation related to treatment success might be related to altered self-referential processes and a differential response to external emotional stimuli, suggesting differences in the processing of emotionally salient stimuli between those who are likely to respond to pharmacological treatment and those who will not. The present investigation suggests the pCC, dmPFC, SFG, caudate nucleus and insula may have a key role as a biological marker for treatment response and predictor for therapeutic success.

Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?

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Haker, Helene; Schneebeli, Maya; Stephan, Klaas Enno

2016-01-01

Diagnosis and individualized treatment of autism spectrum disorder (ASD) represent major problems for contemporary psychiatry. Tackling these problems requires guidance by a pathophysiological theory. In this paper, we consider recent theories that re-conceptualize ASD from a "Bayesian brain" perspective, which posit that the core abnormality of ASD resides in perceptual aberrations due to a disbalance in the precision of prediction errors (sensory noise) relative to the precision of predictions (prior beliefs). This results in percepts that are dominated by sensory inputs and less guided by top-down regularization and shifts the perceptual focus to detailed aspects of the environment with difficulties in extracting meaning. While these Bayesian theories have inspired ongoing empirical studies, their clinical implications have not yet been carved out. Here, we consider how this Bayesian perspective on disease mechanisms in ASD might contribute to improving clinical care for affected individuals. Specifically, we describe a computational strategy, based on generative (e.g., hierarchical Bayesian) models of behavioral and functional neuroimaging data, for establishing diagnostic tests. These tests could provide estimates of specific cognitive processes underlying ASD and delineate pathophysiological mechanisms with concrete treatment targets. Written with a clinical audience in mind, this article outlines how the development of computational diagnostics applicable to behavioral and functional neuroimaging data in routine clinical practice could not only fundamentally alter our concept of ASD but eventually also transform the clinical management of this disorder.

Surgical improvement of speech disorder caused by amyotrophic lateral sclerosis.

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Saigusa, Hideto; Yamaguchi, Satoshi; Nakamura, Tsuyoshi; Komachi, Taro; Kadosono, Osamu; Ito, Hiroyuki; Saigusa, Makoto; Niimi, Seiji

2012-12-01

Amyotrophic lateral sclerosis (ALS) is a progressive debilitating neurological disease. ALS disturbs the quality of life by affecting speech, swallowing and free mobility of the arms without affecting intellectual function. It is therefore of significance to improve intelligibility and quality of speech sounds, especially for ALS patients with slowly progressive courses. Currently, however, there is no effective or established approach to improve speech disorder caused by ALS. We investigated a surgical procedure to improve speech disorder for some patients with neuromuscular diseases with velopharyngeal closure incompetence. In this study, we performed the surgical procedure for two patients suffering from severe speech disorder caused by slowly progressing ALS. The patients suffered from speech disorder with hypernasality and imprecise and weak articulation during a 6-year course (patient 1) and a 3-year course (patient 2) of slowly progressing ALS. We narrowed bilateral lateral palatopharyngeal wall at velopharyngeal port, and performed this surgery under general anesthesia without muscle relaxant for the two patients. Postoperatively, intelligibility and quality of their speech sounds were greatly improved within one month without any speech therapy. The patients were also able to generate longer speech phrases after the surgery. Importantly, there was no serious complication during or after the surgery. In summary, we performed bilateral narrowing of lateral palatopharyngeal wall as a speech surgery for two patients suffering from severe speech disorder associated with ALS. With this technique, improved intelligibility and quality of speech can be maintained for longer duration for the patients with slowly progressing ALS.

Predictive Models of Work-Related Musculoskeletal Disorders (WMSDs Among Sewing Machine Operators in the Garments Industry

Directory of Open Access Journals (Sweden)

Carlos Ignacio P. Lugay

2015-02-01

Full Text Available The Philippine garments industry has been a driving force in the country’s economy, with apparel manufacturing firms catering to the local and global markets and providing employment opportunities for skilled Filipinos. Tight competition from neighboring Asian countries however, has made the industry’s situation difficult to flourish, especially in the wake of the Association of Southeast Asian Nations (ASEAN 2015 Integration. To assist the industry, this research examined one of the more common problems among sewing machine operators, termed as Work-related Musculoskeletal Disorders (WMSDs. These disorders are reflective in the frequency and severity of the pain experienced by the sewers while accomplishing their tasks. The causes of these disorders were identified and were correlated with the frequency and severity of pain in various body areas of the operator. To forecast pain from WMSDs among the operators, mathematical models were developed to predict the combined frequency and severity of the pain from WMSDs. Loss time or “unofficial breaktimes” due to pain from WMSDs was likewise forecasted to determine its effects on the firm’s production capacity. Both these predictive models were developed in order to assist garment companies in anticipating better the effects of WMSDs and loss time in their operations. Moreover, ergonomic interventions were suggested to minimize pain from WMSDs, with the expectation of increased productivity of the operators and improved quality of their outputs.

A clinical prediction rule for detecting major depressive disorder in primary care: the PREDICT-NL study.

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Zuithoff, Nicolaas P A; Vergouwe, Yvonne; King, Michael; Nazareth, Irwin; Hak, Eelko; Moons, Karel G M; Geerlings, Mirjam I

2009-08-01

Major depressive disorder often remains unrecognized in primary care. Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. A total of 1046 subjects, aged 18-65 years, were included from seven large general practices in the center of The Netherlands. All subjects were recruited in the general practice waiting room, irrespective of their presenting complaint. Major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Text Revision edition criteria was assessed with the Composite International Diagnostic Interview. Candidate predictors were gender, age, educational level, being single, number of presented complaints, presence of non-somatic complaints, whether a diagnosis was assigned, consultation rate in past 12 months, presentation of depressive complaints or prescription of antidepressants in past 12 months, number of life events in past 6 months and any history of depression. The first multivariable logistic regression model including only predictors that require no confronting depression-related questions had a reasonable degree of discrimination (area under the receiver operating characteristic curve or concordance-statistic (c-statistic) = 0.71; 95% Confidence Interval (CI): 0.67-0.76). Addition of three simple though more depression-related predictors, number of life events and history of depression, significantly increased the c-statistic to 0.80 (95% CI: 0.76-0.83). After transforming this second model to an easily to use risk score, the lowest risk category (sum score depression, which increased to 49% in the highest category (sum score > or = 30). A clinical prediction rule allows GPs to identify patients-irrespective of their complaints-in whom diagnostic workup for major depressive disorder is indicated.

Threat-related selective attention predicts treatment success in childhood anxiety disorders.

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Legerstee, Jeroen S; Tulen, Joke H M; Kallen, Victor L; Dieleman, Gwen C; Treffers, Philip D A; Verhulst, Frank C; Utens, Elisabeth M W J

2009-02-01

The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. Participants consisted of 131 children with anxiety disorders (aged 8-16 years), who received standardized cognitive-behavioral therapy. At pretreatment, a pictorial dot-probe task was administered to assess selective attention. Both at pretreatment and posttreatment, diagnostic status of the children was evaluated with a semistructured clinical interview (the Anxiety Disorders Interview Schedule for Children). Selective attention for severely threatening pictures at pretreatment assessment was predictive of treatment success. Examination of the specific components of selective attention revealed that nonresponders showed difficulties to disengage their attention away from severe threat. Treatment responders showed a tendency not to engage their attention toward severe threat. Age was not associated with selective attention and treatment success. Threat-related selective attention is a significant predictor of treatment success in children with anxiety disorders. Clinically anxious children with difficulties disengaging their attention away from severe threat profit less from cognitive-behavioral therapy. For these children, additional training focused on learning to disengage attention away from anxiety-arousing stimuli may be beneficial.

Predictive gene testing for Huntington disease and other neurodegenerative disorders.

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Wedderburn, S; Panegyres, P K; Andrew, S; Goldblatt, J; Liebeck, T; McGrath, F; Wiltshire, M; Pestell, C; Lee, J; Beilby, J

2013-12-01

Controversies exist around predictive testing (PT) programmes in neurodegenerative disorders. This study sets out to answer the following questions relating to Huntington disease (HD) and other neurodegenerative disorders: differences between these patients in their PT journeys, why and when individuals withdraw from PT, and decision-making processes regarding reproductive genetic testing. A case series analysis of patients having PT from the multidisciplinary Western Australian centre for PT over the past 20 years was performed using internationally recognised guidelines for predictive gene testing in neurodegenerative disorders. Of 740 at-risk patients, 518 applied for PT: 466 at risk of HD, 52 at risk of other neurodegenerative disorders - spinocerebellar ataxias, hereditary prion disease and familial Alzheimer disease. Thirteen percent withdrew from PT - 80.32% of withdrawals occurred during counselling stages. Major withdrawal reasons related to timing in the patients' lives or unknown as the patient did not disclose the reason. Thirty-eight HD individuals had reproductive genetic testing: 34 initiated prenatal testing (of which eight withdrew from the process) and four initiated pre-implantation genetic diagnosis. There was no recorded or other evidence of major psychological reactions or suicides during PT. People withdrew from PT in relation to life stages and reasons that are unknown. Our findings emphasise the importance of: (i) adherence to internationally recommended guidelines for PT; (ii) the role of the multidisciplinary team in risk minimisation; and (iii) patient selection. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.

Psychological treatment of social anxiety disorder improves body dysmorphic concerns.

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Fang, Angela; Sawyer, Alice T; Aderka, Idan M; Hofmann, Stefan G

2013-10-01

Social anxiety disorder and body dysmorphic disorder are considered nosologically distinct disorders. In contrast, some cognitive models suggest that social anxiety disorder and body dysmorphic disorder share similar cognitive maintenance factors. The aim of this study was to examine the effects of psychological treatments for social anxiety disorder on body dysmorphic disorder concerns. In Study 1, we found that 12 weekly group sessions of cognitive-behavioral therapy led to significant decreases in body dysmorphic symptom severity. In Study 2, we found that an attention retraining intervention for social anxiety disorder was associated with a reduction in body dysmorphic concerns, compared to a placebo control condition. These findings support the notion that psychological treatments for individuals with primary social anxiety disorder improve co-occurring body dysmorphic disorder symptoms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

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Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2015-08-01

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Oppositional behavior and longitudinal predictions of early adulthood mental health problems in chronic tic disorders.

Science.gov (United States)

Thériault, Marie-Claude G; Bécue, Jean-Cyprien; Lespérance, Paul; Chouinard, Sylvain; Rouleau, Guy A; Richer, Francois

2018-03-16

Chronic tic disorders (TD) are associated with a number of psychological problems such as attention-deficit hyperactivity disorder (ADHD), obsessive-compulsive behavior (OCB), oppositional-defiant disorder (ODD) as well as anxious and depressive symptoms. ODD is often considered a risk factor for many psychological symptoms and recent work suggests that different ODD dimensions show independent predictions of later psychological problems. This study examined the longitudinal predictions between ODD dimensions of Irritability and Defiance and the most frequent comorbidities in TD from childhood to early adulthood. From an initial sample of 135, parent reports were obtained on 58 participants with TD using standard clinical questionnaires and semi-structured interviews. Defiance symptoms decreased from baseline to follow-up whereas Irritability symptoms were more stable over time. In multiple regressions, Irritability in childhood predicted anxiety and OCB in early adulthood while Defiance in childhood predicted ADHD and conduct disorder symptoms in early adulthood. No developmental link was found for depressive symptoms. Results indicate that ODD dimensions are developmentally linked to both internalizing and externalizing adult mental health symptoms in TD. Copyright © 2018. Published by Elsevier B.V.

Predictors of dropout in concurrent treatment of posttraumatic stress disorder and alcohol dependence: Rate of improvement matters.

Science.gov (United States)

Zandberg, Laurie J; Rosenfield, David; Alpert, Elizabeth; McLean, Carmen P; Foa, Edna B

2016-05-01

The present study examined predictors and moderators of dropout among 165 adults meeting DSM-IV criteria for posttraumatic stress disorder (PTSD) and alcohol dependence (AD). Participants were randomized to 24 weeks of naltrexone (NAL), NAL and prolonged exposure (PE), pill placebo, or pill placebo and PE. All participants received supportive AD counseling (the BRENDA manualized model). Logistic regression using the Fournier approach was conducted to investigate baseline predictors of dropout across the entire study sample. Rates of PTSD and AD symptom improvement were included to evaluate the impact of symptom change on dropout. Trauma type and rates of PTSD and AD improvement significantly predicted dropout, accounting for 76% of the variance in dropout. Accidents and "other" trauma were associated with the highest dropout, and physical assault was associated with the lowest dropout. For participants with low baseline PTSD severity, faster PTSD improvement predicted higher dropout. For those with high baseline severity, both very fast and very slow rates of PTSD improvement were associated with higher dropout. Faster rates of drinking improvement predicted higher dropout among participants who received PE. The current study highlights the influence of symptom trajectory on dropout risk. Clinicians may improve retention in PTSD-AD treatments by monitoring symptom change at regular intervals, and eliciting patient feedback on these changes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictors of dropout in concurrent treatment of posttraumatic stress disorder and alcohol dependence: Rate of improvement matters

Science.gov (United States)

Zandberg, Laurie J.; Rosenfield, David; Alpert, Elizabeth; McLean, Carmen P.; Foa, Edna B.

2016-01-01

Objective The present study examined predictors and moderators of dropout among 165 adults meeting DSM-IV criteria for posttraumatic stress disorder (PTSD) and alcohol dependence (AD). Participants were randomized to 24 weeks of naltrexone (NAL), NAL and prolonged exposure (PE), pill placebo, or pill placebo and PE. All participants received supportive AD counseling (the BRENDA manualized model). Method Logistic regression using the Fournier approach was conducted to investigate baseline predictors of dropout across the entire study sample. Rates of PTSD and AD symptom improvement were included to evaluate the impact of symptom change on dropout. Results Trauma type and rates of PTSD and AD improvement significantly predicted dropout, accounting for 76% of the variance in dropout. Accidents and “other” trauma were associated with the highest dropout, and physical assault was associated with the lowest dropout. For participants with low baseline PTSD severity, faster PTSD improvement predicted higher dropout. For those with high baseline severity, both very fast and very slow rates of PTSD improvement were associated with higher dropout. Faster rates of drinking improvement predicted higher dropout among participants who received PE. Conclusions The current study highlights the influence of symptom trajectory on dropout risk. Clinicians may improve retention in PTSD-AD treatments by monitoring symptom change at regular intervals, and eliciting patient feedback on these changes. PMID:26972745

Rapid response predicts 12-month post-treatment outcomes in binge-eating disorder: theoretical and clinical implications

Science.gov (United States)

Grilo, C. M.; White, M. A.; Wilson, G. T.; Gueorguieva, R.; Masheb, R. M.

2011-01-01

Background We examined rapid response in obese patients with binge-eating disorder (BED) in a clinical trial testing cognitive behavioral therapy (CBT) and behavioral weight loss (BWL). Method Altogether, 90 participants were randomly assigned to CBT or BWL. Assessments were performed at baseline, throughout and post-treatment and at 6- and 12-month follow-ups. Rapid response, defined as ≥70% reduction in binge eating by week four, was determined by receiver operating characteristic curves and used to predict outcomes. Results Rapid response characterized 57% of participants (67% of CBT, 47% of BWL) and was unrelated to most baseline variables. Rapid response predicted greater improvements across outcomes but had different prognostic significance and distinct time courses for CBT versus BWL. Patients receiving CBT did comparably well regardless of rapid response in terms of reduced binge eating and eating disorder psychopathology but did not achieve weight loss. Among patients receiving BWL, those without rapid response failed to improve further. However, those with rapid response were significantly more likely to achieve binge-eating remission (62% v. 13%) and greater reductions in binge-eating frequency, eating disorder psychopathology and weight loss. Conclusions Rapid response to treatment in BED has prognostic significance through 12-month follow-up, provides evidence for treatment specificity and has clinical implications for stepped-care treatment models for BED. Rapid responders who receive BWL benefit in terms of both binge eating and short-term weight loss. Collectively, these findings suggest that BWL might be a candidate for initial intervention in stepped-care models with an evaluation of progress after 1 month to identify non-rapid responders who could be advised to consider a switch to a specialized treatment. PMID:21923964

Frontolimbic neural circuit changes in emotional processing and inhibitory control associated with clinical improvement following transference-focused psychotherapy in borderline personality disorder.

Science.gov (United States)

Perez, David L; Vago, David R; Pan, Hong; Root, James; Tuescher, Oliver; Fuchs, Benjamin H; Leung, Lorene; Epstein, Jane; Cain, Nicole M; Clarkin, John F; Lenzenweger, Mark F; Kernberg, Otto F; Levy, Kenneth N; Silbersweig, David A; Stern, Emily

2016-01-01

Borderline personality disorder (BPD) is characterized by self-regulation deficits, including impulsivity and affective lability. Transference-focused psychotherapy (TFP) is an evidence-based treatment proven to reduce symptoms across multiple cognitive-emotional domains in BPD. This pilot study aimed to investigate neural activation associated with, and predictive of, clinical improvement in emotional and behavioral regulation in BPD following TFP. BPD subjects (n = 10) were scanned pre- and post-TFP treatment using a within-subjects design. A disorder-specific emotional-linguistic go/no-go functional magnetic resonance imaging paradigm was used to probe the interaction between negative emotional processing and inhibitory control. Analyses demonstrated significant treatment-related effects with relative increased dorsal prefrontal (dorsal anterior cingulate, dorsolateral prefrontal, and frontopolar cortices) activation, and relative decreased ventrolateral prefrontal cortex and hippocampal activation following treatment. Clinical improvement in constraint correlated positively with relative increased left dorsal anterior cingulate cortex activation. Clinical improvement in affective lability correlated positively with left posterior-medial orbitofrontal cortex/ventral striatum activation, and negatively with right amygdala/parahippocampal activation. Post-treatment improvements in constraint were predicted by pre-treatment right dorsal anterior cingulate cortex hypoactivation, and pre-treatment left posterior-medial orbitofrontal cortex/ventral striatum hypoactivation predicted improvements in affective lability. These preliminary findings demonstrate potential TFP-associated alterations in frontolimbic circuitry and begin to identify neural mechanisms associated with a psychodynamically oriented psychotherapy. © 2015 The Authors. Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology.

Testing predictions of the emotion regulation model of binge-eating disorder.

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Kenny, Therese E; Singleton, Christopher; Carter, Jacqueline C

2017-11-01

The emotion regulation (ER) model of binge eating posits that individuals with binge-eating disorder (BED) experience more intense emotions and greater difficulties in ER than individuals without BED, leading them to binge eat as a means of regulating emotions. According to this model, individuals with BED should report greater difficulties in ER than their non-BED counterparts, the severity of these difficulties should be positively associated with BED symptoms, and this association should be stronger when individuals experience persistent negative emotions (i.e., depression). Studies examining these hypotheses, however, have been limited. Data were collected from adults meeting the DSM 5 criteria for BED (n = 71; 93% female) and no history of an eating disorder (NED; n =  79; 83.5% female). Participants completed self-report measures of difficulties in ER, eating disorder (ED) psychopathology, and depression. Individuals with BED reported greater difficulties in ER compared to those with NED. Moreover, difficulties in ER predicted unique variance in binge frequency and ED psychopathology in BED. Depression moderated the association between ER difficulties and binge frequency such that emotion dysregulation and binge frequency were positively associated in those reporting high, but not low, depression levels. The association between difficulties in ER and ED pathology in BED suggests that treatments focusing on improving ER skills may be effective in treating this ED; however, the moderating effect of depression underscores the need for research on individual differences and treatment moderators. These findings suggest the importance of ER in understanding and treating BED. © 2017 Wiley Periodicals, Inc.

First-episode types in bipolar disorder: predictive associations with later illness.

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Baldessarini, R J; Tondo, L; Visioli, C

2014-05-01

Characteristics of initial illness in bipolar disorder (BD) may predict later morbidity. We reviewed computerized clinical records and life charts of DSM-IV-TR BD-I or BD-II patients at affiliated mood-disorder centers to ascertain relationships of initial major illnesses to later morbidity and other clinical characteristics. Adult BD patient-subjects (N=1081; 59.8% BD-I; 58.1% women; 43% ever hospitalized) were followed 15.7±12.8 years after onsets ranking: depression (59%)>mania (13%)>psychosis (8.0%)≥anxiety (7.6%)≥hypomania (6.7%)>mixed states (5.5%). Onset types differed in clinical characteristics and strongly predicted later morbidity. By initial episode types, total time-ill ranked: mania≥hypomania≥mixed-states≥psychosis>depression>anxiety. Depression was most prevalent long-term, overall; its ratio to mania-like illness (D/M, by per cent-time-ill) ranked by onset type: anxiety (4.75)>depression (3.27)>mixed states (1.39)>others (allanxiety (38.8%), depression (30.8%), or mixed onsets (13.3%); both were predicted by initial mania depression sequences. First-lifetime illnesses and cycles predicted later morbidity patterns among BD patients, indicating value of early morbidity for prognosis and long-term planning. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Threat-Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

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J.S. Legerstee (Jeroen); J.H.M. Tulen (Joke); V.L. Kallen (Victor); G.C. Dieleman (Gwen); P.D.A. Treffers (Philip); F.C. Verhulst (Frank); E.M.W.J. Utens (Elisabeth)

2009-01-01

textabstractAbstract OBJECTIVE: The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders

Threat-related selective attention predicts treatment success in childhood anxiety disorders

NARCIS (Netherlands)

Legerstee, Jeroen S.; Tulen, Joke H. M.; Kallen, Victor L.; Dieleman, Gwen C.; Treffers, Philip D. A.; Verhulst, Frank C.; Utens, Elisabeth M. W. J.

2009-01-01

The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. Participants

Improving Treatment Response for Paediatric Anxiety Disorders: An Information-Processing Perspective.

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Ege, Sarah; Reinholdt-Dunne, Marie Louise

2016-12-01

Cognitive behavioural therapy (CBT) is considered the treatment of choice for paediatric anxiety disorders, yet there remains substantial room for improvement in treatment outcomes. This paper examines whether theory and research into the role of information-processing in the underlying psychopathology of paediatric anxiety disorders indicate possibilities for improving treatment response. Using a critical review of recent theoretical, empirical and academic literature, the paper examines the role of information-processing biases in paediatric anxiety disorders, the extent to which CBT targets information-processing biases, and possibilities for improving treatment response. The literature reviewed indicates a role for attentional and interpretational biases in anxious psychopathology. While there is theoretical grounding and limited empirical evidence to indicate that CBT ameliorates interpretational biases, evidence regarding the effects of CBT on attentional biases is mixed. Novel treatment methods including attention bias modification training, attention feedback awareness and control training, and mindfulness-based therapy may hold potential in targeting attentional biases, and thereby in improving treatment response. The integration of novel interventions into an existing evidence-based protocol is a complex issue and faces important challenges with regard to determining the optimal treatment package. Novel interventions targeting information-processing biases may hold potential in improving response to CBT for paediatric anxiety disorders. Many important questions remain to be answered.

Plasma fluoxetine concentrations and clinical improvement in an adolescent sample diagnosed with major depressive disorder, obsessive-compulsive disorder, or generalized anxiety disorder.

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Blázquez, Ana; Mas, Sergi; Plana, Maria Teresa; Gassó, Patricia; Méndez, Iria; Torra, Mercè; Arnaiz, Joan Albert; Lafuente, Amàlia; Lázaro, Luisa

2014-06-01

Fluoxetine (FLX) has been one of the most widely studied selective serotonin reuptake inhibitors in adolescents. Despite its efficacy, however, 30% to 40% of patients do not respond to treatment. The aim of this study was to evaluate whether clinical improvement or adverse events are related to the corrected dose of FLX at 8 and 12 weeks after starting treatment in a sample of adolescents diagnosed with major depressive disorder, obsessive-compulsive disorder, or generalized anxiety disorder. Seventy-four subjects aged between 10 and 17 years participated in the study. Clinical improvement was measured with the Clinical Global Impression-Improvement Scale, whereas the UKU (Udvalg for Klinske Undersogelser) scale was administered to assess adverse effects of treatment. Fluoxetine per kilograms of body weight was related to serum concentration of FLX, NORFLX (norfluoxetine), FLX + NORFLX, and FLX/NORFLX. No relationship was found between dose-corrected FLX levels and therapeutic or adverse effects. No differences in serum concentrations were found between responders and nonresponders to treatment. Sex differences were observed in relation to dose and FLX serum concentration. The analysis by diagnosis revealed differences in FLX dose between obsessive-compulsive disorder patients and both generalized anxiety disorder and major depressive disorder patients. Fluoxetine response seems to be influenced by factors such as sex, diagnosis, or certain genes that might be involved in the drug's pharmacokinetics and pharmacodynamics. Clinical and pharmacogenetic studies are needed to elucidate further the differences between treatment responders and nonresponders.

Prediction of Splint Therapy Efficacy Using Bone Scan in Patients with Unilateral Temporomandibular Disorder

International Nuclear Information System (INIS)

Lee, Sang Mi; Lee, Won Woo; Yun, Pil Young; Kim, Young Kyun; Kim, Sang Eun

2009-01-01

It is not known whether bone scan is useful for the prediction of the prognosis of patients with temporomandibular disorders (TMD). The aim of the present study was to identify useful prognostic markers on bone scan for the pre-therapeutic assessment of patients with unilateral TMD. Between January 2005 and July 2007, 55 patients (M:F=9:46; mean age, 34.7±14.1 y) with unilateral TMD that underwent a pre-therapeutic bone scan were enrolled. Uptake of Tc-99m HDP in each temporomandibular joint (TMJ) was quantitated using a 13X13 pixel-square region-of-interest over TMJ and parietal skull area as background. TMJ uptake ratios and asymmetric indices were calculated. TMD patients were classified as improved or not improved and the bone scan findings associated with each group were investigated. Forty-six patients were improved, whereas 9 patients were not improved. There was no significant difference between the two groups of patients regarding the TMJ uptake ratio of the involved joint, the TMJ uptake ratio of the non-involved joint, and the asymmetric index (p>0.05). However, in a subgroup analysis, the patients with an increased uptake of Tc-99m HDP at the disease-involved TMJ, by visual assessment, could be easily identified by the asymmetric index; the patients that improved had a higher asymmetric index than the patients that did not improve (1.32±0.35 vs. 1.08±0.04, p=0.023), The Tc-99m HDP bone scan may help predict the prognosis of patients with unilateral TMD after splint therapy when the TMD-involved joint reveals increased uptake by visual assessment

Prediction of Splint Therapy Efficacy Using Bone Scan in Patients with Unilateral Temporomandibular Disorder

Energy Technology Data Exchange (ETDEWEB)

Lee, Sang Mi; Lee, Won Woo; Yun, Pil Young; Kim, Young Kyun; Kim, Sang Eun [Seoul National University Bundang Hospital, Seoul (Korea, Republic of)

2009-04-15

It is not known whether bone scan is useful for the prediction of the prognosis of patients with temporomandibular disorders (TMD). The aim of the present study was to identify useful prognostic markers on bone scan for the pre-therapeutic assessment of patients with unilateral TMD. Between January 2005 and July 2007, 55 patients (M:F=9:46; mean age, 34.7{+-}14.1 y) with unilateral TMD that underwent a pre-therapeutic bone scan were enrolled. Uptake of Tc-99m HDP in each temporomandibular joint (TMJ) was quantitated using a 13X13 pixel-square region-of-interest over TMJ and parietal skull area as background. TMJ uptake ratios and asymmetric indices were calculated. TMD patients were classified as improved or not improved and the bone scan findings associated with each group were investigated. Forty-six patients were improved, whereas 9 patients were not improved. There was no significant difference between the two groups of patients regarding the TMJ uptake ratio of the involved joint, the TMJ uptake ratio of the non-involved joint, and the asymmetric index (p>0.05). However, in a subgroup analysis, the patients with an increased uptake of Tc-99m HDP at the disease-involved TMJ, by visual assessment, could be easily identified by the asymmetric index; the patients that improved had a higher asymmetric index than the patients that did not improve (1.32{+-}0.35 vs. 1.08{+-}0.04, p=0.023), The Tc-99m HDP bone scan may help predict the prognosis of patients with unilateral TMD after splint therapy when the TMD-involved joint reveals increased uptake by visual assessment.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

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Maciukiewicz, Malgorzata; Marshe, Victoria S; Hauschild, Anne-Christin; Foster, Jane A; Rotzinger, Susan; Kennedy, James L; Kennedy, Sidney H; Müller, Daniel J; Geraci, Joseph

2018-04-01

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction. Copyright © 2017. Published by Elsevier Ltd.

A Non-linear Predictive Model of Borderline Personality Disorder Based on Multilayer Perceptron.

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Maldonato, Nelson M; Sperandeo, Raffaele; Moretto, Enrico; Dell'Orco, Silvia

2018-01-01

Borderline Personality Disorder is a serious mental disease, classified in Cluster B of DSM IV-TR personality disorders. People with this syndrome presents an anamnesis of traumatic experiences and shows dissociative symptoms. Since not all subjects who have been victims of trauma develop a Borderline Personality Disorder, the emergence of this serious disease seems to have the fragility of character as a predisposing condition. Infect, numerous studies show that subjects positive for diagnosis of Borderline Personality Disorder had scores extremely high or extremely low to some temperamental dimensions (harm Avoidance and reward dependence) and character dimensions (cooperativeness and self directedness). In a sample of 602 subjects, who have had consecutive access to an Outpatient Mental Health Service, it was evaluated the presence of Borderline Personality Disorder using the semi-structured interview for the DSM IV-TR personality disorders. In this population we assessed the presence of dissociative symptoms with the Dissociative Experiences Scale and the personality traits with the Temperament and Character Inventory developed by Cloninger. To assess the weight and the predictive value of these psychopathological dimensions in relation to the Borderline Personality Disorder diagnosis, a neural network statistical model called "multilayer perceptron," was implemented. This model was developed with a dichotomous dependent variable, consisting in the presence or absence of the diagnosis of borderline personality disorder and with five covariates. The first one is the taxonomic subscale of dissociative experience scale, the others are temperamental and characterial traits: Novelty-Seeking, Harm-Avoidance, Self-Directedness and Cooperativeness. The statistical model, that results satisfactory, showed a significance capacity (89%) to predict the presence of borderline personality disorder. Furthermore, the dissociative symptoms seem to have a greater influence than

A Non-linear Predictive Model of Borderline Personality Disorder Based on Multilayer Perceptron

Directory of Open Access Journals (Sweden)

Nelson M. Maldonato

2018-04-01

Full Text Available Borderline Personality Disorder is a serious mental disease, classified in Cluster B of DSM IV-TR personality disorders. People with this syndrome presents an anamnesis of traumatic experiences and shows dissociative symptoms. Since not all subjects who have been victims of trauma develop a Borderline Personality Disorder, the emergence of this serious disease seems to have the fragility of character as a predisposing condition. Infect, numerous studies show that subjects positive for diagnosis of Borderline Personality Disorder had scores extremely high or extremely low to some temperamental dimensions (harm Avoidance and reward dependence and character dimensions (cooperativeness and self directedness. In a sample of 602 subjects, who have had consecutive access to an Outpatient Mental Health Service, it was evaluated the presence of Borderline Personality Disorder using the semi-structured interview for the DSM IV-TR personality disorders. In this population we assessed the presence of dissociative symptoms with the Dissociative Experiences Scale and the personality traits with the Temperament and Character Inventory developed by Cloninger. To assess the weight and the predictive value of these psychopathological dimensions in relation to the Borderline Personality Disorder diagnosis, a neural network statistical model called “multilayer perceptron,” was implemented. This model was developed with a dichotomous dependent variable, consisting in the presence or absence of the diagnosis of borderline personality disorder and with five covariates. The first one is the taxonomic subscale of dissociative experience scale, the others are temperamental and characterial traits: Novelty-Seeking, Harm-Avoidance, Self-Directedness and Cooperativeness. The statistical model, that results satisfactory, showed a significance capacity (89% to predict the presence of borderline personality disorder. Furthermore, the dissociative symptoms seem to have a

Predicting posttraumatic stress disorder in children and parents following accidental child injury: evaluation of the Screening Tool for Early Predictors of Posttraumatic Stress Disorder (STEPP).

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van Meijel, Els P M; Gigengack, Maj R; Verlinden, Eva; Opmeer, Brent C; Heij, Hugo A; Goslings, J Carel; Bloemers, Frank W; Luitse, Jan S K; Boer, Frits; Grootenhuis, Martha A; Lindauer, Ramón J L

2015-05-12

Children and their parents are at risk of posttraumatic stress disorder (PTSD) following injury due to pediatric accidental trauma. Screening could help predict those at greatest risk and provide an opportunity for monitoring so that early intervention may be provided. The purpose of this study was to evaluate the Screening Tool for Early Predictors of Posttraumatic Stress Disorder (STEPP) in a mixed-trauma sample in a non-English speaking country (the Netherlands). Children aged 8-18 and one of their parents were recruited in two academic level I trauma centers. The STEPP was assessed in 161 children (mean age 13.9 years) and 156 parents within one week of the accident. Three months later, clinical diagnoses and symptoms of PTSD were assessed in 147 children and 135 parents. We used the Anxiety Disorders Interview Schedule for DSM-IV - Child and Parent version, the Children's Revised Impact of Event Scale and the Impact of Event Scale-Revised. Receiver Operating Characteristic analyses were performed to estimate the Areas Under the Curve as a measure of performance and to determine the optimal cut-off score in our sample. Sensitivity, specificity, positive and negative predictive values were calculated. The aim was to maximize both sensitivity and negative predictive values. PTSD was diagnosed in 12% of the children; 10% of their parents scored above the cut-off point for PTSD. At the originally recommended cut-off scores (4 for children, 3 for parents), the sensitivity in our sample was 41% for children and 54% for parents. Negative predictive values were 92% for both groups. Adjusting the cut-off scores to 2 improved sensitivity to 82% for children and 92% for parents, with negative predictive values of 92% and 96%, respectively. With adjusted cut-off scores, the STEPP performed well: 82% of the children and 92% of the parents with a subsequent positive diagnosis were identified correctly. Special attention in the screening procedure is required because of a

Predicting personality disorder functioning styles by the Chinese Adjective Descriptors of Personality: a preliminary trial in healthy people and personality disorder patients.

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Fan, Hongying; Zhu, Qisha; Ma, Guorong; Shen, Chanchan; Zhang, Bingren; Wang, Wei

2016-08-30

Cultural and personality factors might contribute to the clinical differences of psychiatric patients all over the world including China. One cultural oriented Chinese Adjective Descriptors of Personality (CADP) designed to measure normal personality traits, might be specifically associated with different personality disorder functioning styles. We therefore have invited 201 healthy volunteers and 67 personality disorder patients to undergo CADP, the Parker Personality Measure (PERM), and the Plutchik-van Praag Depression Inventory (PVP) tests. Patients scored significantly higher on PVP scale and all 11 PERM personality disorder functioning styles, as well as CADP Emotional and Unsocial traits. The PVP was significantly correlated with some CADP traits and PERM styles in both groups. In healthy volunteers, only one CADP trait, Unsocial, prominently predicted 11 PERM styles. By contrast in patients, CADP Intelligent predicted the PERM Narcissistic and Passive-Aggressive styles; CADP Emotional the PERM Paranoid, Borderline, and Histrionic styles; CADP Conscientious the PERM Obsessive-Compulsive style; CADP Unsocial the PERM Schizotypal, Antisocial, Narcissistic, Avoidant, Dependent, and Passive-Aggressive styles; CADP Agreeable the PERM Antisocial style. As a preliminary study, our results demonstrated that, in personality disorder patients, all five CADP traits were specifically associated with almost all 11 personality disorder functioning styles, indicating that CADP might be used as an aid to diagnose personality disorders in China.

Improving Earth/Prediction Models to Improve Network Processing

Science.gov (United States)

Wagner, G. S.

2017-12-01

The United States Atomic Energy Detection System (USAEDS) primaryseismic network consists of a relatively small number of arrays andthree-component stations. The relatively small number of stationsin the USAEDS primary network make it both necessary and feasibleto optimize both station and network processing.Station processing improvements include detector tuning effortsthat use Receiver Operator Characteristic (ROC) curves to helpjudiciously set acceptable Type 1 (false) vs. Type 2 (miss) errorrates. Other station processing improvements include the use ofempirical/historical observations and continuous background noisemeasurements to compute time-varying, maximum likelihood probabilityof detection thresholds.The USAEDS network processing software makes extensive use of theazimuth and slowness information provided by frequency-wavenumberanalysis at array sites, and polarization analysis at three-componentsites. Most of the improvements in USAEDS network processing aredue to improvements in the models used to predict azimuth, slowness,and probability of detection. Kriged travel-time, azimuth andslowness corrections-and associated uncertainties-are computedusing a ground truth database. Improvements in station processingand the use of improved models for azimuth, slowness, and probabilityof detection have led to significant improvements in USADES networkprocessing.

Low pre-treatment end-tidal CO2 predicts dropout from cognitive-behavioral therapy for anxiety and related disorders.

Science.gov (United States)

Tolin, David F; Billingsley, Amber L; Hallion, Lauren S; Diefenbach, Gretchen J

2017-03-01

Recent clinical trial research suggests that baseline low end-tidal CO 2 (ETCO 2 , the biological marker of hyperventilation) may predict poorer response to cognitive-behavioral therapy (CBT) for anxiety-related disorders. The present study examined the predictive value of baseline ETCO 2 among patients treated for such disorders in a naturalistic clinical setting. Sixty-nine adults with a primary diagnosis of a DSM-5 anxiety disorder, obsessive-compulsive disorder, or posttraumatic stress disorder completed a 4-min assessment of resting ETCO 2 , and respiration rate (the first minute was analyzed). Lower ETCO 2 was not associated with a diagnosis of panic disorder, and was associated with lower subjective distress ratings on certain measures. Baseline ETCO 2 significantly predicted treatment dropout: those meeting cutoff criteria for hypocapnia were more than twice as likely to drop out of treatment, and ETCO 2 significantly predicted dropout beyond other pre-treatment variables. Weekly measurement suggested that the lower-ETCO 2 patients who dropped out were not responding well to treatment prior to dropout. The present results, along with previous clinical trial data, suggest that lower pre-treatment ETCO 2 is a negative prognostic indicator for CBT for anxiety-related disorders. It is suggested that patients with lower ETCO 2 might benefit from additional intervention that targets respiratory abnormality. Copyright © 2016 Elsevier Ltd. All rights reserved.

Panic disorder and health-related quality of life: the predictive roles of anxiety sensitivity and trait anxiety.

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Kang, Eun-Ho; Kim, Borah; Choe, Ah Young; Lee, Jun-Yeob; Choi, Tai Kiu; Lee, Sang-Hyuk

2015-01-30

Panic disorder (PD) is a very common anxiety disorder and is often a chronic disabling condition. However, little is known about the factors that predict health-related quality of life (HRQOL) other than sociodemographic factors and illness-related symptomatology that explain HRQOL in only small to modest degrees. This study explored whether anxiety-related individual traits including anxiety sensitivity and trait anxiety can predict independently HRQOL in panic patients. Patients with panic disorder with or without agoraphobia (N=230) who met the diagnostic criteria in the Structured Clinical Interview for DSM-IV were recruited. Stepwise regression analysis was performed to determine the factors that predict HRQOL in panic disorder. HRQOL was assessed by the 36-item Short-Form Health Survey (SF-36). Anxiety sensitivity was an independent predictor of bodily pain and social functioning whereas trait anxiety independently predicted all of the eight domains of the SF-36. Our data suggests that the assessment of symptomatology as well as individual anxiety-related trait should be included in the evaluation of HRQOL in panic patients. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Personality traits predict treatment outcome with an antidepressant in patients with functional gastrointestinal disorder.

Science.gov (United States)

Tanum, L; Malt, U F

2000-09-01

We investigated the relationship between personality traits and response to treatment with the tetracyclic antidepressant mianserin or placebo in patients with functional gastrointestinal disorder (FGD) without psychopathology. Forty-eight patients completed the Buss-Durkee Hostility Inventory, Neuroticism Extroversion Openness -Personality Inventory (NEO-PI), and Eysenck Personality Questionnaire (EPQ), neuroticism + lie subscales, before they were consecutively allocated to a 7-week double-blind treatment study with mianserin or placebo. Treatment response to pain and target symptoms were recorded daily with the Visual Analogue Scale and Clinical Global Improvement Scale at every visit. A low level of neuroticism and little concealed aggressiveness predicted treatment outcome with the antidepressant drug mianserin in non-psychiatric patients with FGD. Inversely, moderate to high neuroticism and marked concealed aggressiveness predicted poor response to treatment. These findings were most prominent in women. Personality traits were better predictors of treatment outcome than serotonergic sensitivity assessed with the fenfluramine test. Assessment of the personality traits negativism, irritability, aggression, and neuroticism may predict response to drug treatment of FGD even when serotonergic sensitivity is controlled for. If confirmed in future studies, the findings point towards a more differential psychopharmacologic treatment of FGD.

Decadal climate predictions improved by ocean ensemble dispersion filtering

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

Brain anatomy and chemistry may predict treatment response in paediatric obsessive--compulsive disorder.

Science.gov (United States)

Rosenberg, D R; MacMillan, S N; Moore, G J

2001-06-01

Obsessive--compulsive disorder (OCD) is a severe, highly prevalent and often chronically disabling illness with frequent onset in childhood and adolescence. This underscores the importance of studying the illness during childhood near the onset of illness to minimize potential confounds of long-term illness duration and treatment intervention as well as to examine the developmental underpinnings of the illness. In this review, the authors focus on an integrated series of brain-imaging studies in paediatric OCD suggesting a reversible glutamatergically mediated thalamo-cortical--striatal dysfunction in OCD and their relevance for improved diagnosis and treatment of the condition. Developmental neurobiological models for OCD are presented and particular attention is devoted to evaluating neuroimaging studies designed to test these models and how they may help predict treatment response in paediatric OCD.

Does impulsivity predict outcome in treatment for binge eating disorder? A multimodal investigation.

Science.gov (United States)

Manasse, Stephanie M; Espel, Hallie M; Schumacher, Leah M; Kerrigan, Stephanie G; Zhang, Fengqing; Forman, Evan M; Juarascio, Adrienne S

2016-10-01

Multiple dimensions of impulsivity (e.g., affect-driven impulsivity, impulsive inhibition - both general and food-specific, and impulsive decision-making) are associated with binge eating pathology cross-sectionally, yet the literature on whether impulsivity predicts treatment outcome is limited. The present pilot study explored impulsivity-related predictors of 20-week outcome in a small open trial (n = 17) of a novel treatment for binge eating disorder. Overall, dimensions of impulsivity related to emotions (i.e., negative urgency) and food cues emerged as predictors of treatment outcomes (i.e., binge eating frequency and global eating pathology as measured by the Eating Disorders Examination), while more general measures of impulsivity were statistically unrelated to global eating pathology or binge frequency. Specifically, those with higher levels of negative urgency at baseline experienced slower and less pronounced benefit from treatment, and those with higher food-specific impulsivity had more severe global eating pathology at baseline that was consistent at post-treatment and follow-up. These preliminary findings suggest that patients high in negative urgency and with poor response inhibition to food cues may benefit from augmentation of existing treatments to achieve optimal outcomes. Future research will benefit from replication with a larger sample, parsing out the role of different dimensions of impulsivity in treatment outcome for eating disorders, and identifying how treatment can be improved to accommodate higher levels of baseline impulsivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Absence of back disorders in adults and work-related predictive factors in a 5-year perspective.

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Reigo, T; Tropp, H; Timpka, T

2001-06-01

Factors important for avoiding back disorders in different age-groups have seldom been compared and studied over time. We therefore set out to study age-related differences in socio-economic and work-related factors associated with the absence of back disorders in a 5-year comparative cohort study using a mailed questionnaire. Two subgroups (aged 25-34 and 54-59 years) derived from a representative sample of the Swedish population were followed at baseline, 1 year and 5 years. Questions were asked about the duration of back pain episodes, relapses, work changes and work satisfaction. A work adaptability, partnership, growth, affection, resolve (APGAR) score was included in the final questionnaire. Multivariate logistic regression was used to identify factors predicting the absence of back disorders. Absence of physically heavy work predicted an absence of back disorders [odds ratio (OR), 2.86; 95% confidence interval (CI), 1.3-6.3] in the older group. In the younger age-group, the absence of stressful work predicted absence of back disorders (OR, 2.0; 95% CI, 1.1-3.6). Thirty-seven per cent of the younger age-group and 43% of the older age-group did not experience any back pain episodes during the study period. The exploratory work APGAR scores indicated that back disorders were only associated with lower work satisfaction in the older group. The analyses point out the importance of avoiding perceived psychological stress in the young and avoiding perceived physically heavy work in the older age-group for avoiding back disorders. The results suggest a need for different programmes at workplaces to avoid back disorders depending on the age of the employees concerned.

Predicting the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition): The Mystery of How to Constrain Unchecked Growth.

Science.gov (United States)

Blashfield, Roger K; Fuller, A Kenneth

2016-06-01

Twenty years ago, slightly after the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition was published, we predicted the characteristics of the future Diagnostic and Statistical Manual of Mental Disorders (fifth edition) (). Included in our predictions were how many diagnoses it would contain, the physical size of the Diagnostic and Statistical Manual of Mental Disorders (fifth edition), who its leader would be, how many professionals would be involved in creating it, the revenue generated, and the color of its cover. This article reports on the accuracy of our predictions. Our largest prediction error concerned financial revenue. The earnings growth of the DSM's has been remarkable. Drug company investments, insurance benefits, the financial need of the American Psychiatric Association, and the research grant process are factors that have stimulated the growth of the DSM's. Restoring order and simplicity to the classification of mental disorders will not be a trivial task.

Dexamethasone-suppressed cortisol awakening response predicts treatment outcome in posttraumatic stress disorder

NARCIS (Netherlands)

Nijdam, M. J.; van Amsterdam, J. G. C.; Gersons, B. P. R.; Olff, M.

2015-01-01

Posttraumatic stress disorder (PTSD) has been associated with several alterations in the neuroendocrine system, including enhanced cortisol suppression in response to the dexamethasone suppression test. The aim of this study was to examine whether specific biomarkers of PTSD predict treatment

Adding propensity scores to pure prediction models fails to improve predictive performance

Directory of Open Access Journals (Sweden)

Amy S. Nowacki

2013-08-01

Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

A Predictive Coding Account of Psychotic Symptoms in Autism Spectrum Disorder

Science.gov (United States)

van Schalkwyk, Gerrit I.; Volkmar, Fred R.; Corlett, Philip R.

2017-01-01

The co-occurrence of psychotic and autism spectrum disorder (ASD) symptoms represents an important clinical challenge. Here we consider this problem in the context of a computational psychiatry approach that has been applied to both conditions--predictive coding. Some symptoms of schizophrenia have been explained in terms of a failure of top-down…

Personality Pathology Predicts Outcomes in a Treatment-Seeking Sample with Bipolar I Disorder

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Susan J. Wenze

2014-01-01

Full Text Available We conducted a secondary analysis of data from a clinical trial to explore the relationship between degree of personality disorder (PD pathology (i.e., number of subthreshold and threshold PD symptoms and mood and functioning outcomes in Bipolar I Disorder (BD-I. Ninety-two participants completed baseline mood and functioning assessments and then underwent 4 months of treatment for an index manic, mixed, or depressed phase acute episode. Additional assessments occurred over a 28-month follow-up period. PD pathology did not predict psychosocial functioning or manic symptoms at 4 or 28 months. However, it did predict depressive symptoms at both timepoints, as well as percent time symptomatic. Clusters A and C pathology were most strongly associated with depression. Our findings fit with the literature highlighting the negative repercussions of PD pathology on a range of outcomes in mood disorders. This study builds upon previous research, which has largely focused on major depression and which has primarily taken a categorical approach to examining PD pathology in BD.

Attentional Bias Predicts Increased Reward Salience and Risk Taking in Bipolar Disorder.

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Mason, Liam; Trujillo-Barreto, Nelson J; Bentall, Richard P; El-Deredy, Wael

2016-02-15

There is amassing evidence that risky decision-making in bipolar disorder is related to reward-based differences in frontostriatal regions. However, the roles of early attentional and later cognitive processes remain unclear, limiting theoretical understanding and development of targeted interventions. Twenty euthymic bipolar disorder and 19 matched control participants played a Roulette task in which they won and lost money. Event-related potentials and source analysis were used to quantify predominantly sensory-attentional (N1), motivational salience (feedback-related negativities [FRN]), and cognitive appraisal (P300) stages of processing. We predicted that the bipolar disorder group would show increased N1, consistent with increased attentional orienting, and reduced FRN, consistent with a bias to perceive outcomes more favorably. As predicted, the bipolar disorder group showed increased N1 and reduced FRN but no differences in P300. N1 amplitude was additionally associated with real-life risk taking, and N1 source activity was reduced in visual cortex but increased activity in precuneus, frontopolar, and premotor cortex, compared to those of controls. These findings demonstrate an early attentional bias to reward that potentially drives risk taking by priming approach behavior and elevating reward salience in the frontostriatal pathway. Although later cognitive appraisals of these inputs may be relatively intact in remission, interventions targeting attention orienting may also be effective in long-term reduction of relapse. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Drug Improves Birth Rates for Women with Ovary Disorder

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... NIH Research Matters July 21, 2014 Drug Improves Birth Rates for Women with Ovary Disorder At a ... more effective than standard therapy in increasing live births for women with polycystic ovary syndrome. Letrozole could ...

Effectiveness of Memantine in Improvement of Cognitive Deficits in Specific Learning Disorder

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Elham Ahmadi Zahrani

2016-12-01

Full Text Available Abstract Background: Specific learning disorder is a neurodevelopmental disorder characterized by persistent difficulties in learning academic skills in reading, written expression, or mathematics. This study was performed to investigate the effectiveness of memantine in the relief of cognitive deficits (selective attention, sustained attention, and working memory in specific learning disorder. Materials and Methods: This study is a clinical trial. Of all children 8-12 years referred to Amir Kabir Hospital 94 patients diagnosed with specific learning disorder based on DSMV diagnostic interview referred by specialist and randomly divided by two groups, memantine and placebo. Cognitive deficits before and after treatment were measured with continuous performance test, Stroop test and Wechsler Digit Span forward and reverse and Corsi test. Results: Multivariate analysis of variance showed a significant difference in error when answering, omission answer and corrected answer in continuous performance test, but this difference is not significant in response time. Difference in forward, reverse and collected auditory was significant and not significant in the auditory span. In active visual working memory at corsi cube test, difference was significant (p <0.05. Conclusion: The results showed that memantine in improvement of sustained attention, auditory working memory and visual working memory, is effective, while in selective attention is not effective and according to similarities of learning disorder and Attention deficit / Hyperactivity disorder (ADHD and the effectiveness of memantine in improvement of symptoms of ADHD, we can also use this drug in improvement of cognitive deficits of specific learning disorder.

Elevated left mid-frontal cortical activity prospectively predicts conversion to bipolar I disorder

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Nusslock, Robin; Harmon-Jones, Eddie; Alloy, Lauren B.; Urosevic, Snezana; Goldstein, Kim; Abramson, Lyn Y.

2013-01-01

Bipolar disorder is characterized by a hypersensitivity to reward-relevant cues and a propensity to experience an excessive increase in approach-related affect, which may be reflected in hypo/manic symptoms. The present study examined the relationship between relative left-frontal electroencephalographic (EEG) activity, a proposed neurophysiological index of approach-system sensitivity and approach/reward-related affect, and bipolar course and state-related variables. Fifty-eight individuals with cyclothymia or bipolar II disorder and 59 healthy control participants with no affective psychopathology completed resting EEG recordings. Alpha power was obtained and asymmetry indices computed for homologous electrodes. Bipolar spectrum participants were classified as being in a major/minor depressive episode, a hypomanic episode, or a euthymic/remitted state at EEG recording. Participants were then followed prospectively for an average 4.7 year follow-up period with diagnostic interview assessments every four-months. Sixteen bipolar spectrum participants converted to bipolar I disorder during follow-up. Consistent with hypotheses, elevated relative left-frontal EEG activity at baseline 1) prospectively predicted a greater likelihood of converting from cyclothymia or bipolar II disorder to bipolar I disorder over the 4.7 year follow-up period, 2) was associated with an earlier age-of-onset of first bipolar spectrum episode, and 3) was significantly elevated in bipolar spectrum individuals in a hypomanic episode at EEG recording. This is the first study to identify a neurophysiological marker that prospectively predicts conversion to bipolar I disorder. The fact that unipolar depression is characterized by decreased relative left-frontal EEG activity suggests that unipolar depression and vulnerability to hypo/mania may be characterized by different profiles of frontal EEG asymmetry. PMID:22775582

Improved Modeling and Prediction of Surface Wave Amplitudes

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2017-05-31

AFRL-RV-PS- AFRL-RV-PS- TR-2017-0162 TR-2017-0162 IMPROVED MODELING AND PREDICTION OF SURFACE WAVE AMPLITUDES Jeffry L. Stevens, et al. Leidos...data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented...SUBTITLE Improved Modeling and Prediction of Surface Wave Amplitudes 5a. CONTRACT NUMBER FA9453-14-C-0225 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER

Early improvement in eating attitudes during cognitive behavioural therapy for eating disorders: the impact of personality disorder cognitions.

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Park, Emma C; Waller, Glenn; Gannon, Kenneth

2014-03-01

The personality disorders are commonly comorbid with the eating disorders. Personality disorder pathology is often suggested to impair the treatment of axis 1 disorders, including the eating disorders. This study examined whether personality disorder cognitions reduce the impact of cognitive behavioural therapy (CBT) for eating disorders, in terms of treatment dropout and change in eating disorder attitudes in the early stages of treatment. Participants were individuals with a diagnosed eating disorder, presenting for individual outpatient CBT. They completed measures of personality disorder cognitions and eating disorder attitudes at sessions one and six of CBT. Drop-out rates prior to session six were recorded. CBT had a relatively rapid onset of action, with a significant reduction in eating disorder attitudes over the first six sessions. Eating disorder attitudes were most strongly associated with cognitions related to anxiety-based personality disorders (avoidant, obsessive-compulsive and dependent). Individuals who dropped out of treatment prematurely had significantly higher levels of dependent personality disorder cognitions than those who remained in treatment. For those who remained in treatment, higher levels of avoidant, histrionic and borderline personality disorder cognitions were associated with a greater change in global eating disorder attitudes. CBT's action and retention of patients might be improved by consideration of such personality disorder cognitions when formulating and treating the eating disorders.

Improving contact prediction along three dimensions.

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Christoph Feinauer

2014-10-01

Full Text Available Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i filter and align the raw sequence data representing the evolutionarily related proteins; (ii choose a predictive model to describe a sequence alignment; (iii infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.

Predicting Mood Changes in Bipolar Disorder through Heartbeat Nonlinear Dynamics.

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Valenza, Gaetano; Nardelli, Mimma; Lanata', Antonio; Gentili, Claudio; Bertschy, Gilles; Kosel, Markus; Scilingo, Enzo Pasquale

2016-04-20

Bipolar Disorder (BD) is characterized by an alternation of mood states from depression to (hypo)mania. Mixed states, i.e., a combination of depression and mania symptoms at the same time, can also be present. The diagnosis of this disorder in the current clinical practice is based only on subjective interviews and questionnaires, while no reliable objective psychophysiological markers are available. Furthermore, there are no biological markers predicting BD outcomes, or providing information about the future clinical course of the phenomenon. To overcome this limitation, here we propose a methodology predicting mood changes in BD using heartbeat nonlinear dynamics exclusively, derived from the ECG. Mood changes are here intended as transitioning between two mental states: euthymic state (EUT), i.e., the good affective balance, and non-euthymic (non-EUT) states. Heart Rate Variability (HRV) series from 14 bipolar spectrum patients (age: 33.439.76, age range: 23-54; 6 females) involved in the European project PSYCHE, undergoing whole night ECG monitoring were analyzed. Data were gathered from a wearable system comprised of a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire ECGs. Each patient was monitored twice a week, for 14 weeks, being able to perform normal (unstructured) activities. From each acquisition, the longest artifact-free segment of heartbeat dynamics was selected for further analyses. Sub-segments of 5 minutes of this segment were used to estimate trends of HRV linear and nonlinear dynamics. Considering data from a current observation at day t0, and past observations at days (t1, t2,...,), personalized prediction accuracies in forecasting a mood state (EUT/non-EUT) at day t+1 were 69% on average, reaching values as high as 83.3%. This approach opens to the possibility of predicting mood states in bipolar patients through heartbeat nonlinear dynamics exclusively.

Text mining improves prediction of protein functional sites.

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Karin M Verspoor

Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

Text Mining Improves Prediction of Protein Functional Sites

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Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

Improving management of patients with autism spectrum disorder having scheduled surgery: optimizing practice.

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Thompson, Debbie Gearner; Tielsch-Goddard, Anna

2014-01-01

Surgical preparation for children with autism spectrum disorders can be a challenge to perioperative staff because of the unique individual needs and behaviors in this population. Most children with autism function best in predictable, routine environments, and being in the hospital and other health care settings can create a stressful situation. This prospective, descriptive, quality improvement project was conducted to optimize best practices for perioperative staff and better individualize the plan of care for the autistic child and his or her family. Forty-three patients with a diagnosis of autism or autistic spectrum disorder were seen over 6 months at a suburban pediatric hospital affiliated with a major urban pediatric hospital and had an upcoming scheduled surgery or procedure requiring anesthesia. Caregivers were interviewed before and after surgery to collect information to better help their child cope with their hospital visit. In an evaluation of project outcomes, data were tabulated and summarized and interview data were qualitatively coded for emerging themes to improve the perioperative process for the child. Findings showed that staff members were able to recognize potential and actual stressors and help identify individual needs of surgical patients with autism. The families were pleased and appreciative of the individual attention and focus on their child's special needs. Investigators also found increased staff interest in optimizing the surgical experience for autistic children. Copyright © 2014 National Association of Pediatric Nurse Practitioners. Published by Mosby, Inc. All rights reserved.

What characteristics of primary anxiety disorders predict subsequent major depressive disorder?

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Bittner, Antje; Goodwin, Renee D; Wittchen, Hans-Ulrich; Beesdo, Katja; Höfler, Michael; Lieb, Roselind

2004-05-01

The goal of this study was to examine the associations between specific anxiety disorders and the risk of major depressive disorder and to explore the role of various clinical characteristics of anxiety disorders in these relationships using a prospective, longitudinal design. The data are from a 4-year prospective, longitudinal community study, which included both baseline and follow-up survey data on 2548 adolescents and young adults aged 14 to 24 years at baseline. DSM-IV diagnoses were made using the Munich-Composite International Diagnostic Interview. The presence at baseline of any anxiety disorder (odds ratio [OR] = 2.2 [95% CI = 1.6 to 3.2]) and each of the anxiety disorders (specific phobia, OR = 1.9 [95% CI = 1.3 to 2.8]; social phobia, OR = 2.9 [95% CI = 1.7 to 4.8]; agoraphobia, OR = 3.1 [95% CI = 1.4 to 6.7]; panic disorder, OR = 3.4 [95% CI = 1.2 to 9.0]; generalized anxiety disorder, OR = 4.5 [95% CI = 1.9 to 10.3]) was associated with a significantly (p depressive disorder. These associations remained significant after we adjusted for mental disorders occurring prior to the onset of the anxiety disorder, with the exception of the panic disorder association. The following clinical characteristics of anxiety disorders were associated with a significantly (p depressive disorder: more than 1 anxiety disorder, severe impairment due to the anxiety disorder, and comorbid panic attacks. In the final model, which included all clinical characteristics, severe impairment remained the only clinical characteristic that was an independent predictor of the development of major depressive disorder (OR = 2.2 [95% CI = 1.0 to 4.4]). Our findings suggest that anxiety disorders are risk factors for the first onset of major depressive disorder. Although a number of clinical characteristics of anxiety disorders appear to play a role in the association between anxiety disorders and depression, severe impairment is the strongest predictor of major depressive disorder.

Implicit attitudes toward eating stimuli differentiate eating disorder and non-eating disorder groups and predict eating disorder behaviors.

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Smith, April R; Forrest, Lauren N; Velkoff, Elizabeth A; Ribeiro, Jessica D; Franklin, Joseph

2018-04-01

The current study tested whether people with and without eating disorders (EDs) varied in their implicit attitudes toward ED-relevant stimuli. Additionally, the study tested whether implicit evaluations of ED-relevant stimuli predicted ED symptoms and behaviors over a 4-week interval. Participants were people without EDs (N = 85) and people seeking treatment for EDs (N = 92). All participants completed self-report questionnaires and a version of the affect misattribution procedure (AMP) at baseline. The AMP indexed implicit evaluations of average body stimuli, eating stimuli, and ED-symptom stimuli. Participants with EDs completed weekly follow-up measures of ED symptoms and behaviors for 4 weeks. Contrary to predictions, the anorexia nervosa (AN) group did not differ from the no ED group on implicit attitudes toward ED-symptom stimuli, and the bulimia nervosa (BN) group had less positive implicit attitudes toward ED-symptom stimuli relative to the no ED group. In line with predictions, people with AN and BN had more negative implicit attitudes toward average body and eating stimuli relative to the no ED group. In addition, among the ED group more negative implicit attitudes toward eating stimuli predicted ED symptoms and behaviors 4 weeks later, over and above baseline ED symptoms and behaviors. Taken together, implicit evaluations of eating stimuli differentiated people with AN and BN from people without EDs and longitudinally predicted ED symptoms and behaviors. Interventions that increase implicit liking of eating-related stimuli may reduce ED behaviors. © 2018 Wiley Periodicals, Inc.

Disrupted rapid eye movement sleep predicts poor declarative memory performance in post-traumatic stress disorder.

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Lipinska, Malgorzata; Timol, Ridwana; Kaminer, Debra; Thomas, Kevin G F

2014-06-01

Successful memory consolidation during sleep depends on healthy slow-wave and rapid eye movement sleep, and on successful transition across sleep stages. In post-traumatic stress disorder, sleep is disrupted and memory is impaired, but relations between these two variables in the psychiatric condition remain unexplored. We examined whether disrupted sleep, and consequent disrupted memory consolidation, is a mechanism underlying declarative memory deficits in post-traumatic stress disorder. We recruited three matched groups of participants: post-traumatic stress disorder (n = 16); trauma-exposed non-post-traumatic stress disorder (n = 15); and healthy control (n = 14). They completed memory tasks before and after 8 h of sleep. We measured sleep variables using sleep-adapted electroencephalography. Post-traumatic stress disorder-diagnosed participants experienced significantly less sleep efficiency and rapid eye movement sleep percentage, and experienced more awakenings and wake percentage in the second half of the night than did participants in the other two groups. After sleep, post-traumatic stress disorder-diagnosed participants retained significantly less information on a declarative memory task than controls. Rapid eye movement percentage, wake percentage and sleep efficiency correlated with retention of information over the night. Furthermore, lower rapid eye movement percentage predicted poorer retention in post-traumatic stress disorder-diagnosed individuals. Our results suggest that declarative memory consolidation is disrupted during sleep in post-traumatic stress disorder. These data are consistent with theories suggesting that sleep benefits memory consolidation via predictable neurobiological mechanisms, and that rapid eye movement disruption is more than a symptom of post-traumatic stress disorder. © 2014 European Sleep Research Society.

Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study.

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Johnston, Blair A; Coghill, David; Matthews, Keith; Steele, J Douglas

2015-01-01

Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH. © The Author(s) 2014.

Predictive and associated factors of psychiatric disorders after traumatic brain injury: a prospective study.

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Gould, Kate Rachel; Ponsford, Jennie Louise; Johnston, Lisa; Schönberger, Michael

2011-07-01

Psychiatric disorders are common and often debilitating following traumatic brain injury (TBI). However, there is little consensus within the literature regarding the risk factors for post-injury psychiatric disorders. A 1-year prospective study was conducted to examine which pre-injury, injury-related, and concurrent factors were associated with experiencing a psychiatric disorder, diagnosed using the Structured Clinical Interview for DSM-IV-TR Axis I Disorders, at 1 year post-injury. Participants were 122 adults with TBI and 88 proxy informants. Psychiatric disorders were common both pre-injury (54.1%) and at 12 months post-injury (45.9%). Results of regression analyses indicated individuals without a pre-injury psychiatric disorder or psychiatric symptomatology in the acute post-injury period were less likely to have a psychiatric disorder at 12 months post-injury. These findings confirm the importance of pre-injury history for the prediction of post-injury psychiatric disorders. Limb injury also emerged as a useful early indicator of later psychiatric disorder. Post-injury psychiatric disorders were associated with concurrent unemployment, pain, poor quality of life, and use of unproductive coping skills. The clinical implications of these findings are discussed.

Comparative Analysis of Predictive Models of Pain Level from Work-Related Musculoskeletal Disorders among Sewing Machine Operators in the Garments Industry

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Carlos Ignacio P. Luga

2017-02-01

Full Text Available The Philippine garments industry has been experiencing a roller-coaster ride during the past decades, with much competition from its Asian neighbors, especially in the wake of the ASEAN 2015 Integration. One of the areas in the industry which can be looked into and possibly improved is the concern on Work-related Musculoskeletal Disorders (WMSDs. Literatures have shown that pain from WMSDs among sewing machine operators in this industry is very prevalent and its effects on the same operators have been very costly. After identifying the risk factors which may cause pain from WMSDs, this study generated three models which would predict the said pain level. These models were analyzed, compared and the best model was identified to make the most accurate prediction of pain level. This predictive model would be helpful for management of garment firms since first, the risk factors have been identified and hence can be used as bases for proposed improvements. Second, the prediction of each operator’s pain level would allow management to assess better its employees in terms of their sewing capacity vis-à-vis the company’s production plans.

Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

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Yavuz, Ahmet Sinan; Sezerman, Osman Ugur

2014-01-01

Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

Predicting the duration of sickness absence for patients with common mental disorders in occupational health care.

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Nieuwenhuijsen, Karen; Verbeek, Jos H A M; de Boer, Angela G E M; Blonk, Roland W B; van Dijk, Frank J H

2006-02-01

This study attempted to determine the factors that best predict the duration of absence from work among employees with common mental disorders. A cohort of 188 employees, of whom 102 were teachers, on sick leave with common mental disorders was followed for 1 year. Only information potentially available to the occupational physician during a first consultation was included in the predictive model. The predictive power of the variables was tested using Cox's regression analysis with a stepwise backward selection procedure. The hazard ratios (HR) from the final model were used to deduce a simple prediction rule. The resulting prognostic scores were then used to predict the probability of not returning to work after 3, 6, and 12 months. Calculating the area under the curve from the ROC (receiver operating characteristic) curve tested the discriminative ability of the prediction rule. The final Cox's regression model produced the following four predictors of a longer time until return to work: age older than 50 years [HR 0.5, 95% confidence interval (95% CI) 0.3-0.8], expectation of duration absence longer than 3 months (HR 0.5, 95% CI 0.3-0.8), higher educational level (HR 0.5, 95% CI 0.3-0.8), and diagnosis depression or anxiety disorder (HR 0.7, 95% CI 0.4-0.9). The resulting prognostic score yielded areas under the curves ranging from 0.68 to 0.73, which represent acceptable discrimination of the rule. A prediction rule based on four simple variables can be used by occupational physicians to identify unfavorable cases and to predict the duration of sickness absence.

Predicting the duration of sickness absence for patients with common mental disorders in occupational health care

NARCIS (Netherlands)

Nieuwenhuijsen, K.; Verbeek, J.H.A.M.; Boer, A.G.E.M. de; Blonk, R.W.B.; Dijk, F.J.H. van

2006-01-01

Objectives: This study attempted to determine the factors that best predict the duration of absence from work among employees with common mental disorders. Methods: A cohort of 188 employees, of whom 102 were teachers, on sick leave with common mental disorders was followed for 1 year. Only

Predicting the duration of sickness absence for patients with common mental disorders in occupational health care

NARCIS (Netherlands)

Nieuwenhuijsen, Karen; Verbeek, Jos H. A. M.; de Boer, Angela G. E. M.; Blonk, Roland W. B.; van Dijk, Frank J. H.

2006-01-01

OBJECTIVES: This study attempted to determine the factors that best predict the duration of absence from work among employees with common mental disorders. METHODS: A cohort of 188 employees, of whom 102 were teachers, on sick leave with common mental disorders was followed for 1 year. Only

The prediction of the incidence rate of upper limb musculoskeletal disorders, with CTD risk index method on potters of Meybod city

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Reza Khani Jazani

2012-02-01

Full Text Available Background: The objective of this study was to predict the incidence of musculoskeletal disorders in potters of Meybod city by performing CTD risk index method.Materials and Method: This is a descriptive cross-sectional study. Target society was all workers in pottery workshops which were located in the Meybod. Information related to musculoskeletal disorders was obtained by the Nordic questionnaire and we used CTD risk index method to predict the incidence of musculoskeletal disorders.Results: We observed in this study that 59.3% of the potters had symptoms of musculoskeletal disorders in at least in one of their upper extremities. Also significant differences between mean CTD risk index on potters with and without symptoms of the upper limb musculoskeletal disorders, respectively (p=0.038.Conclusion: CTD risk index method can be as a suitable method for predicting the incidence of musculoskeletal disorders used in the potters

Predicting Personality Disorder Functioning Styles by the Five-Factor Nonverbal Personality Questionnaire in Healthy Volunteers and Personality Disorder Patients.

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Gao, Qianqian; Ma, Guorong; Zhu, Qisha; Fan, Hongying; Wang, Wei

2016-01-01

Detecting personality disorders in the illiterate population is a challenge, but nonverbal tools measuring personality traits such as the Five-Factor Nonverbal Personality Questionnaire (FFNPQ) might help. We hypothesized that FFNPQ traits are associated with personality disorder functioning styles in a predictable way, especially in a sample of personality disorder patients. We therefore invited 106 personality disorder patients and 205 healthy volunteers to answer the FFNPQ and the Parker Personality Measure (PERM) which measures 11 personality disorder functioning styles. Patients scored significantly higher on the FFNPQ neuroticism and conscientiousness traits and all 11 PERM styles. In both groups, the 5 FFNPQ traits displayed extensive associations with the 11 PERM styles, respectively, and the associations were more specific in patients. Associations between neuroticism, extraversion and agreeableness traits and most PERM styles were less exclusive, but conscientiousness was associated with antisocial (-) and obsessive-compulsive styles, and openness to experience with schizotypal and dependent (-) styles. Our study has demonstrated correlations between FFNPQ traits and PERM styles, and implies the nonverbal measure of personality traits is capable of aiding the diagnoses of personality disorders in the illiterate population. Enlarging sample size and including the illiterate might make for more stable results. © 2016 S. Karger AG, Basel.

Sudarshan Kriya Yoga improves cardiac autonomic control in patients with anxiety-depression disorders.

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Toschi-Dias, Edgar; Tobaldini, Eleonora; Solbiati, Monica; Costantino, Giorgio; Sanlorenzo, Roberto; Doria, Stefania; Irtelli, Floriana; Mencacci, Claudio; Montano, Nicola

2017-05-01

Several studies have demonstrated that adjuvant therapies as exercise and breathing training are effective in improving cardiac autonomic control (CAC) in patients with affective spectrum disorders. However, the effects of Sudarshan Kriya Yoga (SKY) on autonomic function in this population is unknown. Our objective was to test the hypothesis that SKY training improves CAC and cardiorespiratory coupling in patients with anxiety and/or depression disorders. Forty-six patients with a diagnosis of anxiety and/or depression disorders (DSM-IV) were consecutively enrolled and divided in two groups: 1) conventional therapy (Control) and 2) conventional therapy associated with SKY (Treatment) for 15 days. Anxiety and depression levels were determined using quantitative questionnaires. For the assessment of CAC and cardiorespiratory coupling, cardiorespiratory traces were analyzed using monovariate and bivariate autoregressive spectral analysis, respectively. After 15-days, we observed a reduction of anxiety and depression levels only in Treatment group. Moreover, sympathetic modulation and CAC were significantly lower while parasympathetic modulation and cardiorespiratory coupling were significantly higher in the Treatment compared to Control group. Intensive breathing training using SKY approach improves anxiety and/or depressive disorders as well as CAC and cardiorespiratory coupling. These finding suggest that the SKY training may be a useful non-pharmacological intervention to improve symptoms and reduce cardiovascular risk in patients with anxiety/depression disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

Development and validation of a prediction algorithm for the onset of common mental disorders in a working population.

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Fernandez, Ana; Salvador-Carulla, Luis; Choi, Isabella; Calvo, Rafael; Harvey, Samuel B; Glozier, Nicholas

2018-01-01

Common mental disorders are the most common reason for long-term sickness absence in most developed countries. Prediction algorithms for the onset of common mental disorders may help target indicated work-based prevention interventions. We aimed to develop and validate a risk algorithm to predict the onset of common mental disorders at 12 months in a working population. We conducted a secondary analysis of the Household, Income and Labour Dynamics in Australia Survey, a longitudinal, nationally representative household panel in Australia. Data from the 6189 working participants who did not meet the criteria for a common mental disorders at baseline were non-randomly split into training and validation databases, based on state of residence. Common mental disorders were assessed with the mental component score of 36-Item Short Form Health Survey questionnaire (score ⩽45). Risk algorithms were constructed following recommendations made by the Transparent Reporting of a multivariable prediction model for Prevention Or Diagnosis statement. Different risk factors were identified among women and men for the final risk algorithms. In the training data, the model for women had a C-index of 0.73 and effect size (Hedges' g) of 0.91. In men, the C-index was 0.76 and the effect size was 1.06. In the validation data, the C-index was 0.66 for women and 0.73 for men, with positive predictive values of 0.28 and 0.26, respectively Conclusion: It is possible to develop an algorithm with good discrimination for the onset identifying overall and modifiable risks of common mental disorders among working men. Such models have the potential to change the way that prevention of common mental disorders at the workplace is conducted, but different models may be required for women.

Sex differences in the prediction of the effectiveness of paroxetine for patients with major depressive disorder identified using a receiver operating characteristic curve analysis for early response.

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Tomita, Tetsu; Yasui-Furukori, Norio; Norio, Yasui-Furukori; Sato, Yasushi; Nakagami, Taku; Tsuchimine, Shoko; Kaneda, Ayako; Kaneko, Sunao

2014-01-01

We investigated cutoff values for the early response of patients with major depressive disorder to paroxetine and their sex differences by using a receiver operating characteristic (ROC) curve analysis to predict the effectiveness of paroxetine. In total, 120 patients with major depressive disorder were enrolled and treated with 10-40 mg/day paroxetine for 6 weeks; 89 patients completed the protocol. A clinical evaluation using the Montgomery-Asberg Depression Rating Scale (MADRS) was performed at weeks 0, 1, 2, 4, and 6. In male subjects, the cutoff values for MADRS improvement rating in week 1, week 2, and week 4 were 20.9%, 34.9%, and 33.3%, respectively. The sensitivities and the specificities were 83.3% and 80.0%, 83.3% and 80.0%, and 100% and 90%, respectively. The areas under the curve (AUC) were 0.908, 0.821, and 0.979, respectively. In female subjects, the cutoff values for the MADRS improvement rating in week 1, week 2, and week 4 were 21.4%, 35.7%, and 32.3%, respectively. The sensitivities and the specificities were 71.4% and 84.6%, 73.8% and 76.9%, and 90.5% and 76.9%, respectively. The AUCs were 0.781, 0.735, and 0.904, respectively. Early improvement with paroxetine may predict the long-term response. The accuracy of the prediction for the response is higher in male subjects.

Predicting borderline personality disorder symptoms in adolescents from childhood physical and relational aggression, depression, and attention-deficit/hyperactivity disorder.

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Vaillancourt, Tracy; Brittain, Heather L; McDougall, Patricia; Krygsman, Amanda; Boylan, Khrista; Duku, Eric; Hymel, Shelley

2014-08-01

Developmental cascade models linking childhood physical and relational aggression with symptoms of depression and attention-deficit/hyperactivity disorder (ADHD; assessed at ages 10, 11, 12, 13, and 14) to borderline personality disorder (BPD) features (assessed at age 14) were examined in a community sample of 484 youth. Results indicated that, when controlling for within-time covariance and across-time stability in the examination of cross-lagged relations among study variables, BPD features at age 14 were predicted by childhood relational aggression and symptoms of depression for boys, and physical and relational aggression, symptoms of depression, and symptoms of ADHD for girls. Moreover, for boys BPD features were predicted from age 10 ADHD through age 12 depression, whereas for girls the pathway to elevated BPD features at age 14 was from depression at age 10 through physical aggression symptoms at age 12. Controlling for earlier associations among variables, we found that for girls the strongest predictor of BPD features at age 14 was physical aggression, whereas for boys all the risk indicators shared a similar predictive impact. This study adds to the growing literature showing that physical and relational aggression ought to be considered when examining early precursors of BPD features.

Major depressive disorder subtypes to predict long-term course

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van Loo, Hanna M.; Cai, Tianxi; Gruber, Michael J.; Li, Junlong; de Jonge, Peter; Petukhova, Maria; Rose, Sherri; Sampson, Nancy A.; Schoevers, Robert A.; Wardenaar, Klaas J.; Wilcox, Marsha A.; Al-Hamzawi, Ali Obaid; Andrade, Laura Helena; Bromet, Evelyn J.; Bunting, Brendan; Fayyad, John; Florescu, Silvia E.; Gureje, Oye; Hu, Chiyi; Huang, Yueqin; Levinson, Daphna; Medina-Mora, Maria Elena; Nakane, Yoshibumi; Posada-Villa, Jose; Scott, Kate M.; Xavier, Miguel; Zarkov, Zahari; Kessler, Ronald C.

2016-01-01

Background Variation in course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. Methods Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs) followed by k-means cluster analysis, are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) Surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode; number of with an episode lasting most of the year) and severity (hospitalization for MDD; disability due to MDD). Results Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity and was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. Conclusions Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data. PMID:24425049

Improving orbit prediction accuracy through supervised machine learning

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Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

Combining gene prediction methods to improve metagenomic gene annotation

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Rosen Gail L

2011-01-01

Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

Incremental validity of positive and negative valence in predicting personality disorder.

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Simms, Leonard J; Yufik, Tom; Gros, Daniel F

2010-04-01

The Big Seven model of personality includes five dimensions similar to the Big Five model as well as two evaluative dimensions—Positive Valence (PV) and Negative Valence (NV)—which reflect extremely positive and negative person descriptors, respectively. Recent theory and research have suggested that PV and NV predict significant variance in personality disorder (PD) above that predicted by the Big Five, but firm conclusions have not been possible because previous studies have been limited to only single measures of PV, NV, and the Big Five traits. In the present study, we replicated and extended previous findings using three markers of all key constructs—including PV, NV, and the Big Five—in a diverse sample of 338 undergraduates. Results of hierarchical multiple regression analyses revealed that PV incrementally predicted Narcissistic and Histrionic PDs above the Big Five and that NV nonspecifically incremented the prediction of most PDs. Implications for dimensional models of personality pathology are discussed. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Increasing body image flexibility in a residential eating disorder facility: Correlates with symptom improvement.

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Lee, Eric B; Ong, Clarissa W; Twohig, Michael P; Lensegrav-Benson, Tera; Quakenbush-Roberts, Benita

2018-01-01

The purpose of this study was to examine the effects of changes in body image psychological flexibility over the course of treatment on various outcome variables. Participants included 103 female, residential patients diagnosed with an eating disorder. Pretreatment and posttreatment data were collected that examined body image psychological flexibility, general psychological flexibility, symptom severity, and other outcome variables. Changes in body image psychological flexibility significantly predicted changes in all outcome measures except for obsessive-compulsive symptoms after controlling for body mass index, depression, and anxiety. Additionally, these results were maintained after controlling for general psychological flexibility, contributing to the incremental validity of the BI-AAQ. This study suggests that changes in body image psychological flexibility meaningfully predict changes in various treatment outcomes of interest, including eating disorder risk, quality of life, and general mental health. Findings indicate that body image psychological flexibility might be a viable target for eating disorder treatment.

The Role of Socio-Physical Anxiety, Body Image, and Self Esteem in Prediction of the Eating Disorder in Sportswomen

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Aidin Valizade

2012-03-01

Full Text Available Background: Socio-physical anxiety, body image, and self esteem are variables that play an important role on eating disorders. The purpose of this research was the role of socio-physical anxiety, body image and self esteem in prediction of the eating disorders in sportswomen.Materials and Method: 181 of aerobic and physical readiness sportswomen were selected by clustered sampling method and filled the questionnaire containing eating disorder, socio-physical anxiety, body image concern and self esteem scales. Results: According to this research, there was meaningful correlation between social physical anxiety (r=-0.326, body image concern (r=0.466 and self-esteem (r=0.349 with eating disorders and these variables were explained the 0.27 variance in eating disorders. Conclusion: Results are corresponding with previous studies and have important implications in attention to the predicting variables of eating disorders in athletes’ women

Evaluating the Impact of Naltrexone on the Rat Gambling Task to Test Its Predictive Validity for Gambling Disorder.

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Patricia Di Ciano

Full Text Available Gambling Disorder has serious consequences and no medications are currently approved for the treatment of this disorder. One factor that may make medication development difficult is the lack of animal models of gambling that would allow for the pre-clinical screening of efficacy. Despite this, there is evidence from clinical trials that opiate antagonists, in particular naltrexone, may be useful in treating gambling disorder. To-date, the effects of naltrexone on pre-clinical models of gambling have not been evaluated. The purpose of the present study was to evaluate the effects of naltrexone in an animal model of gambling, the rat gambling task (rGT, to determine whether this model has some predictive validity. The rGT is a model in which rats are given a choice of making either a response that produces a large reward or a small reward. The larger the reward, the greater the punishment, and thus this task requires that the animal inhibit the 'tempting' choice, as the smaller reward option produces overall the most number of rewards per session. People with gambling disorder chose the tempting option more, thus the rGT may provide a model of problem gambling. It was found that naltrexone improved performance on this task in a subset of animals that chose the 'tempting', disadvantageous choice, more at baseline. Thus, the results of this study suggest that the rGT should be further investigated as a pre-clinical model of gambling disorder and that further investigation into whether opioid antagonists are effective in treating Gambling Disorder may be warranted.

Improved Wind Speed Prediction Using Empirical Mode Decomposition

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ZHANG, Y.

2018-05-01

Full Text Available Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD, the Radial Basis Function Neural Network (RBF and the Least Square Support Vector Machine (SVM, an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM, the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.

Does mindfulness meditation improve attention in attention deficit hyperactivity disorder?

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Modesto-Lowe, Vania; Farahmand, Pantea; Chaplin, Margaret; Sarro, Lauren

2015-12-22

Attention deficit hyperactivity disorder (ADHD) manifests by high levels of inattention, impulsiveness and hyperactivity. ADHD starts in childhood and results in impairments that continue into adulthood. While hyperactivity declines over time, inattention and executive function difficulties persist, leading to functional deficits. Adolescents and adults with ADHD have pervasive impairment in interpersonal and family relationships. They may develop addiction, delinquent behavior and comorbid psychiatric disorders. Despite advances in diagnosis and treatment, persistent residual symptoms are common, highlighting the need for novel treatment strategies. Mindfulness training, derived from Eastern meditation practices, may improve self-regulation of attention. It may also be a useful strategy to augment standard ADHD treatments and may be used as a potential tool to reduce impairments in patients with residual symptoms of ADHD. Clinically, this would manifest by an increased ability to suppress task-unrelated thoughts and distractions resulting in improved attention, completion of tasks and potential improvement in occupational and social function.

Improvement of gas entrainment prediction method. Introduction of surface tension effect

International Nuclear Information System (INIS)

Ito, Kei; Sakai, Takaaki; Ohshima, Hiroyuki; Uchibori, Akihiro; Eguchi, Yuzuru; Monji, Hideaki; Xu, Yongze

2010-01-01

A gas entrainment (GE) prediction method has been developed to establish design criteria for the large-scale sodium-cooled fast reactor (JSFR) systems. The prototype of the GE prediction method was already confirmed to give reasonable gas core lengths by simple calculation procedures. However, for simplification, the surface tension effects were neglected. In this paper, the evaluation accuracy of gas core lengths is improved by introducing the surface tension effects into the prototype GE prediction method. First, the mechanical balance between gravitational, centrifugal, and surface tension forces is considered. Then, the shape of a gas core tip is approximated by a quadratic function. Finally, using the approximated gas core shape, the authors determine the gas core length satisfying the mechanical balance. This improved GE prediction method is validated by analyzing the gas core lengths observed in simple experiments. Results show that the analytical gas core lengths calculated by the improved GE prediction method become shorter in comparison to the prototype GE prediction method, and are in good agreement with the experimental data. In addition, the experimental data under different temperature and surfactant concentration conditions are reproduced by the improved GE prediction method. (author)

The Role of Thought Suppression, Meta-Cognitive Factors and Negative Emotions in Prediction of Substance Dependency Disorder

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Omid Saed

2011-08-01

Full Text Available Introduction: This study investigated the role of thought suppression, meta- cognitive factors, and negative emotions in predicting of substance dependency disorder. Method: Subjects were 70 patients with substance dependence disorder and 70 normal individuals (total 140. Substance dependants were selected of outpatient treatment centers and the normal sample was selected of the general population too. Sampling methods in both samples were convenience sampling. All people were assessed by MCQ-30, White Bear Suppression Inventory, and Beck’s Anxiety and Depression Questionnaires. For data analysis, discriminant analysis were used. Results: Negative meta-cognitive beliefs about worry, depression, and thought suppression were the most significant predictors of substance dependence disorder. Conclusion: Through meta-cognitive beliefs, thought suppression and negative emotion (especially depression, substance dependency disorder can be predicted. Based on this model can be used to take a substance dependency disorder prevention approach and psychotherapy approach (based on cognitive and meta-cognitive therapies. In addition, the findings of this research can be applied in clinical and counseling environments to help substance dependant clients.

Randomized controlled trial of a mobile phone intervention for improving adherence to naltrexone for alcohol use disorders.

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Susan A Stoner

Full Text Available Naltrexone is a front-line treatment for alcohol use disorders, but its efficacy is limited by poor medication adherence. This randomized controlled trial evaluated whether a mobile health intervention could improve naltrexone adherence.Treatment-seeking participants with an alcohol use disorder (N = 76 were randomized to intervention and control conditions. All participants received naltrexone (50 mg/day with a medication event monitoring system (MEMS and a prepaid smartphone, and received a daily text message querying medication side effects, alcohol use, and craving. Those in the intervention arm received additional medication reminders and adherence assessment via text message.The primary outcome, proportion of participants with adequate adherence (defined as ≥80% of prescribed doses taken through Week 8, did not differ between groups in intent-to-treat analyses (p = .34. Mean adherence at study midpoint (Week 4 was 83% in the intervention condition and 77% in the control condition (p = .35. Survival analysis found that the intervention group sustained adequate adherence significantly longer (M = 19 days [95% CI = 0.0-44.0] than those in the control group (M = 3 days [95% CI = 0.0-8.1] during the first month of treatment (p = .04. Medication adherence did not predict drinking outcomes.These results suggest that in the context of daily monitoring and assessment via cell phone, additional text message reminders do not further improve medication adherence. Although this initial trial does not provide support for the efficacy of text messaging to improve adherence to pharmacotherapy for alcohol use disorders, additional trials with larger samples and alternate designs are warranted.ClinicalTrials.gov: NCT01349985.

Narrative therapy for adults with major depressive disorder: improved symptom and interpersonal outcomes.

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Vromans, Lynette P; Schweitzer, Robert D

2011-01-01

This study investigated depressive symptom and interpersonal relatedness outcomes from eight sessions of manualized narrative therapy for 47 adults with major depressive disorder. Post-therapy, depressive symptom improvement (d=1.36) and proportions of clients achieving reliable improvement (74%), movement to the functional population (61%), and clinically significant improvement (53%) were comparable to benchmark research outcomes. Post-therapy interpersonal relatedness improvement (d=.62) was less substantial than for symptoms. Three-month follow-up found maintenance of symptom, but not interpersonal gains. Benchmarking and clinical significance analyses mitigated repeated measure design limitations, providing empirical evidence to support narrative therapy for adults with major depressive disorder.

Fear of food prospectively predicts drive for thinness in an eating disorder sample recently discharged from intensive treatment.

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Levinson, Cheri A; Brosof, Leigh C; Ma, Jackie; Fewell, Laura; Lenze, Eric J

2017-12-01

Fears of food are common in individuals with eating disorders and contribute to the high relapse rates. However, it is unknown how fears of food contribute to eating disorder symptoms across time, potentially contributing to an increased likelihood of relapse. Participants diagnosed with an eating disorder (N=168) who had recently completed intensive treatment were assessed after discharge and one month later regarding fear of food, eating disorder symptoms, anxiety sensitivity, and negative affect. Cross lagged path analysis was utilized to determine if fear of food predicted subsequent eating disorder symptoms one month later. Fear of food-specifically, anxiety about eating and feared concerns about eating-predicted drive for thinness, a core symptom domain of eating disorders. These relationships held while accounting for anxiety sensitivity and negative affect. There is a specific, direct relationship between anxiety about eating and feared concerns about eating and drive for thinness. Future research should test if interventions designed to target fear of food can decrease drive for thinness and thereby prevent relapse. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Relationship between Symptom Relief and Psychosocial Functional Improvement during Acute Electroconvulsive Therapy for Patients with Major Depressive Disorder.

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Lin, Ching-Hua; Yang, Wei-Cheng

2017-07-01

We aimed to compare the degree of symptom relief to psychosocial functional (abbreviated as "functional") improvement and explore the relationships between symptom relief and functional improvement during acute electroconvulsive therapy for patients with major depressive disorder. Major depressive disorder inpatients (n=130) requiring electroconvulsive therapy were recruited. Electroconvulsive therapy was generally performed for a maximum of 12 treatments. Symptom severity, using the 17-item Hamilton Depression Rating Scale, and psychosocial functioning (abbreviated as "functioning"), using the Modified Work and Social Adjustment Scale, were assessed before electroconvulsive therapy, after every 3 electroconvulsive therapy treatments, and after the final electroconvulsive therapy. Both 17-item Hamilton Depression Rating Scale and Modified Work and Social Adjustment Scale scores were converted to T-score units to compare the degrees of changes between depressive symptoms and functioning after electroconvulsive therapy. Structural equation modeling was used to test the relationships between 17-item Hamilton Depression Rating Scale and Modified Work and Social Adjustment Scale during acute electroconvulsive therapy. One hundred sixteen patients who completed at least the first 3 electroconvulsive therapy treatments entered the analysis. Reduction of 17-item Hamilton Depression Rating Scale T-scores was significantly greater than that of Modified Work and Social Adjustment Scale T-scores at assessments 2, 3, 4, and 5. The model analyzed by structural equation modeling satisfied all indices of goodness-of-fit (chi-square = 32.882, P =.107, TLI = 0.92, CFI = 0.984, RMSEA = 0.057). The 17-item Hamilton Depression Rating Scale change did not predict subsequent Modified Work and Social Adjustment Scale change. Functioning improved less than depressive symptoms during acute electroconvulsive therapy. Symptom reduction did not predict subsequent functional improvement

Reduced Predictable Information in Brain Signals in Autism Spectrum Disorder

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Carlos eGomez

2014-02-01

Full Text Available Autism spectrum disorder (ASD is a common developmental disorder characterized by communication difficulties and impaired social interaction. Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD. Here, we aim to describe potential information-processing consequences of these alterations by measuring active information storage (AIS – a key quantity in the theory of distributed computation in biological networks. AIS is defined as the mutual information between the semi-infinite past of a process and its next state. It measures the amount of stored information that is used for computation of the next time step of a process. AIS is high for rich but predictable dynamics. We recorded magnetoencephalography (MEG signals in 13 ASD patients and 14 matched control subjects in a visual task. After a beamformer source analysis, twelve task-relevant sources were obtained. For these sources, stationary baseline activity was analyzed using AIS. Our results showed a decrease of AIS values in the hippocampus of ASD patients in comparison with controls, meaning that brain signals in ASD were either less predictable, reduced in their dynamic richness or both. Our study suggests the usefulness of AIS to detect an abnormal type of dynamics in ASD. The observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.

Plant water potential improves prediction of empirical stomatal models.

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William R L Anderegg

Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

The impacts of migraine and anxiety disorders on painful physical symptoms among patients with major depressive disorder.

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Hung, Ching-I; Liu, Chia-Yih; Chen, Ching-Yen; Yang, Ching-Hui; Wang, Shuu-Jiun

2014-11-10

No study has simultaneously investigated the impacts of migraine and anxiety disorders on painful physical symptoms (PPS) among patients with major depressive disorder (MDD). The study aimed to investigate this issue. This open-label study enrolled 155 outpatients with MDD, who were then treated with venlafaxine 75 mg per day for four weeks. Eighty-five participants with good compliance completed the treatment. Migraine was diagnosed according to the International Classification of Headache Disorders. MDD and anxiety disorders were diagnosed using the Structured Clinical Interview for DSM-IV-TR. The visual analog scale (VAS) was used to evaluate the severity of eight PPS. Multiple linear and logistic regressions were used to investigate the impacts of migraine and anxiety disorders on PPS. Compared with patients without migraine, patients with migraine had a greater severity of PPS at baseline and post-treatment. After controlling for demographic variables and depressive severity, migraine independently predicted the intensities of eight PPS at baseline and four PPS post-treatment. Moreover, migraine independently predicted poorer treatment responses of chest pain and full remission of pains in the head, chest, neck and/or shoulder. Anxiety disorders predicted less full remission of pains in the abdomen and limbs. Migraine and anxiety disorders have negative impacts on PPS among patients with MDD. Integrating the treatment of migraine and anxiety disorders into the management of depression might help to improve PPS and the prognosis of MDD.

Does enhanced cognitive behaviour therapy for eating disorders improve quality of life?

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Watson, Hunna J; Allen, Karina; Fursland, Anthea; Byrne, Susan M; Nathan, Paula R

2012-09-01

Quality of life (QOL) is the degree of enjoyment and satisfaction experienced in life, and embraces emotional well-being, physical health, economic and living circumstances, and work satisfaction. QOL recovery with eating disorder treatment has received sparse attention, and until now, no study has investigated QOL recovery with enhanced cognitive behaviour therapy (CBT-E). Patients (n = 196) admitted to a specialist eating disorders outpatient programme and receiving CBT-E completed measures of QOL, eating disorder psychopathology, depression, anxiety and self-esteem, before and after treatment. QOL at intake was compared with community norms, and QOL below the norm was predicted from sociodemographic and clinical correlates with logistic regression. Baseline QOL below the norm was associated with depression and anxiety Axis I comorbidity, and severity of depressive symptoms. Predictors of post-treatment QOL were baseline QOL and level of depressive symptoms and self-esteem at post-treatment. CBT-E was associated with gains in QOL over the course of treatment, in addition to eating disorder symptom relief. Copyright © 2012 John Wiley & Sons, Ltd and Eating Disorders Association.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients. PMID:28152063

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

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Mumtaz, Wajid; Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad; Malik, Aamir Saeed

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant's treatment outcome for the MDD patients.

Improving recognition of late life anxiety disorders in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition: observations and recommendations of the Advisory Committee to the Lifespan Disorders Work Group

NARCIS (Netherlands)

Mohlman, J.; Bryant, C.; Lenze, E.J.; Stanley, M.A.; Gum, A.; Flint, A.; Beekman, A.T.F.; Wetherell, J.L.; Thorp, S.R.; Craske, MG

2012-01-01

Background Recognition of the significance of anxiety disorders in older adults is growing. The revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM) provides a timely opportunity to consider potential improvements to diagnostic criteria for psychiatric disorders for use with

OPAL: prediction of MoRF regions in intrinsically disordered protein sequences.

Science.gov (United States)

Sharma, Ronesh; Raicar, Gaurav; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

2018-06-01

Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues. OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/. ashwini@hgc.jp or alok.sharma@griffith.edu.au. Supplementary data are available at Bioinformatics online.

Predictive value of work-related self-efficacy change on RTW for employees with common mental disorders.

Science.gov (United States)

Lagerveld, Suzanne E; Brenninkmeijer, Veerle; Blonk, Roland W B; Twisk, Jos; Schaufeli, Wilmar B

2017-05-01

To improve interventions that aim to promote return to work (RTW) of workers with common mental disorders (CMD), insight into modifiable predictors of RTW is needed. This study tested the predictive value of self-efficacy change for RTW in addition to preintervention levels of self-efficacy. RTW self-efficacy was measured 5 times within 9 months among 168 clients of a mental healthcare organisation who were on sick leave due to CMD. Self-efficacy parameters were modelled with multilevel analyses and added as predictors into a Cox regression analysis. Results showed that both high baseline self-efficacy and self-efficacy increase until full RTW were predictive of a shorter duration until full RTW. Both self-efficacy parameters remained significant predictors of RTW when controlled for several relevant covariates and within subgroups of employees with either high or low preintervention self-efficacy levels. This is the first study that demonstrated the prognostic value of self-efficacy change, over and above the influence of psychological symptoms, for RTW among employees with CMD. By showing that RTW self-efficacy increase predicted a shorter duration until full RTW, this study points to the relevance of enhancing RTW self-efficacy in occupational or mental health interventions for employees with CMD. Efforts to improve self-efficacy appear valuable both for people with relatively low and high baseline self-efficacy. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Abnormal infant neurodevelopment predicts schizophrenia spectrum disorders.

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Fish, Barbara; Kendler, Kenneth S

2005-06-01

The aim of this study was to detect infants who carry a schizophrenic genotype and study the development of schizophrenia spectrum disorders (SZSD) from birth. In the 1940s, Bender described uneven maturation in childhood schizophrenics and in 1952 found this in the infant histories of 6 schizophrenic children. We tested a possible index for defective neural integration in infants termed "pandysmaturation" (PDM). This required retarded cranial growth plus retarded and erratic gross motor development on a single exam. Twelve offspring of hospitalized schizophrenic mothers and 12 infants in a "Well Baby Clinic," were examined 10 times between birth and 2 years of age. Psychiatric interviews and psychological testing were done at 10, 15, and 22 years of age, plus follow-up at 27-35 years of age. Six infants had PDM at 2, 6, or 13 months of age. Five individuals have been blindly diagnosed (by KSK) as having lifetime SZSD; all 5 had PDM before 8 months. Chi-square one-tailed tests confirmed the predictions: (1) PDM was related to subsequent SZSD (chi(2) = 11.43; p < 0.0005); (2) schizophrenic mothers had more infants with PDM than nonschizophrenic mothers (chi(2) = 3.28; p < 0.05); and (3) schizophrenic mothers had more SZSD offspring than nonschizophrenic mothers (chi(2) = 6.39; p < 0.0125). These first behavioral observations of aberrant neurodevelopment in pre- SZSD infants support the evidence of early neurodevelopmental disorder seen in studies of brain pathology in SZSD adults.

Impact of dissociation on treatment of depressive and anxiety spectrum disorders with and without personality disorders.

Science.gov (United States)

Prasko, Jan; Grambal, Ales; Kasalova, Petra; Kamardova, Dana; Ociskova, Marie; Holubova, Michaela; Vrbova, Kristyna; Sigmundova, Zuzana; Latalova, Klara; Slepecky, Milos; Zatkova, Marta

2016-01-01

degree of dissociation at the beginning of the treatment predicted minor improvement, and also, higher therapeutic change was connected to greater reduction of the dissociation level. Dissociation is an important factor that influences the treatment effectiveness in anxiety/depression patients with or without personality disorders resistant to previous treatment. Targeting dissociation in the treatment of these disorders may be beneficial.

Anticipated Benefits of Care (ABC): psychometrics and predictive value in psychiatric disorders.

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Warden, D; Trivedi, M H; Carmody, T J; Gollan, J K; Kashner, T M; Lind, L; Crismon, M L; Rush, A J

2010-06-01

Attitudes and expectations about treatment have been associated with symptomatic outcomes, adherence and utilization in patients with psychiatric disorders. No measure of patients' anticipated benefits of treatment on domains of everyday functioning has previously been available. The Anticipated Benefits of Care (ABC) is a new, 10-item questionnaire used to measure patient expectations about the impact of treatment on domains of everyday functioning. The ABC was collected at baseline in adult out-patients with major depressive disorder (MDD) (n=528), bipolar disorder (n=395) and schizophrenia (n=447) in the Texas Medication Algorithm Project (TMAP). Psychometric properties of the ABC were assessed, and the association of ABC scores with treatment response at 3 months was evaluated. Evaluation of the ABC's internal consistency yielded Cronbach's alpha of 0.90-0.92 for patients across disorders. Factor analysis showed that the ABC was unidimensional for all patients and for patients with each disorder. For patients with MDD, lower anticipated benefits of treatment was associated with less symptom improvement and lower odds of treatment response [odds ratio (OR) 0.72, 95% confidence interval (CI) 0.57-0.87, p=0.0011]. There was no association between ABC and symptom improvement or treatment response for patients with bipolar disorder or schizophrenia, possibly because these patients had modest benefits with treatment. The ABC is the first self-report that measures patient expectations about the benefits of treatment on everyday functioning, filling an important gap in available assessments of attitudes and expectations about treatment. The ABC is simple, easy to use, and has acceptable psychometric properties for use in research or clinical settings.

High neuroticism at age 20 predicts history of mental disorders and low self-esteem at age 35.

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Lönnqvist, Jan-Erik; Verkasalo, Markku; Mäkinen, Seppo; Henriksson, Markus

2009-07-01

The authors assessed whether neuroticism in emerging adulthood predicts mental disorders and self-esteem in early adulthood after controlling for possible confounding variables. A sample of 69 male military conscripts was initially assessed at age 20 and again as civilians at age 35. The initial assessment included a psychiatric interview, objective indicators of conscript competence, an intellectual performance test, and neuroticism questionnaires. The follow-up assessment included a Structured Clinical Interview for DSM-IV (SCID; First, Spitzer, Gibbon, & Williams, 1996) and the Rosenberg Self-Esteem Scale (Rosenberg, 1965). Neuroticism predicted future mental disorders and low self-esteem beyond more objective indicators of adjustment. The results support the use of neuroticism as a predictor of future mental disorders, even over periods of time when personality is subject to change.

An Investigation into the Roles of Theory of Mind, Emotion Regulation, and Attachment Styles in Predicting the Traits of Borderline Personality Disorder.

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Ghiasi, Hamed; Mohammadi, Abolalfazl; Zarrinfar, Pouria

2016-10-01

Objective: Borderline personality disorder is one of the most complex and prevalent personality disorders. Many variables have so far been studied in relation to this disorder. This study aimed to investigate the role of emotion regulation, attachment styles, and theory of mind in predicting the traits of borderline personality disorder. Method: In this study, 85 patients with borderline personality disorder were selected using convenience sampling method. To measure the desired variables, the questionnaires of Gross emotion regulation, Collins and Read attachment styles, and Baron Cohen's Reading Mind from Eyes Test were applied. The data were analyzed using multivariate stepwise regression technique. Results: Emotion regulation, attachment styles, and theory of mind predicted 41.2% of the variance criterion altogether; among which, the shares of emotion regulation, attachment styles and theory of mind to the distribution of the traits of borderline personality disorder were 27.5%, 9.8%, and 3.9%, respectively.‎‎ Conclusion : The results of the study revealed that emotion regulation, attachment styles, and theory of mind are important variables in predicting the traits of borderline personality disorder and that these variables can be well applied for both the treatment and identification of this disorder.

An Investigation into the Roles of Theory of Mind, Emotion Regulation, and Attachment Styles in Predicting the Traits of Borderline Personality Disorder

Directory of Open Access Journals (Sweden)

Hamed Ghiasi

2016-12-01

Full Text Available Objective: Borderline personality disorder is one of the most complex and prevalent personality disorders. Many variables have so far been studied in relation to this disorder. This study aimed to investigate the role of emotion regulation, attachment styles, and theory of mind in predicting the traits of borderline personality disorder.Method: In this study, 85 patients with borderline personality disorder were selected using convenience sampling method. To measure the desired variables, the questionnaires of Gross emotion regulation, Collins and Read attachment styles, and Baron Cohen's Reading Mind from Eyes Test were applied. The data were analyzed using multivariate stepwise regression technique.Results: Emotion regulation, attachment styles, and theory of mind predicted 41.2% of the variance criterion altogether; among which, the shares of emotion regulation, attachment styles and theory of mind to the distribution of the traits of borderline personality disorder were 27.5%, 9.8%, and 3.9%, respectively.‎‎Conclusion: The results of the study revealed that emotion regulation, attachment styles, and theory of mind are important variables in predicting the traits of borderline personality disorder and that these variables can be well applied for both the treatment and identification of this disorder.

Screening for attention-deficit/hyperactivity disorder in borderline personality disorder.

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Weibel, Sébastien; Nicastro, Rosetta; Prada, Paco; Cole, Pierre; Rüfenacht, Eva; Pham, Eléonore; Dayer, Alexandre; Perroud, Nader

2018-01-15

A valid screening instrument is needed to detect attention-deficit/hyperactivity disorder (ADHD) in treatment-seeking borderline personality disorder (BPD) patients. We aimed to test the performance of the widely-used Adult ADHD Self-Report Scale v1.1 screener (ASRS-v1.1). 317 BPD subjects were systematically assessed for comorbid ADHD and completed the ASRS-v1.1. 79 BPD patients also completed the Wender Utah Rating Scale (WURS-25). The prevalence of adult ADHD was of 32.4%. The overall positive predictive value of the ASRS-v1.1 was of 38.5%, the negative predictive value 77.0%, the sensitivity 72.8%, and the specificity 43.9%. Combining WURS-25 and ASRS-v1.1 improved sensitivity to 81.8% and specificity to 59.6%. Cross-sectional study on treatment-seeking patients. We found a high prevalence of ADHD using structured interviews. The ASRS-v1.1 was not a sensitive screener for identifying possible ADHD cases in a BPD population, with a high number of false positives. When combined with the WURS-25, it offered improved screening. Copyright © 2017 Elsevier B.V. All rights reserved.

Stressful Life Events Predict Eating Disorder Relapse Following Remission: Six-Year Prospective Outcomes

Science.gov (United States)

Grilo, Carlos M.; Pagano, Maria E.; Stout, Robert L.; Markowitz, John C.; Ansell, Emily B.; Pinto, Anthony; Zanarini, Mary C.; Yen, Shirley; Skodol, Andrew E.

2012-01-01

Objective To examine prospectively the natural course of bulimia nervosa (BN) and eating disorder not-otherwise-specified (EDNOS) and test for the effects of stressful life events (SLE) on relapse after remission from these eating disorders. Method 117 female patients with BN (N = 35) or EDNOS (N = 82) were prospectively followed for 72 months using structured interviews performed at baseline, 6- and 12-months, and then yearly thereafter. ED were assessed with the structured clinical interview for DSM-IV, and monitored over time with the longitudinal interval follow-up evaluation. Personality disorders were assessed with the diagnostic interview for DSM-IV-personality-disorders, and monitored over time with the follow-along-version. The occurrence and specific timing of SLE were assessed with the life events assessment interview. Cox proportional-hazard-regression-analyses tested associations between time-varying levels of SLE and ED relapse, controlling for comorbid psychiatric disorders, ED duration, and time-varying personality-disorder status. Results ED relapse probability was 43%; BN and EDNOS did not differ in time to relapse. Negative SLE significantly predicted ED relapse; elevated work and social stressors were significant predictors. Psychiatric comorbidity, ED duration, and time-varying personality-disorder status were not significant predictors. Discussion Higher work and social stress represent significant warning signs for triggering relapse for women with remitted BN and EDNOS. PMID:21448971

Predicting violence in veterans with posttraumatic stress disorder

Directory of Open Access Journals (Sweden)

Jovanović Aleksandar A.

2009-01-01

Full Text Available Background/Aim. Frequent expression of negative affects, hostility and violent behavior in individuals suffering from posttraumatic stress disorder (PTSD were recognized long ago, and have been retrospectively well documented in war veterans with PTSD who were shown to have an elevated risk for violent behavior when compared to both veterans without PTSD and other psychiatric patients. The aim of this study was to evaluate the accuracy of clinical prediction of violence in combat veterans suffering from PTSD. Methods. The subjects of this study, 104 male combat veterans with PTSD were assessed with the Historical, Clinical and Risk Management 20 (HCR-20, a 20-item clinicianrated instrument for assessing the risks for violence, and their acts of violence during one-year follow-up period were registered based on bimonthly check-up interviews. Results. Our findings showed that the HCR-20, as an actuarial measure, had good internal consistency reliability (α = 0.82, excellent interrater reliability (Interaclass Correlation ICC = 0.85, as well as excellent predictive validity for acts of any violence, non-physical violence or physical violence in the follow-up period (AUC = 0.82-0.86. The HCR-20 also had good interrater reliability (Cohen's kappa = 0.74, and acceptable predictive accuracy for each outcome criterion (AUC = 0.73-0.79. Conclusion. The results of this research confirm that the HCR-20 may also be applied in prediction of violent behavior in the population of patients suffering from PTSD with reliability and validity comparable with the results of previous studies where this instrument was administered to other populations of psychiatric patients.

Childhood pegboard task predicts adult-onset psychosis-spectrum disorder among a genetic high-risk sample

DEFF Research Database (Denmark)

Rakhshan, Pamela; Sørensen, Holger Jelling; DeVylder, Jordan

2016-01-01

Motor abnormalities have been established as a core aspect of psychosis-spectrum disorders, with numerous studies identifying deficits prior to clinical symptom presentation. Additional research is needed to pinpoint standardized motor assessments associated with psychosis-spectrum disorders prior...... to illness onset to enhance prediction and understanding of etiology. With a long history of findings among people with diagnosable psychosis-spectrum disorders, but little research conducted during the premorbid phase, pegboard tasks are a viable and understudied measure of premorbid for psychosis motor......-spectrum disorder (n=33) were less likely to successfully complete the task within time limit relative to controls (χ(2)(2, N=244)=6.94, p=0.03, ϕ=0.17). Additionally, children who eventually developed a psychosis-spectrum disorder took significantly longer to complete the task relative to controls (χ(2)(2, N=244...

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

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Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

Directory of Open Access Journals (Sweden)

Guoxing Li

2015-01-01

Full Text Available The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious.

Preschool anxiety disorders predict different patterns of amygdala-prefrontal connectivity at school-age.

Directory of Open Access Journals (Sweden)

Kimberly L H Carpenter

Full Text Available In this prospective, longitudinal study of young children, we examined whether a history of preschool generalized anxiety, separation anxiety, and/or social phobia is associated with amygdala-prefrontal dysregulation at school-age. As an exploratory analysis, we investigated whether distinct anxiety disorders differ in the patterns of this amygdala-prefrontal dysregulation.Participants were children taking part in a 5-year study of early childhood brain development and anxiety disorders. Preschool symptoms of generalized anxiety, separation anxiety, and social phobia were assessed with the Preschool Age Psychiatric Assessment (PAPA in the first wave of the study when the children were between 2 and 5 years old. The PAPA was repeated at age 6. We conducted functional MRIs when the children were 5.5 to 9.5 year old to assess neural responses to viewing of angry and fearful faces.A history of preschool social phobia predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces. Preschool generalized anxiety predicted less functional connectivity between the amygdala and dorsal prefrontal cortices in response to fearful faces. Finally, a history of preschool separation anxiety predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces and greater school-age functional connectivity between the amygdala and dorsal prefrontal cortices to angry faces.Our results suggest that there are enduring neurobiological effects associated with a history of preschool anxiety, which occur over-and-above the effect of subsequent emotional symptoms. Our results also provide preliminary evidence for the neurobiological differentiation of specific preschool anxiety disorders.

Neurofeedback improves executive functioning in children with autistic spectrum disorders

NARCIS (Netherlands)

Kouijzer, M.E.J.; Moor, J.M.H. de; Gerrits, B.J.L.; Congedo, M.; Schie, H.T. van

2009-01-01

Seven autistic children diagnosed with autism spectrum disorders (ASD) received a neurofeedback treatment that aimed to improve their level of executive control. Neurofeedback successfully reduced children’s heightened theta/beta ratio by inhibiting theta activation and enhancing beta activation

What determines the management of anxiety disorders and its improvement?

NARCIS (Netherlands)

Smolders, M.; Laurant, M.G.H.; Wamel, A. van; Grol, R.P.T.M.; Wensing, M.J.P.

2008-01-01

INTRODUCTION: Although anxiety disorders are highly prevalent, lack of correct diagnosis and related concerns about treatment are serious clinical problems. Several factors affect, positively or negatively, management of anxiety and its improvement. A literature review and thematic analysis was

Recent Improvements in IERS Rapid Service/Prediction Center Products

National Research Council Canada - National Science Library

Stamatakos, N; Luzum, B; Wooden, W

2007-01-01

...) at USNO has made several improvements to its combination and pre- diction products. These improvements are due to the inclusion of new input data sources as well as modifications to the combination and prediction algorithms...

Predictive Capacity of Cloninger's temperament and character inventory (TCI-R) in alcohol use disorder outcomes.

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Ávila Escribano, José Juan; Sánchez Barba, Mercedes; Álvarez Pedrero, Aida; López Villarreal, Ana; Recio Pérez, Joaquina; Rodríguez Rodilla, Manuela; Fraile García, Eulalia

2016-06-14

to investigate the ability to predict the outcome of alcohol use disorders through Cloninger's temperament and character inventory (TCI-R). this is a prospective study consisting of 237 outpatients with alcohol use disorders who underwent follow-up treatment for 6 months and whose personality traits were studied using TCI-R. At the end of that period, the scores of each TCI-R trait were analyzed in terms of those who remained in treatment and those who dropped out. The whole group scored highly in novelty seeking (NS) and harm avoidance (HA) and produced low scores in self-directedness (SD), these last traits are considered prominent. The drop-out group scored significantly (p=.004) higher in novelty seeking (NS) than the follow-up group. Also, when the score was higher than the 67 percentile the likelihood of abandoning the treatment was 1.07 times higher. Cloninger's temperament and character inventory is a good instrument to predict the outcome of treatment of patients with alcohol use disorders and the novelty seeking (NS) dimension is strongly related to therapeutic drop-out.

Heterogeneity in development of aspects of working memory predicts longitudinal attention deficit hyperactivity disorder symptom change.

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Karalunas, Sarah L; Gustafsson, Hanna C; Dieckmann, Nathan F; Tipsord, Jessica; Mitchell, Suzanne H; Nigg, Joel T

2017-08-01

The role of cognitive mechanisms in the clinical course of neurodevelopmental disorders is poorly understood. Attention Deficit Hyperactivity Disorder (ADHD) is emblematic in that numerous alterations in cognitive development are apparent, yet how they relate to changes in symptom expression with age is unclear. To resolve the role of cognitive mechanisms in ADHD, a developmental perspective that takes into account expected within-group heterogeneity is needed. The current study uses an accelerated longitudinal design and latent trajectory growth mixture models in a sample of children ages 7-13 years carefully characterized as with (n = 437) and without (n = 297) ADHD to (a) identify heterogeneous developmental trajectories for response inhibition, visual spatial working memory maintenance, and delayed reward discounting and (b) to assess the relationships between these cognitive trajectories and ADHD symptom change. Best-fitting models indicated multiple trajectory classes in both the ADHD and typically developing samples, as well as distinct relationships between each cognitive process and ADHD symptom change. Developmental change in response inhibition and delayed reward discounting were unrelated to ADHD symptom change, while individual differences in the rate of visual spatial working memory maintenance improvement predicted symptom remission in ADHD. Characterizing heterogeneity in cognitive development will be crucial for clarifying mechanisms of symptom persistence and recovery. Results here suggest working memory maintenance may be uniquely related to ADHD symptom improvement. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predictive value of the DASH tool for predicting return to work of injured workers with musculoskeletal disorders of the upper extremity.

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Armijo-Olivo, Susan; Woodhouse, Linda J; Steenstra, Ivan A; Gross, Douglas P

2016-12-01

To determine whether the Disabilities of the Arm, Shoulder, and Hand (DASH) tool added to the predictive ability of established prognostic factors, including patient demographic and clinical outcomes, to predict return to work (RTW) in injured workers with musculoskeletal (MSK) disorders of the upper extremity. A retrospective cohort study using a population-based database from the Workers' Compensation Board of Alberta (WCB-Alberta) that focused on claimants with upper extremity injuries was used. Besides the DASH, potential predictors included demographic, occupational, clinical and health usage variables. Outcome was receipt of compensation benefits after 3 months. To identify RTW predictors, a purposeful logistic modelling strategy was used. A series of receiver operating curve analyses were performed to determine which model provided the best discriminative ability. The sample included 3036 claimants with upper extremity injuries. The final model for predicting RTW included the total DASH score in addition to other established predictors. The area under the curve for this model was 0.77, which is interpreted as fair discrimination. This model was statistically significantly different than the model of established predictors alone (pmodels (p=0.34). The DASH tool together with other established predictors significantly helped predict RTW after 3 months in participants with upper extremity MSK disorders. An appealing result for clinicians and busy researchers is that DASH item 23 has equal predictive ability to the total DASH score. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Low social rhythm regularity predicts first onset of bipolar spectrum disorders among at-risk individuals with reward hypersensitivity.

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Alloy, Lauren B; Boland, Elaine M; Ng, Tommy H; Whitehouse, Wayne G; Abramson, Lyn Y

2015-11-01

The social zeitgeber model (Ehlers, Frank, & Kupfer, 1988) suggests that irregular daily schedules or social rhythms provide vulnerability to bipolar spectrum disorders. This study tested whether social rhythm regularity prospectively predicted first lifetime onset of bipolar spectrum disorders in adolescents already at risk for bipolar disorder based on exhibiting reward hypersensitivity. Adolescents (ages 14-19 years) previously screened to have high (n = 138) or moderate (n = 95) reward sensitivity, but no lifetime history of bipolar spectrum disorder, completed measures of depressive and manic symptoms, family history of bipolar disorder, and the Social Rhythm Metric. They were followed prospectively with semistructured diagnostic interviews every 6 months for an average of 31.7 (SD = 20.1) months. Hierarchical logistic regression indicated that low social rhythm regularity at baseline predicted greater likelihood of first onset of bipolar spectrum disorder over follow-up among high-reward-sensitivity adolescents but not moderate-reward-sensitivity adolescents, controlling for follow-up time, gender, age, family history of bipolar disorder, and initial manic and depressive symptoms (β = -.150, Wald = 4.365, p = .037, odds ratio = .861, 95% confidence interval [.748, .991]). Consistent with the social zeitgeber theory, low social rhythm regularity provides vulnerability to first onset of bipolar spectrum disorder among at-risk adolescents. It may be possible to identify adolescents at risk for developing a bipolar spectrum disorder based on exhibiting both reward hypersensitivity and social rhythm irregularity before onset occurs. (c) 2015 APA, all rights reserved).

Risk Factors of Attempted Suicide in Bipolar Disorder

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Cassidy, Frederick

2011-01-01

Suicide rates of bipolar patients are among the highest of any psychiatric disorder, and improved identification of risk factors for attempted and completed suicide translates into improved clinical outcome. Factors that may be predictive of suicidality in an exclusively bipolar population are examined. White race, family suicide history, and…

Factors of Predicted Learning Disorders and their Interaction with Attentional and Perceptual Training Procedures.

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Friar, John T.

Two factors of predicted learning disorders were investigated: (1) inability to maintain appropriate classroom behavior (BEH), (2) perceptual discrimination deficit (PERC). Three groups of first-graders (BEH, PERC, normal control) were administered measures of impulse control, distractability, auditory discrimination, and visual discrimination.…

DISC Predictive Scales (DPS): Factor Structure and Uniform Differential Item Functioning Across Gender and Three Racial/Ethnic Groups for ADHD, Conduct Disorder, and Oppositional Defiant Disorder Symptoms

OpenAIRE

Wiesner, Margit; Kanouse, David E.; Elliott, Marc N.; Windle, Michael; Schuster, Mark A.

2015-01-01

The factor structure and potential uniform differential item functioning (DIF) among gender and three racial/ethnic groups of adolescents (African American, Latino, White) were evaluated for attention deficit/hyperactivity disorder (ADHD), conduct disorder (CD), and oppositional defiant disorder (ODD) symptom scores of the DISC Predictive Scales (DPS; Leung et al., 2005; Lucas et al., 2001). Primary caregivers reported on DSM–IV ADHD, CD, and ODD symptoms for a probability sample of 4,491 chi...

Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions.

Science.gov (United States)

Xie, Hongbo; Vucetic, Slobodan; Iakoucheva, Lilia M; Oldfield, Christopher J; Dunker, A Keith; Uversky, Vladimir N; Obradovic, Zoran

2007-05-01

Identifying relationships between function, amino acid sequence, and protein structure represents a major challenge. In this study, we propose a bioinformatics approach that identifies functional keywords in the Swiss-Prot database that correlate with intrinsic disorder. A statistical evaluation is employed to rank the significance of these correlations. Protein sequence data redundancy and the relationship between protein length and protein structure were taken into consideration to ensure the quality of the statistical inferences. Over 200,000 proteins from the Swiss-Prot database were analyzed using this approach. The predictions of intrinsic disorder were carried out using PONDR VL3E predictor of long disordered regions that achieves an accuracy of above 86%. Overall, out of the 710 Swiss-Prot functional keywords that were each associated with at least 20 proteins, 238 were found to be strongly positively correlated with predicted long intrinsically disordered regions, whereas 302 were strongly negatively correlated with such regions. The remaining 170 keywords were ambiguous without strong positive or negative correlation with the disorder predictions. These functions cover a large variety of biological activities and imply that disordered regions are characterized by a wide functional repertoire. Our results agree well with literature findings, as we were able to find at least one illustrative example of functional disorder or order shown experimentally for the vast majority of keywords showing the strongest positive or negative correlation with intrinsic disorder. This work opens a series of three papers, which enriches the current view of protein structure-function relationships, especially with regards to functionalities of intrinsically disordered proteins, and provides researchers with a novel tool that could be used to improve the understanding of the relationships between protein structure and function. The first paper of the series describes our

Internet-Based Motivation Program for Women With Eating Disorders: Eating Disorder Pathology and Depressive Mood Predict Dropout

Science.gov (United States)

Hirschfeld, Gerrit; Rieger, Elizabeth; Schmidt, Ulrike; Kosfelder, Joachim; Hechler, Tanja; Schulte, Dietmar; Vocks, Silja

2014-01-01

Background One of the main problems of Internet-delivered interventions for a range of disorders is the high dropout rate, yet little is known about the factors associated with this. We recently developed and tested a Web-based 6-session program to enhance motivation to change for women with anorexia nervosa, bulimia nervosa, or related subthreshold eating pathology. Objective The aim of the present study was to identify predictors of dropout from this Web program. Methods A total of 179 women took part in the study. We used survival analyses (Cox regression) to investigate the predictive effect of eating disorder pathology (assessed by the Eating Disorders Examination-Questionnaire; EDE-Q), depressive mood (Hopkins Symptom Checklist), motivation to change (University of Rhode Island Change Assessment Scale; URICA), and participants’ age at dropout. To identify predictors, we used the least absolute shrinkage and selection operator (LASSO) method. Results The dropout rate was 50.8% (91/179) and was equally distributed across the 6 treatment sessions. The LASSO analysis revealed that higher scores on the Shape Concerns subscale of the EDE-Q, a higher frequency of binge eating episodes and vomiting, as well as higher depression scores significantly increased the probability of dropout. However, we did not find any effect of the URICA or age on dropout. Conclusions Women with more severe eating disorder pathology and depressive mood had a higher likelihood of dropping out from a Web-based motivational enhancement program. Interventions such as ours need to address the specific needs of women with more severe eating disorder pathology and depressive mood and offer them additional support to prevent them from prematurely discontinuing treatment. PMID:24686856

Internet-based motivation program for women with eating disorders: eating disorder pathology and depressive mood predict dropout.

Science.gov (United States)

von Brachel, Ruth; Hötzel, Katrin; Hirschfeld, Gerrit; Rieger, Elizabeth; Schmidt, Ulrike; Kosfelder, Joachim; Hechler, Tanja; Schulte, Dietmar; Vocks, Silja

2014-03-31

One of the main problems of Internet-delivered interventions for a range of disorders is the high dropout rate, yet little is known about the factors associated with this. We recently developed and tested a Web-based 6-session program to enhance motivation to change for women with anorexia nervosa, bulimia nervosa, or related subthreshold eating pathology. The aim of the present study was to identify predictors of dropout from this Web program. A total of 179 women took part in the study. We used survival analyses (Cox regression) to investigate the predictive effect of eating disorder pathology (assessed by the Eating Disorders Examination-Questionnaire; EDE-Q), depressive mood (Hopkins Symptom Checklist), motivation to change (University of Rhode Island Change Assessment Scale; URICA), and participants' age at dropout. To identify predictors, we used the least absolute shrinkage and selection operator (LASSO) method. The dropout rate was 50.8% (91/179) and was equally distributed across the 6 treatment sessions. The LASSO analysis revealed that higher scores on the Shape Concerns subscale of the EDE-Q, a higher frequency of binge eating episodes and vomiting, as well as higher depression scores significantly increased the probability of dropout. However, we did not find any effect of the URICA or age on dropout. Women with more severe eating disorder pathology and depressive mood had a higher likelihood of dropping out from a Web-based motivational enhancement program. Interventions such as ours need to address the specific needs of women with more severe eating disorder pathology and depressive mood and offer them additional support to prevent them from prematurely discontinuing treatment.

Interest level in 2-year-olds with autism spectrum disorder predicts rate of verbal, nonverbal, and adaptive skill acquisition

OpenAIRE

Klintwall, Lars; Macari, Suzanne; Eikeseth, Svein; Chawarska, Katarzyna

2014-01-01

Recent studies have suggested that skill acquisition rates for children with autism spectrum disorders receiving early interventions can be predicted by child motivation. We examined whether level of interest during an Autism Diagnostic Observation Schedule assessment at 2 years predicts subsequent rates of verbal, nonverbal, and adaptive skill acquisition to the age of 3 years. A total of 70 toddlers with autism spectrum disorder, mean age of 21.9 months, were scored using Interest Level Sco...

The presence, predictive utility, and clinical significance of body dysmorphic symptoms in women with eating disorders

Science.gov (United States)

2013-01-01

Background Both eating disorders (EDs) and body dysmorphic disorder (BDD) are disorders of body image. This study aimed to assess the presence, predictive utility, and impact of clinical features commonly associated with BDD in women with EDs. Methods Participants recruited from two non-clinical cohorts of women, symptomatic and asymptomatic of EDs, completed a survey on ED (EDE-Q) and BDD (BDDE-SR) psychopathology, psychological distress (K-10), and quality of life (SF-12). Results A strong correlation was observed between the total BDDE-SR and the global EDE-Q scores (r = 0.79, p 0.05) measured appearance checking, reassurance-seeking, camouflaging, comparison-making, and social avoidance. In addition to these behaviors, inspection of sensitivity (Se) and specificity (Sp) revealed that BDDE-SR items measuring preoccupation and dissatisfaction with appearance were most predictive of ED cases (Se and Sp > 0.60). Higher total BDDE-SR scores were associated with greater distress on the K-10 and poorer quality of life on the SF-12 (all p < 0.01). Conclusions Clinical features central to the model of BDD are common in, predictive of, and associated with impairment in women with EDs. Practice implications are that these features be included in the assessment and treatment of EDs. PMID:24999401

Neurophysiology in preschool improves behavioral prediction of reading ability throughout primary school.

Science.gov (United States)

Maurer, Urs; Bucher, Kerstin; Brem, Silvia; Benz, Rosmarie; Kranz, Felicitas; Schulz, Enrico; van der Mark, Sanne; Steinhausen, Hans-Christoph; Brandeis, Daniel

2009-08-15

More struggling readers could profit from additional help at the beginning of reading acquisition if dyslexia prediction were more successful. Currently, prediction is based only on behavioral assessment of early phonological processing deficits associated with dyslexia, but it might be improved by adding brain-based measures. In a 5-year longitudinal study of children with (n = 21) and without (n = 23) familial risk for dyslexia, we tested whether neurophysiological measures of automatic phoneme and tone deviance processing obtained in kindergarten would improve prediction of reading over behavioral measures alone. Together, neurophysiological and behavioral measures obtained in kindergarten significantly predicted reading in school. Particularly the late mismatch negativity measure that indicated hemispheric lateralization of automatic phoneme processing improved prediction of reading ability over behavioral measures. It was also the only significant predictor for long-term reading success in fifth grade. Importantly, this result also held for the subgroup of children at familial risk. The results demonstrate that brain-based measures of processing deficits associated with dyslexia improve prediction of reading and thus may be further evaluated to complement clinical practice of dyslexia prediction, especially in targeted populations, such as children with a familial risk.

Prediction of methylphenidate treatment outcome in adults with attention-deficit/hyperactivity disorder (ADHD).

Science.gov (United States)

Retz, Wolfgang; Retz-Junginger, Petra

2014-11-01

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent mental disorder of childhood, which often persists in adulthood. Methylphenidate (MPH) is one of the most effective medications to treat ADHD, but also few adult patients show no sufficient response to this drug. In this paper, we give an overview regarding genetic, neuroimaging, clinical and other studies which have tried to reveal the reasons for non-response in adults with ADHD, based on a systematic literature search. Although MPH is a well-established treatment for adults with ADHD, research regarding the prediction of treatment outcome is still limited and has resulted in inconsistent findings. No reliable neurobiological markers of treatment response have been identified so far. Some findings from clinical studies suggest that comorbidity with substance use disorders and personality disorders has an impact on treatment course and outcome. As MPH is widely used in the treatment of adults with ADHD, much more work is needed regarding positive and negative predictors of long-term treatment outcome in order to optimize the pharmacological treatment of adult ADHD patients.

Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification.

Science.gov (United States)

Al-Kaysi, Alaa M; Al-Ani, Ahmed; Loo, Colleen K; Powell, Tamara Y; Martin, Donel M; Breakspear, Michael; Boonstra, Tjeerd W

2017-01-15

Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing. We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (30-100Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach. Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004). Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept. These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach. Copyright © 2016 Elsevier B.V. All rights reserved.

Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy

KAUST Repository

Ogorzalek, Tadeusz L.

2018-01-04

Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As HT, solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. This article is protected by copyright. All rights reserved.

Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy

KAUST Repository

Ogorzalek, Tadeusz L.; Hura, Greg L.; Belsom, Adam; Burnett, Kathryn H.; Kryshtafovych, Andriy; Tainer, John A.; Rappsilber, Juri; Tsutakawa, Susan E.; Fidelis, Krzysztof

2018-01-01

Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As HT, solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. This article is protected by copyright. All rights reserved.

Predictive Validity of DSM-IV Oppositional Defiant and Conduct Disorders in Clinically Referred Preschoolers

Science.gov (United States)

Keenan, Kate; Boeldt, Debra; Chen, Diane; Coyne, Claire; Donald, Radiah; Duax, Jeanne; Hart, Katherine; Perrott, Jennifer; Strickland, Jennifer; Danis, Barbara; Hill, Carri; Davis, Shante; Kampani, Smita; Humphries, Marisha

2011-01-01

Background: Diagnostic validity of oppositional defiant and conduct disorders (ODD and CD) for preschoolers has been questioned based on concerns regarding the ability to differentiate normative, transient disruptive behavior from clinical symptoms. Data on concurrent validity have accumulated, but predictive validity is limited. Predictive…

Maternal stress predicted by characteristics of children with autism spectrum disorder and intellectual disability

NARCIS (Netherlands)

Peters-Scheffer, N.C.; Didden, H.C.M.; Korzilius, H.P.L.M.

2012-01-01

To determine maternal stress and child variables predicting maternal stress, 104 mothers of children with autism spectrum disorder (ASD) and intellectual disability (ID) completed the Dutch version of the Parental Stress Index (PSI; De Brock, Vermulst, Gerris, & Abidin, 1992) every six months over a

Neurofeedback Improves Executive Functioning in Children with Autism Spectrum Disorders

Science.gov (United States)

Kouijzer, Mirjam E. J.; de Moor, Jan M. H.; Gerrits, Berrie J. L.; Congedo, Marco; van Schie, Hein T.

2009-01-01

Seven autistic children diagnosed with autism spectrum disorders (ASD) received a neurofeedback treatment that aimed to improve their level of executive control. Neurofeedback successfully reduced children's heightened theta/beta ratio by inhibiting theta activation and enhancing beta activation over sessions. Following treatment, children's…

Machine Learning Principles Can Improve Hip Fracture Prediction

DEFF Research Database (Denmark)

Kruse, Christian; Eiken, Pia; Vestergaard, Peter

2017-01-01

Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

Improving urban wind flow predictions through data assimilation

Science.gov (United States)

Sousa, Jorge; Gorle, Catherine

2017-11-01

Computational fluid dynamic is fundamentally important to several aspects in the design of sustainable and resilient urban environments. The prediction of the flow pattern for example can help to determine pedestrian wind comfort, air quality, optimal building ventilation strategies, and wind loading on buildings. However, the significant variability and uncertainty in the boundary conditions poses a challenge when interpreting results as a basis for design decisions. To improve our understanding of the uncertainties in the models and develop better predictive tools, we started a pilot field measurement campaign on Stanford University's campus combined with a detailed numerical prediction of the wind flow. The experimental data is being used to investigate the potential use of data assimilation and inverse techniques to better characterize the uncertainty in the results and improve the confidence in current wind flow predictions. We consider the incoming wind direction and magnitude as unknown parameters and perform a set of Reynolds-averaged Navier-Stokes simulations to build a polynomial chaos expansion response surface at each sensor location. We subsequently use an inverse ensemble Kalman filter to retrieve an estimate for the probabilistic density function of the inflow parameters. Once these distributions are obtained, the forward analysis is repeated to obtain predictions for the flow field in the entire urban canopy and the results are compared with the experimental data. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR.

Childhood trauma and dissociative symptoms predict frontal EEG asymmetry in borderline personality disorder.

Science.gov (United States)

Popkirov, Stoyan; Flasbeck, Vera; Schlegel, Uwe; Juckel, Georg; Brüne, Martin

2018-03-15

Frontal EEG asymmetry (FEA) has been studied as both state and trait parameter in emotion regulation and affective disorders. Its significance in borderline personality disorder (BPD) remains largely unknown. Twenty-six BPD patients and 26 healthy controls underwent EEG before and after mood induction using aversive images. A slight but significant shift from left- to right-sided asymmetry over prefrontal electrodes occurred across all subjects. In BPD baseline FEA over F7 and F8 correlated significantly with childhood trauma and functional neurological "conversion" symptoms as assessed by respective questionnaires. Regression analysis revealed a predictive role of both childhood trauma and dissociative neurological symptoms. FEA offers a relatively stable electrophysiological correlate of BPD psychopathology that responds only minimally to acute mood changes. Future studies should address whether this psychophysiological association is universal for trauma- and dissociation-related disorders, and whether it is responsive to psychotherapy.

Premorbid teacher-rated social functioning predicts adult schizophrenia-spectrum disorder: A high-risk prospective investigation

DEFF Research Database (Denmark)

Tsuji, Thomas; Kline, Emily; Sorensen, Holger J.

2013-01-01

Social functioning deficits are a core component of schizophrenia spectrum disorders, and may emerge years prior to the onset of diagnosable illness. The current study prospectively examines the relation between teacher-rated childhood social dysfunction and later mental illness among participants...... who were at genetic high-risk for schizophrenia and controls (n=244). The teacher-rated social functioning scale significantly predicted psychiatric outcomes (schizophrenia-spectrum vs. other psychiatric disorder vs. no mental illness). Poor premorbid social functioning appears to constitute a marker...

Improving the Transportability of CBT for Internalizing Disorders in Children

Science.gov (United States)

Elkins, R. Meredith; McHugh, R. Kathryn; Santucci, Lauren C.; Barlow, David H.

2011-01-01

Research provides strong support for the efficacy and effectiveness of cognitive behavioral therapy (CBT) for the treatment of childhood internalizing disorders. Given evidence for limited dissemination and implementation of CBT outside of academic settings, efforts are underway to improve its transportability so that more children with mental…

Improving prevention of depression and anxiety disorders: repetitive negative thinking as a promising target

NARCIS (Netherlands)

Topper, M.; Emmelkamp, P.M.G.; Ehring, T.

2010-01-01

Prevention of depression and anxiety disorders is widely acknowledged as an important health care investment. However, existing preventive interventions have only shown modest effects. In order to improve the efficacy of prevention of depression and anxiety disorders, a number of authors have

The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

Science.gov (United States)

Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J

2011-07-01

The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

State dissociation moderates response to dialectical behavior therapy for posttraumatic stress disorder in women with and without borderline personality disorder

Directory of Open Access Journals (Sweden)

Nikolaus Kleindienst

2016-07-01

Full Text Available Background: Patients with posttraumatic stress disorder (PTSD are prone to dissociation, which in theory should interfere with successful treatment. However, most empirical studies do not substantiate this assumption. Objective: The primary objective was to test whether state dissociation predicts the success of an adaptation of dialectical behavior therapy designed for the treatment of patients with PTSD after childhood sexual abuse (CSA (DBT-PTSD. We further explored whether the operationalization of dissociation as state versus trait dissociation made a difference with respect to prediction of improvement. Methods: We present a hypothesis-driven post hoc analysis of a randomized controlled trial on the efficacy in patients with PTSD after CSA. Regression analyses relating pre–post improvements in the Clinician-Administered PTSD Scale (CAPS and the Posttraumatic Diagnostic Scale (PDS to dissociation were applied to the women who participated in the active treatment arm (DBT-PTSD. Multivariate models accounting for major confounders were used to relate improvements in both the CAPS and the PDS to (1 state dissociation as assessed after each treatment session and (2 trait dissociation as assessed at baseline. Results: State dissociation during psychotherapy sessions predicted improvement after DBT-PTSD: patients with low state dissociation during treatment had a higher chance to show substantial improvement. This relation consistently emerged across subgroups of PTSD patients with and without borderline personality disorder. The operationalization of dissociation as state versus trait dissociation made a difference as improvement was not significantly predicted from trait dissociation. Conclusions: Dissociation during treatment sessions may reduce success with trauma-focused therapies such as DBT-PTSD. Accordingly, clinical studies aimed at improving ways to address dissociation are needed.

State dissociation moderates response to dialectical behavior therapy for posttraumatic stress disorder in women with and without borderline personality disorder.

Science.gov (United States)

Kleindienst, Nikolaus; Priebe, Kathlen; Görg, Nora; Dyer, Anne; Steil, Regina; Lyssenko, Lisa; Winter, Dorina; Schmahl, Christian; Bohus, Martin

2016-01-01

Patients with posttraumatic stress disorder (PTSD) are prone to dissociation, which in theory should interfere with successful treatment. However, most empirical studies do not substantiate this assumption. The primary objective was to test whether state dissociation predicts the success of an adaptation of dialectical behavior therapy designed for the treatment of patients with PTSD after childhood sexual abuse (CSA) (DBT-PTSD). We further explored whether the operationalization of dissociation as state versus trait dissociation made a difference with respect to prediction of improvement. We present a hypothesis-driven post hoc analysis of a randomized controlled trial on the efficacy in patients with PTSD after CSA. Regression analyses relating pre-post improvements in the Clinician-Administered PTSD Scale (CAPS) and the Posttraumatic Diagnostic Scale (PDS) to dissociation were applied to the women who participated in the active treatment arm (DBT-PTSD). Multivariate models accounting for major confounders were used to relate improvements in both the CAPS and the PDS to (1) state dissociation as assessed after each treatment session and (2) trait dissociation as assessed at baseline. State dissociation during psychotherapy sessions predicted improvement after DBT-PTSD: patients with low state dissociation during treatment had a higher chance to show substantial improvement. This relation consistently emerged across subgroups of PTSD patients with and without borderline personality disorder. The operationalization of dissociation as state versus trait dissociation made a difference as improvement was not significantly predicted from trait dissociation. Dissociation during treatment sessions may reduce success with trauma-focused therapies such as DBT-PTSD. Accordingly, clinical studies aimed at improving ways to address dissociation are needed.

Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD relative to autism using structural magnetic resonance imaging.

Directory of Open Access Journals (Sweden)

Lena Lim

Full Text Available Attention Deficit Hyperactivity Disorder (ADHD is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study's aim was to apply Gaussian process classification (GPC to grey matter (GM volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD.Twenty-nine adolescent ADHD boys and 29 age-matched healthy and 19 boys with ASD were scanned. GPC was applied to make disorder-specific predictions of ADHD diagnostic status based on individual brain structure patterns. In addition, voxel-based morphometry (VBM analysis tested for traditional univariate group level differences in GM.The pattern of GM correctly classified 75.9% of patients and 82.8% of controls, achieving an overall classification accuracy of 79.3%. Furthermore, classification was disorder-specific relative to ASD. The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.The study provides evidence that pattern recognition analysis can provide significant individual diagnostic classification of ADHD patients and healthy controls based on distributed GM patterns with 79.3% accuracy and that this is disorder-specific relative to ASD. Findings are a promising first step towards finding an objective differential diagnostic tool based on brain imaging measures to aid with the subjective clinical diagnosis of ADHD.

Comparative Genomics and Disorder Prediction Identify Biologically Relevant SH3 Protein Interactions.

Directory of Open Access Journals (Sweden)

2005-08-01

Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions.

Directory of Open Access Journals (Sweden)

Pedro Beltrao

2005-08-01

Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

Meal and snack-time eating disorder cognitions predict eating disorder behaviors and vice versa in a treatment seeking sample: A mobile technology based ecological momentary assessment study.

Science.gov (United States)

Levinson, Cheri A; Sala, Margarita; Fewell, Laura; Brosof, Leigh C; Fournier, Lauren; Lenze, Eric J

2018-06-01

Individuals with eating disorders experience high anxiety when eating, which may contribute to the high relapse rates seen in the eating disorders. However, it is unknown if specific cognitions associated with such anxiety (e.g., fears of gaining weight) may lead to engagement in eating disorder behaviors (e.g., weighing oneself). Participants (N = 66) recently treated at a residential eating disorder facility and diagnosed with an eating disorder (primarily anorexia nervosa; n = 40; 60.6%) utilized a mobile application to answer questions about mealtime cognitions, anxiety, and eating disorder behaviors four times a day for one week. Hierarchical linear models using cross-lag analyses identified that there were quasi-causal (and sometimes reciprocal) within-person relationships between specific eating disorder cognitions and subsequent eating disorder behaviors. These cognitions predicted higher anxiety during the next meal and eating disorder pathology at one-month follow-up. Interventions personalized to target these specific cognitions in real time might reduce eating disorder relapse. Copyright © 2018 Elsevier Ltd. All rights reserved.

Motivation and treatment credibility predict alliance in cognitive behavioral treatment for youth with anxiety disorders in community clinics.

Science.gov (United States)

Fjermestad, K W; Lerner, M D; McLeod, B D; Wergeland, G J H; Haugland, B S M; Havik, O E; Öst, L-G; Silverman, W K

2017-11-16

We examined whether motivation and treatment credibility predicted alliance in a 10-session cognitive behavioral treatment delivered in community clinics for youth anxiety disorders. Ninety-one clinic-referred youths (mean age  = 11.4 years, standard deviation = 2.1, range 8-15 years, 49.5% boys) with anxiety disorders-rated treatment motivation at pretreatment and perceived treatment credibility after session 1. Youths and therapists (YT) rated alliance after session 3 (early) and session 7 (late). Hierarchical linear models were applied to examine whether motivation and treatment credibility predicted YT early alliance, YT alliance change, and YT alliance agreement. Motivation predicted high early YT alliance, but not YT alliance change or alliance agreement. Youth-rated treatment credibility predicted high early youth alliance and high YT positive alliance change, but not early therapist alliance or alliance agreement. Conclusion Efforts to enhance youth motivation and treatment credibility early in treatment could facilitate the formation of a strong YT alliance. © 2017 Wiley Periodicals, Inc.

Sleep-wake profiles predict longitudinal changes in manic symptoms and memory in young people with mood disorders.

Science.gov (United States)

Robillard, Rébecca; Hermens, Daniel F; Lee, Rico S C; Jones, Andrew; Carpenter, Joanne S; White, Django; Naismith, Sharon L; Southan, James; Whitwell, Bradley; Scott, Elizabeth M; Hickie, Ian B

2016-10-01

Mood disorders are characterized by disabling symptoms and cognitive difficulties which may vary in intensity throughout the course of the illness. Sleep-wake cycles and circadian rhythms influence emotional regulation and cognitive functions. However, the relationships between the sleep-wake disturbances experienced commonly by people with mood disorders and the longitudinal changes in their clinical and cognitive profile are not well characterized. This study investigated associations between initial sleep-wake patterns and longitudinal changes in mood symptoms and cognitive functions in 50 young people (aged 13-33 years) with depression or bipolar disorder. Data were based on actigraphy monitoring conducted over approximately 2 weeks and clinical and neuropsychological assessment. As part of a longitudinal cohort study, these assessments were repeated after a mean follow-up interval of 18.9 months. No significant differences in longitudinal clinical changes were found between the participants with depression and those with bipolar disorder. Lower sleep efficiency was predictive of longitudinal worsening in manic symptoms (P = 0.007). Shorter total sleep time (P = 0.043) and poorer circadian rhythmicity (P = 0.045) were predictive of worsening in verbal memory. These findings suggest that some sleep-wake and circadian disturbances in young people with mood disorders may be associated with less favourable longitudinal outcomes, notably for subsequent manic symptoms and memory difficulties. © 2016 European Sleep Research Society.

Default mode network deactivation to smoking cue relative to food cue predicts treatment outcome in nicotine use disorder.

Science.gov (United States)

Wilcox, Claire E; Claus, Eric D; Calhoun, Vince D; Rachakonda, Srinivas; Littlewood, Rae A; Mickey, Jessica; Arenella, Pamela B; Goodreau, Natalie; Hutchison, Kent E

2018-01-01

Identifying predictors of treatment outcome for nicotine use disorders (NUDs) may help improve efficacy of established treatments, like varenicline. Brain reactivity to drug stimuli predicts relapse risk in nicotine and other substance use disorders in some studies. Activity in the default mode network (DMN) is affected by drug cues and other palatable cues, but its clinical significance is unclear. In this study, 143 individuals with NUD (male n = 91, ages 18-55 years) received a functional magnetic resonance imaging scan during a visual cue task during which they were presented with a series of smoking-related or food-related video clips prior to randomization to treatment with varenicline (n = 80) or placebo. Group independent components analysis was utilized to isolate the DMN, and temporal sorting was used to calculate the difference between the DMN blood-oxygen-level dependent signal during smoke cues and that during food cues for each individual. Food cues were associated with greater deactivation compared with smoke cues in the DMN. In correcting for baseline smoking and other clinical variables, which have been shown to be related to treatment outcome in previous work, a less positive Smoke - Food difference score predicted greater smoking at 6 and 12 weeks when both treatment groups were combined (P = 0.005, β = -0.766). An exploratory analysis of executive control and salience networks demonstrated that a more positive Smoke - Food difference score for executive control network predicted a more robust response to varenicline relative to placebo. These findings provide further support to theories that brain reactivity to palatable cues, and in particular in DMN, may have a direct clinical relevance in NUD. © 2017 Society for the Study of Addiction.

The effect of erythropoietin on cognition in affective disorders

DEFF Research Database (Denmark)

Ott, Caroline Vintergaard; Vinberg, Maj; Kessing, Lars V

2016-01-01

impairment predicted treatment-efficacy. Pearson correlations were used to assess associations between objective and subjective cognition, quality of life and socio-occupational capacity. EPO improved speed of complex cognitive processing across affective disorders at weeks 9 and 14 (p≤0.05). In EPO......-efficacy and (III) if cognitive improvement correlates with better subjective cognitive function, quality of life and socio-occupational capacity. Patients with unipolar or bipolar disorder were randomized to eight weekly EPO (N=40) or saline (N=39) infusions. Cognition, mood, quality of life and socio...... improvement correlated with reduced cognitive complaints but not with quality of life or socio-occupational function. As the analyses were performed post-hoc, findings are only hypothesis-generating. In conclusion, pro-cognitive effects of EPO occurred across affective disorders. Neuropsychological screening...

Predicting incentives to change among adolescents with substance abuse disorder.

Science.gov (United States)

Breda, Carolyn; Heflinger, Craig Anne

2004-05-01

While interest in understanding the incentives to change among individuals with substance abuse disorders is growing, little is known about incentives among adolescents with substance abuse disorders who are participating in formal services. The present research assesses the degree and nature of motivation and treatment readiness among adolescents admitted to substance abuse services, and whether such factors vary across significant subgroups of youth based on their social, legal, or clinical profiles. Data are based on interviews with 249 youth between 12 and 18 years of age who have been admitted to either inpatient, residential, or outpatient substance abuse treatment. Measures are adapted from an instrument developed to assess multiple domains of motivation to change (e.g., intrinsic and extrinsic motivation, treatment readiness). Results suggest that the incentive to change among adolescents with substance-abusing behavior is modest at best, regardless of dimension. Nonetheless, ethnicity, type of substance use, and psychopathology significantly predict incentives to change, though the predictors depend on which dimension is considered. The most robust predictor of incentives is the severity of negative consequences associated with youth's substance use--the greater the severity, the greater the incentives. Findings underscore the need to examine the utility and dimensionality of incentive for treatment planning, while at the same time, they identify factors that treatment planners can consider as they seek ways to enhance incentives and help adolescents with substance use disorders attain positive outcomes.

Maternal Stress Predicted by Characteristics of Children with Autism Spectrum Disorder and Intellectual Disability

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Peters-Scheffer, Nienke; Didden, Robert; Korzilius, Hubert

2012-01-01

To determine maternal stress and child variables predicting maternal stress, 104 mothers of children with autism spectrum disorder (ASD) and intellectual disability (ID) completed the Dutch version of the Parental Stress Index (PSI; De Brock, Vermulst, Gerris, & Abidin, 1992) every six months over a period of two years. The level of maternal…

Personality predicts drop-out from therapist-guided internet-based cognitive behavioural therapy for eating disorders. Results from a randomized controlled trial

Directory of Open Access Journals (Sweden)

Louise Högdahl

2016-09-01

Full Text Available Internet-based guided self-help cognitive behavioural therapy (ICBT seems a promising way of delivering eating disorder treatment. However, treatment drop-out is a common problem and little is known about the correlates, especially in clinical settings. The study aimed to explore prediction of drop-out in the context of a randomized controlled trial within specialized eating disorder care in terms of eating disorder symptomatology, personality traits, comorbidity, and demographic characteristics. 109 outpatients diagnosed with bulimia nervosa or similar eating disorder were randomized to two types of ICBT. Participants were assessed with several clinical- and self-ratings. The average drop-out rate was 36%. Drop-out was predicted by lower scores in the personality traits Dutifulness and Assertiveness as measured by the NEO Personality Inventory Revised, and by higher scores in Self-affirm as measured by the Structural Analysis of Social Behaviour. Drop-out was also predicted by therapist factors: one therapist had significantly more drop-outs (82% than the other three (M = 30%. Theoretical and clinical implications of the impact of the predictors are discussed.

Nicotine dependence predicts cannabis use disorder symptoms among adolescents and young adults.

Science.gov (United States)

Dierker, Lisa; Braymiller, Jessica; Rose, Jennifer; Goodwin, Renee; Selya, Arielle

2018-06-01

We evaluate if cigarette smoking and/or nicotine dependence predicts cannabis use disorder symptoms among adolescent and young adult cannabis users and whether the relationships differ based on frequency of cannabis use. Data were drawn from seven annual surveys of the NSDUH to include adolescents and young adults (age 12-21) who reported using cannabis at least once in the past 30 days (n = 21,928). Cannabis use frequency trends in the association between cigarette smoking, nicotine dependence and cannabis use disorder symptoms were assessed using Varying Coefficient Models (VCM's). Over half of current cannabis users also smoked cigarettes in the past 30 days (54.7% SE 0.48). Cigarette smoking in the past 30 days was associated with earlier onset of cannabis use, more frequent cannabis use and a larger number of cannabis use disorder symptoms compared to those who did not smoke cigarettes. After statistical control for socio-demographic characteristics and other substance use behaviors, nicotine dependence but not cigarette smoking quantity or frequency was positively and significantly associated with each of the cannabis use disorder symptoms as well as the total number of cannabis symptoms endorsed. Higher nicotine dependence scores were consistently associated with the cannabis use disorder symptoms across all levels of cannabis use from 1 day used (past month) to daily cannabis use, though the relationship was strongest among infrequent cannabis users. Prevention and treatment efforts should consider cigarette smoking comorbidity when addressing the increasing proportion of the US population that uses cannabis. Copyright © 2018 Elsevier B.V. All rights reserved.

Nonverbal interpersonal attunement and extravert personality predict outcome of light treatment in seasonal affective disorder

NARCIS (Netherlands)

Geerts, E; Kouwert, E; Bouhuys, N; Meesters, Y; Jansen, J

We investigated whether personality and nonverbal interpersonal processes can predict the subsequent response to light treatment in seasonal affective disorder (SAD) patients. In 60 SAD patients, Neuroticism and Extraversion were assessed prior to light treatment (4 days with 30 min of 10.000 lux).

Are signs of temporomandibular disorders stable and predictable in adolescents with headache?

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Liljeström, M-R; Le Bell, Y; Laimi, K; Anttila, P; Aromaa, M; Jämsä, T; Metsähonkala, L; Vahlberg, T; Viander, S; Alanen, P; Sillanpää, M

2008-06-01

The aim of the present study was to study changes in signs and symptoms of temporomandibular disorders (TMD) and factors predicting TMD signs in adolescents with and without headache. A population-based sample (n = 212) of 13-year-olds with and without headache was re-examined at the age of 16. The study included a questionnaire, face-to-face interview and somatic examination. In addition, a neurological examination, a muscle evaluation and a stomatognathic examination were performed. Significant changes were seen in TMD signs during the follow-up, but TMD signs at the end of the follow-up could not be predicted by baseline headache, sleeping difficulties, depression or muscle pain. TMD signs at the age of 16 were associated with female gender and muscle pain. We conclude that considerable changes in TMD signs occur in the follow-up of adolescents with and without headache. Headache-related TMD are not predictable in adolescents with and without headache.

Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

Directory of Open Access Journals (Sweden)

Sun Zhangzhen

2012-08-01

Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

Functional Anthology of Intrinsic Disorder. I. Biological Processes and Functions of Proteins with Long Disordered Regions

Science.gov (United States)

Xie, Hongbo; Vucetic, Slobodan; Iakoucheva, Lilia M.; Oldfield, Christopher J.; Dunker, A. Keith; Uversky, Vladimir N.; Obradovic, Zoran

2008-01-01

Identifying relationships between function, amino acid sequence and protein structure represents a major challenge. In this study we propose a bioinformatics approach that identifies functional keywords in the Swiss-Prot database that correlate with intrinsic disorder. A statistical evaluation is employed to rank the significance of these correlations. Protein sequence data redundancy and the relationship between protein length and protein structure were taken into consideration to ensure the quality of the statistical inferences. Over 200,000 proteins from Swiss-Prot database were analyzed using this approach. The predictions of intrinsic disorder were carried out using PONDR VL3E predictor of long disordered regions that achieves an accuracy of above 86%. Overall, out of the 710 Swiss-Prot functional keywords that were each associated with at least 20 proteins, 238 were found to be strongly positively correlated with predicted long intrinsically disordered regions, whereas 302 were strongly negatively correlated with such regions. The remaining 170 keywords were ambiguous without strong positive or negative correlation with the disorder predictions. These functions cover a large variety of biological activities and imply that disordered regions are characterized by a wide functional repertoire. Our results agree well with literature findings, as we were able to find at least one illustrative example of functional disorder or order shown experimentally for the vast majority of keywords showing the strongest positive or negative correlation with intrinsic disorder. This work opens a series of three papers, which enriches the current view of protein structure-function relationships, especially with regards to functionalities of intrinsically disordered proteins and provides researchers with a novel tool that could be used to improve the understanding of the relationships between protein structure and function. The first paper of the series describes our statistical

Take charge: Personality as predictor of recovery from eating disorder.

Science.gov (United States)

Levallius, Johanna; Roberts, Brent W; Clinton, David; Norring, Claes

2016-12-30

Many treatments for eating disorders (ED) have demonstrated success. However, not all patients respond the same to interventions nor achieve full recovery, and obvious candidates like ED diagnosis and symptoms have generally failed to explain this variability. The current study investigated the predictive utility of personality for outcome in ED treatment. One hundred and thirty adult patients with bulimia nervosa or eating disorder not otherwise specified enrolled in an intensive multimodal treatment for 16 weeks. Personality was assessed with the NEO Personality Inventory Revised (NEO PI-R). Outcome was defined as recovered versus still ill and also as symptom score at termination with the Eating Disorder Inventory-2 (EDI-2). Personality significantly predicted both recovery (70% of patients) and symptom improvement. Patients who recovered reported significantly higher levels of Extraversion at baseline than the still ill, and Assertiveness emerged as the personality trait best predicting variance in outcome. This study indicates that personality might hold promise as predictor of recovery after treatment for ED. Future research might investigate if adding interventions to address personality features improves outcome for ED patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

External Validation and Update of a Prediction Rule for the Duration of Sickness Absence Due to Common Mental Disorders

NARCIS (Netherlands)

Norder, Giny; Roelen, Corne A. M.; van der Klink, Jac J. L.; Bultmann, Ute; Sluiter, J. K.; Nieuwenhuijsen, K.

Purpose The objective of the present study was to validate an existing prediction rule (including age, education, depressive/anxiety symptoms, and recovery expectations) for predictions of the duration of sickness absence due to common mental disorders (CMDs) and investigate the added value of

Rapid Eye Movement Sleep Behavior Disorder in Paraneoplastic Cerebellar Degeneration: Improvement with Immunotherapy.

Science.gov (United States)

Vale, Thiago Cardoso; Fernandes do Prado, Lucila Bizari; do Prado, Gilmar Fernandes; Povoas Barsottini, Orlando Graziani; Pedroso, José Luiz

2016-01-01

To report two female patients with paraneoplastic cerebellar degeneration (PCD) related to breast cancer that presented with rapid eye movement-sleep behavior disorder (RBD) and improved sleep symptoms with immunotherapy. The two patients were evaluated through clinical scale and polysomnography before and after therapy with intravenous immunoglobulin. RBD was successfully treated with immunotherapy in both patients. Score on the RBD screening questionnaire dropped from 10 to 1 or 0, allied with the normalization of polysomnographic findings. A marked improvement in RBD after immunotherapy in PCD raises the hypothesis that secondary RBD may be an immune-mediated sleep disorder. © 2016 Associated Professional Sleep Societies, LLC.

Emotion regulation and Residual Depression Predict Psychosocial Functioning in Bipolar Disorder: Preliminary Study

OpenAIRE

Becerra, Rodrigo; Cruise, Kate; Harms, Craig; Allan, Alfred; Bassett, Darryl; Hood, Sean; Murray, Greg

2015-01-01

This study explores the predictive value of various clinical, neuropsychological, functional, and emotion regulation processes for recovery in Bipolar Disorder. Clinical and demographic information was collected for 27 euthymic or residually depressed BD participants. Seventy one percent of the sample reported some degree of impairment in psychosocial functioning. Both residual depression and problems with emotion regulation were identified as significant predictors of poor psychosocial funct...

Hypomanic symptoms predict an increase in narcissistic and histrionic personality disorder features in suicidal young adults.

Science.gov (United States)

Shahar, Golan; Scotti, Margaret-Ann; Rudd, M David; Joiner, Thomas E

2008-01-01

Consistent with the "scar hypothesis", according to which mood depression might impact personality, we examined the effect of unipolar and hypomanic mood disturbances on cluster B (i.e., narcissistic, histrionic, and borderline) personality disorder features. Data from 113 suicidal young adults were utilized, and cross-lagged associations between unipolar and hypomanic mood disturbances and cluster B personality disorder features were examined using manifest-variable structural equation modeling (SEM). Hypomanic symptoms predicted an increase in narcissistic and histrionic personality disorder features over the Time 1-Time 2 period, as well as an increase in narcissistic personality disorder features over the Time 1-Time 3 period. Unipolar depressive symptoms and borderline features were reciprocally and longitudinally associated, albeit at different time periods. The sample distinct features restrict generalization of the findings. An exclusive use of self-report measures might have contributed to shared method variance. Results are consistent with the notion that hypomanic symptoms increase narcissistic personality disorder tendencies. Depression and Anxiety, 2008. (c) 2007 Wiley-Liss, Inc.

Risk factors predict post-traumatic stress disorder differently in men and women

Directory of Open Access Journals (Sweden)

Elklit Ask

2008-11-01

Full Text Available Abstract Background About twice as many women as men develop post-traumatic stress disorder (PTSD, even though men as a group are exposed to more traumatic events. Exposure to different trauma types does not sufficiently explain why women are more vulnerable. Methods The present work examines the effect of age, previous trauma, negative affectivity (NA, anxiety, depression, persistent dissociation, and social support on PTSD separately in men and women. Subjects were exposed to either a series of explosions in a firework factory near a residential area or to a high school stabbing incident. Results Some gender differences were found in the predictive power of well known risk factors for PTSD. Anxiety predicted PTSD in men, but not in women, whereas the opposite was found for depression. Dissociation was a better predictor for PTSD in women than in men in the explosion sample but not in the stabbing sample. Initially, NA predicted PTSD better in women than men in the explosion sample, but when compared only to other significant risk factors, it significantly predicted PTSD for both men and women in both studies. Previous traumatic events and age did not significantly predict PTSD in either gender. Conclusion Gender differences in the predictive value of social support on PTSD appear to be very complex, and no clear conclusions can be made based on the two studies included in this article.

Cognitive-behavioral therapy for obsessive–compulsive disorder: access to treatment, prediction of long-term outcome with neuroimaging

Directory of Open Access Journals (Sweden)

O’Neill J

2015-07-01

Full Text Available Joseph O'Neill,1 Jamie D Feusner,2 1Division of Child Psychiatry, 2Division of Adult Psychiatry, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA Abstract: This article reviews issues related to a major challenge to the field for obsessive–compulsive disorder (OCD: improving access to cognitive-behavioral therapy (CBT. Patient-related barriers to access include the stigma of OCD and reluctance to take on the demands of CBT. Patient-external factors include the shortage of trained CBT therapists and the high costs of CBT. The second half of the review focuses on one partial, yet plausible aid to improve access – prediction of long-term response to CBT, particularly using neuroimaging methods. Recent pilot data are presented revealing a potential for pretreatment resting-state functional magnetic resonance imaging and magnetic resonance spectroscopy of the brain to forecast OCD symptom severity up to 1 year after completing CBT. Keywords: follow-up, access to treatment, relapse, resting-state fMRI, magnetic resonance spectroscopy

Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

DEFF Research Database (Denmark)

Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

Prognosis of patients with whiplash-associated disorders consulting physiotherapy: development of a predictive model for recovery

OpenAIRE

Bohman, Tony; C?t?, Pierre; Boyle, Eleanor; Cassidy, J David; Carroll, Linda J; Skillgate, Eva

2012-01-01

Abstract Background Patients with whiplash-associated disorders (WAD) have a generally favourable prognosis, yet some develop longstanding pain and disability. Predicting who will recover from WAD shortly after a traffic collision is very challenging for health care providers such as physical therapists. Therefore, we aimed to develop a prediction model for the recovery of WAD in a cohort of patients who consulted physical therapists within six weeks after the injury. Methods Our cohort inclu...

NOAA's Strategy to Improve Operational Weather Prediction Outlooks at Subseasonal Time Range

Science.gov (United States)

Schneider, T.; Toepfer, F.; Stajner, I.; DeWitt, D.

2017-12-01

NOAA is planning to extend operational global numerical weather prediction to sub-seasonal time range under the auspices of its Next Generation Global Prediction System (NGGPS) and Extended Range Outlook Programs. A unification of numerical prediction capabilities for weather and subseasonal to seasonal (S2S) timescales is underway at NOAA using the Finite Volume Cubed Sphere (FV3) dynamical core as the basis for the emerging unified system. This presentation will overview NOAA's strategic planning and current activities to improve prediction at S2S time-scales that are ongoing in response to the Weather Research and Forecasting Innovation Act of 2017, Section 201. Over the short-term, NOAA seeks to improve the operational capability through improvements to its ensemble forecast system to extend its range to 30 days using the new FV3 Global Forecast System model, and by using this system to provide reforecast and re-analyses. In parallel, work is ongoing to improve NOAA's operational product suite for 30 day outlooks for temperature, precipitation and extreme weather phenomena.

Sleep duration, but not insomnia, predicts the 2-year course of depressive and anxiety disorders

NARCIS (Netherlands)

van Mill, Josine G; Vogelzangs, Nicole; van Someren, Eus J W; Hoogendijk, Witte J G; Penninx, Brenda W J H

OBJECTIVE: To examine the predictive role of insomnia and sleep duration on the 2-year course of depressive and anxiety disorders. METHOD: This study is a secondary data analysis based on data from the baseline (2004-2007) and 2-year assessment of the Netherlands Study of Depression and Anxiety.

Sleep Duration, but Not Insomnia, Predicts the 2-Year Course of Depressive and Anxiety Disorders

NARCIS (Netherlands)

van Mill, Josine G.; Vogelzangs, Nicole; van Someren, Eus J. W.; Hoogendijk, Witte J. G.; Penninx, Brenda W. J. H.

Objective: To examine the predictive role of insomnia and sleep duration on the 2-year course of depressive and anxiety disorders. Method: This study is a secondary data analysis based on data from the baseline (2004-2007) and 2-year assessment of the Netherlands Study of Depression and Anxiety.

Prefrontal Cortex Structure Predicts Training-Induced Improvements in Multitasking Performance.

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Verghese, Ashika; Garner, K G; Mattingley, Jason B; Dux, Paul E

2016-03-02

The ability to perform multiple, concurrent tasks efficiently is a much-desired cognitive skill, but one that remains elusive due to the brain's inherent information-processing limitations. Multitasking performance can, however, be greatly improved through cognitive training (Van Selst et al., 1999, Dux et al., 2009). Previous studies have examined how patterns of brain activity change following training (for review, see Kelly and Garavan, 2005). Here, in a large-scale human behavioral and imaging study of 100 healthy adults, we tested whether multitasking training benefits, assessed using a standard dual-task paradigm, are associated with variability in brain structure. We found that the volume of the rostral part of the left dorsolateral prefrontal cortex (DLPFC) predicted an individual's response to training. Critically, this association was observed exclusively in a task-specific training group, and not in an active-training control group. Our findings reveal a link between DLPFC structure and an individual's propensity to gain from training on a task that taps the limits of cognitive control. Cognitive "brain" training is a rapidly growing, multibillion dollar industry (Hayden, 2012) that has been touted as the panacea for a variety of disorders that result in cognitive decline. A key process targeted by such training is "cognitive control." Here, we combined an established cognitive control measure, multitasking ability, with structural brain imaging in a sample of 100 participants. Our goal was to determine whether individual differences in brain structure predict the extent to which people derive measurable benefits from a cognitive training regime. Ours is the first study to identify a structural brain marker-volume of left hemisphere dorsolateral prefrontal cortex-associated with the magnitude of multitasking performance benefits induced by training at an individual level. Copyright © 2016 the authors 0270-6474/16/362638-08$15.00/0.

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

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Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction.

Science.gov (United States)

Chen, Chia-Yen; Han, Jiali; Hunter, David J; Kraft, Peter; Price, Alkes L

2015-09-01

Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction. © 2015 WILEY PERIODICALS, INC.

A clinical study of autogenic training-based behavioral treatment for panic disorder.

Science.gov (United States)

Sakai, M

1996-03-01

The present study investigated the effect of autogenic training-based behavioral treatment for panic disorder and identified the predictors of treatment outcome. Thirty-four patients meeting DSM-III-R criteria for panic disorder received autogenic training-based behavioral treatment from October 1981 to December 1994. They were treated individually by the author. The medical records of the patients were investigated for the purpose of this study. The results showed that this autogenic training-based behavioral treatment had successful results. Fifteen patients were cured, nine much improved, five improved, and five unchanged at the end of the treatment. Improvement trends were found as for the severity of panic attack and the severity of agoraphobic avoidance. No consistent findings about predictors emerged when such pretreatment variables as demographics and severity of symptoms were used to predict the outcome. Also, three treatment variables showed useful predictive power. First, practicing the second standard autogenic training exercise satisfactorily predicted better outcomes. Second, application of in vivo exposure was found to be positively associated with the treatment outcome in patients with agoraphobic avoidance. Third, longer treatment periods were associated with better outcomes. These findings suggested that the autogenic training-based behavioral treatment could provide relief to the majority of panic disorder patients.

Therapeutic improvements expected in the near future for schizophrenia and schizoaffective disorder

DEFF Research Database (Denmark)

Garay, Ricardo P; Citrome, Leslie; Samalin, Ludovic

2016-01-01

INTRODUCTION: In this review, the authors describe medications in phase III of clinical development for schizophrenia and schizoaffective disorder, and provide an opinion on how current treatment can be improved in the near future. Areas covered: Recent (post 2013) phase III clinical trials...... and schizoaffective disorder. In addition to better-tolerated antipsychotics that treat positive symptoms, we could see the arrival of the first effective drug for negative symptoms and CIAS, which would strongly facilitate the ultimate goal of recovery in persons with schizophrenia....

Predicting Receptive-Expressive Vocabulary Discrepancies in Preschool Children With Autism Spectrum Disorder.

Science.gov (United States)

McDaniel, Jena; Yoder, Paul; Woynaroski, Tiffany; Watson, Linda R

2018-05-15

Correlates of receptive-expressive vocabulary size discrepancies may provide insights into why language development in children with autism spectrum disorder (ASD) deviates from typical language development and ultimately improve intervention outcomes. We indexed receptive-expressive vocabulary size discrepancies of 65 initially preverbal children with ASD (20-48 months) to a comparison sample from the MacArthur-Bates Communicative Development Inventories Wordbank (Frank, Braginsky, Yurovsky, & Marchman, 2017) to quantify typicality. We then tested whether attention toward a speaker and oral motor performance predict typicality of the discrepancy 8 months later. Attention toward a speaker correlated positively with receptive-expressive vocabulary size discrepancy typicality. Imitative and nonimitative oral motor performance were not significant predictors of vocabulary size discrepancy typicality. Secondary analyses indicated that midpoint receptive vocabulary size mediated the association between initial attention toward a speaker and end point receptive-expressive vocabulary size discrepancy typicality. Findings support the hypothesis that variation in attention toward a speaker might partially explain receptive-expressive vocabulary size discrepancy magnitude in children with ASD. Results are consistent with an input-processing deficit explanation of language impairment in this clinical population. Future studies should test whether attention toward a speaker is malleable and causally related to receptive-expressive discrepancies in children with ASD.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

Science.gov (United States)

Khodayari-Rostamabad, Ahmad; Reilly, James P; Hasey, Gary M; de Bruin, Hubert; Maccrimmon, Duncan J

2013-10-01

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure. A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%]. These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG. The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

SCREENING FOR PERSONALITY DISORDERS

Science.gov (United States)

Morse, Jennifer Q.; Pilkonis, Paul A.

2010-01-01

A brief but valid self-report measure to screen for personality disorders (PDs) would be a valuable tool in making decisions about further assessment and in planning optimal treatments. In psychiatric and nonpsychiatric samples, we compared the validity of three screening measures: the PD scales from the Inventory of Interpersonal Problems, a self-report version of the Iowa Personality Disorder Screen, and the self-directedness scale of the Temperament and Character Inventory. Despite their different theoretical origins, the screeners were highly correlated in a range from .71 to .77. As a result, the use of multiple screeners was not a significant improvement over any individual screener, and no single screener stood out as clearly superior to the others. Each performed modestly in predicting the presence of any PD diagnosis in both the psychiatric and nonpsychiatric groups. Performance was best when predicting a more severe PD diagnosis in the psychiatric sample. The results also highlight the potential value of multiple assessments when relying on self-reports. PMID:17492920

ProteinSplit: splitting of multi-domain proteins using prediction of ordered and disordered regions in protein sequences for virtual structural genomics

International Nuclear Information System (INIS)

Wyrwicz, Lucjan S; Koczyk, Grzegorz; Rychlewski, Leszek; Plewczynski, Dariusz

2007-01-01

The annotation of protein folds within newly sequenced genomes is the main target for semi-automated protein structure prediction (virtual structural genomics). A large number of automated methods have been developed recently with very good results in the case of single-domain proteins. Unfortunately, most of these automated methods often fail to properly predict the distant homology between a given multi-domain protein query and structural templates. Therefore a multi-domain protein should be split into domains in order to overcome this limitation. ProteinSplit is designed to identify protein domain boundaries using a novel algorithm that predicts disordered regions in protein sequences. The software utilizes various sequence characteristics to assess the local propensity of a protein to be disordered or ordered in terms of local structure stability. These disordered parts of a protein are likely to create interdomain spacers. Because of its speed and portability, the method was successfully applied to several genome-wide fold annotation experiments. The user can run an automated analysis of sets of proteins or perform semi-automated multiple user projects (saving the results on the server). Additionally the sequences of predicted domains can be sent to the Bioinfo.PL Protein Structure Prediction Meta-Server for further protein three-dimensional structure and function prediction. The program is freely accessible as a web service at http://lucjan.bioinfo.pl/proteinsplit together with detailed benchmark results on the critical assessment of a fully automated structure prediction (CAFASP) set of sequences. The source code of the local version of protein domain boundary prediction is available upon request from the authors

Interest Level in 2-Year-Olds with Autism Spectrum Disorder Predicts Rate of Verbal, Nonverbal, and Adaptive Skill Acquisition

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Klintwall, Lars; Macari, Suzanne; Eikeseth, Svein; Chawarska, Katarzyna

2015-01-01

Recent studies have suggested that skill acquisition rates for children with autism spectrum disorders receiving early interventions can be predicted by child motivation. We examined whether level of interest during an Autism Diagnostic Observation Schedule assessment at 2?years predicts subsequent rates of verbal, nonverbal, and adaptive skill…

CNNcon: improved protein contact maps prediction using cascaded neural networks.

Directory of Open Access Journals (Sweden)

Wang Ding

Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

Simple Mindreading Abilities Predict Complex Theory of Mind: Developmental Delay in Autism Spectrum Disorders

Science.gov (United States)

Pino, Maria Chiara; Mazza, Monica; Mariano, Melania; Peretti, Sara; Dimitriou, Dagmara; Masedu, Francesco; Valenti, Marco; Franco, Fabia

2017-01-01

Theory of mind (ToM) is impaired in individuals with autism spectrum disorders (ASD). The aims of this study were to: (i) examine the developmental trajectories of ToM abilities in two different mentalizing tasks in children with ASD compared to TD children; and (ii) to assess if a ToM simple test known as eyes-test could predict performance on…

Basic traits predict the prevalence of personality disorder across the life span: the example of psychopathy.

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Vachon, David D; Lynam, Donald R; Widiger, Thomas A; Miller, Joshua D; McCrae, Robert R; Costa, Paul T

2013-05-01

Personality disorders (PDs) may be better understood in terms of dimensions of general personality functioning rather than as discrete categorical conditions. Personality-trait descriptions of PDs are robust across methods and settings, and PD assessments based on trait measures show good construct validity. The study reported here extends research showing that basic traits (e.g., impulsiveness, warmth, straightforwardness, modesty, and deliberation) can re-create the epidemiological characteristics associated with PDs. Specifically, we used normative changes in absolute trait levels to simulate age-related differences in the prevalence of psychopathy in a forensic setting. Results demonstrated that trait information predicts the rate of decline for psychopathy over the life span; discriminates the decline of psychopathy from that of a similar disorder, antisocial PD; and accurately predicts the differential decline of subfactors of psychopathy. These findings suggest that basic traits provide a parsimonious account of PD prevalence across the life span.

The role of attachment in predicting CBT treatment outcome in children with anxiety disorders

DEFF Research Database (Denmark)

Walczak, Monika Anna; Normann, Nicoline; Tolstrup, Marie

2015-01-01

Introduction: Child’s insecure attachment to parents and insecure parental attachment has been linked to childhood anxiety (Brumariu & Kerns, 2010; Manassis et al.,1994).Whether attachment patterns can predict treatment outcome, is yet to be investigated. We examined the role of children......’s attachment to parents, and parental attachment in predicting treatment outcome in anxious children receiving cognitive-behavioral treatment. Method: A total of 69 children aged 7-13 years were diagnosed at intake and post-treatment, using Anxiety Disorders Interview Schedule for DSM-IV (Silverman and Albano...... style in responders and non-responders in the present sample. We found a significant difference in maternal attachment anxiety scale (p=.011), with mothers of non-responders showing significantly higher attachment anxiety. Binominal logistic regression analysis was used to measure a predictive value...

The executive control network and symptomatic improvement in attention-deficit/hyperactivity disorder

NARCIS (Netherlands)

Francx, Winke; Oldehinkel, Marianne; Oosterlaan, Jaap; Heslenfeld, Dirk; Hartman, Catharina A.; Hoekstra, Pieter J.; Franke, Barbara; Beckmann, Christian F.; Buitelaar, Jan K.; Mennes, Maarten

2015-01-01

Background: One neurodevelopmental theory hypothesizes remission of attention-deficit/hyperactivity disorder (ADHD) to result from improved prefrontal top-down control, while ADHD, independent of the current diagnosis, is characterized by stable non-cortical deficits (Halperin & Schulz, 2006). We

Specific cognitive-neurophysiological processes predict impulsivity in the childhood attention-deficit/hyperactivity disorder combined subtype.

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Bluschke, A; Roessner, V; Beste, C

2016-04-01

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neuropsychiatric disorders in childhood. Besides inattention and hyperactivity, impulsivity is the third core symptom leading to diverse and serious problems. However, the neuronal mechanisms underlying impulsivity in ADHD are still not fully understood. This is all the more the case when patients with the ADHD combined subtype (ADHD-C) are considered who are characterized by both symptoms of inattention and hyperactivity/impulsivity. Combining high-density electroencephalography (EEG) recordings with source localization analyses, we examined what information processing stages are dysfunctional in ADHD-C (n = 20) compared with controls (n = 18). Patients with ADHD-C made more impulsive errors in a Go/No-go task than healthy controls. Neurophysiologically, different subprocesses from perceptual gating to attentional selection, resource allocation and response selection processes are altered in this patient group. Perceptual gating, stimulus-driven attention selection and resource allocation processes were more pronounced in ADHD-C, are related to activation differences in parieto-occipital networks and suggest attentional filtering deficits. However, only response selection processes, associated with medial prefrontal networks, predicted impulsive errors in ADHD-C. Although the clinical picture of ADHD-C is complex and a multitude of processing steps are altered, only a subset of processes seems to directly modulate impulsive behaviour. The present findings improve the understanding of mechanisms underlying impulsivity in patients with ADHD-C and might help to refine treatment algorithms focusing on impulsivity.

Improving Empathic Communication Skills in Adults with Autism Spectrum Disorder.

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Kern Koegel, Lynn; Ashbaugh, Kristen; Navab, Anahita; Koegel, Robert L

2016-03-01

The literature suggests that many individuals diagnosed with Autism Spectrum Disorder (ASD) experience challenges with recognizing and describing emotions in others, which may result in difficulties with the verbal expression of empathy during communication. Thus, there is a need for intervention techniques targeting this area. Using a multiple baseline across participants design, this study examined the effectiveness of a video-feedback intervention with a visual framework component to improve verbal empathetic statements and questions during conversation for adults with ASD. Following intervention, all participants improved in verbal expression of empathetic statements and empathetic questions during conversation with generalization and maintenance of gains. Furthermore, supplemental assessments indicated that each participant improved in their general level of empathy and confidence in communication skills.

Improving Flash Flood Prediction in Multiple Environments

Science.gov (United States)

Broxton, P. D.; Troch, P. A.; Schaffner, M.; Unkrich, C.; Goodrich, D.; Wagener, T.; Yatheendradas, S.

2009-12-01

Flash flooding is a major concern in many fast responding headwater catchments . There are many efforts to model and to predict these flood events, though it is not currently possible to adequately predict the nature of flash flood events with a single model, and furthermore, many of these efforts do not even consider snow, which can, by itself, or in combination with rainfall events, cause destructive floods. The current research is aimed at broadening the applicability of flash flood modeling. Specifically, we will take a state of the art flash flood model that is designed to work with warm season precipitation in arid environments, the KINematic runoff and EROSion model (KINEROS2), and combine it with a continuous subsurface flow model and an energy balance snow model. This should improve its predictive capacity in humid environments where lateral subsurface flow significantly contributes to streamflow, and it will make possible the prediction of flooding events that involve rain-on-snow or rapid snowmelt. By modeling changes in the hydrologic state of a catchment before a flood begins, we can also better understand the factors or combination of factors that are necessary to produce large floods. Broadening the applicability of an already state of the art flash flood model, such as KINEROS2, is logical because flash floods can occur in all types of environments, and it may lead to better predictions, which are necessary to preserve life and property.

Predictive Maintenance: One key to improved power plant availability

International Nuclear Information System (INIS)

Mobley; Allen, J.W.

1986-01-01

Recent developments in microprocessor technology has provided the ability to routinely monitor the actual mechanical condition of all rotating and reciprocating machinery and process variables (i.e. pressure, temperature, flow, etc.) of other process equipment within an operating electric power generating plant. This direct correlation between frequency domain vibration and actual mechanical condition of machinery and trending process variables of non-rotating equipment can provide the ''key'' to improving the availability and reliability, thermal efficiency and provide the baseline information necessary for developing a realistic plan for extending the useful life of power plants. The premise of utilizing microprocessor-based Predictive Maintenance to improve power plant operation has been proven by a number of utilities. This paper provides a comprehensive discussion of the TEC approach to Predictive Maintenance and examples of successful programs

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

Science.gov (United States)

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach

Directory of Open Access Journals (Sweden)

Christensen Helen

2009-11-01

Full Text Available Abstract Background Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. Methods The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. Results The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. Conclusion The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Usefulness of the Mini Nutritional Assessment (MNA) in predicting the nutritional status of people with mental disorders in Taiwan.

Science.gov (United States)

Tsai, Alan C; Chou, Yuan-Ti; Chang, Tsui-Lan

2011-02-01

The study was to evaluate the ability of the Mini Nutritional Assessment in predicting malnutrition in people with three subtypes of mental disorder (schizophrenia, major depression and bipolar disorder) in Taiwan. The study involved a convenience sample of 120 residents of psychiatric wards managed by a hospital in central Taiwan (52 with schizophrenia, 36 with major depression and 32 with bipolar disorder) classified according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria. A structured questionnaire elicited subjects' personal data, disease history and answers to questions in the Mini Nutritional Assessment. Serum and anthropometrical parameters were measured. Nutritional status was evaluated with a content-equivalent version of the Mini Nutritional Assessment (Taiwan version-1, T1). The Mini Nutritional Assessment-Taiwan version-1 was effective in assessing the nutritional status of people of all three subtypes of disorder. Nutritional statuses predicted with the Mini Nutritional Assessment-Taiwan version-1 agreed well with other nutritional indicators such as BMI, waist circumference and appetite status. According to the Mini Nutritional Assessment-Taiwan version-1, people with major depression were more likely to be at risk of undernutrition, whereas people with schizophrenia or bipolar disorder were more likely to be at risk of overnutrition. The Mini Nutritional Assessment-Taiwan version-1 can effectively grade both undernutrition and overnutrition of people with schizophrenia, major depression or bipolar disorder. The Mini Nutritional Assessment enables nurses to monitor emerging nutritional problems in people with psychiatric disorder without relying on subjective judgement. With proper intervention, it can help reduce nutrition-related chronic conditions in these individuals and save on healthcare cost. © 2011 Blackwell Publishing Ltd.

Post-traumatic stress disorder and cardiometabolic disease: improving causal inference to inform practice.

Science.gov (United States)

Koenen, K C; Sumner, J A; Gilsanz, P; Glymour, M M; Ratanatharathorn, A; Rimm, E B; Roberts, A L; Winning, A; Kubzansky, L D

2017-01-01

Post-traumatic stress disorder (PTSD) has been declared 'a life sentence' based on evidence that the disorder leads to a host of physical health problems. Some of the strongest empirical research - in terms of methodology and findings - has shown that PTSD predicts higher risk of cardiometabolic diseases, specifically cardiovascular disease (CVD) and type 2 diabetes (T2D). Despite mounting evidence, PTSD is not currently acknowledged as a risk factor by cardiovascular or endocrinological medicine. This view is unlikely to change absent compelling evidence that PTSD causally contributes to cardiometabolic disease. This review suggests that with developments in methods for epidemiological research and the rapidly expanding knowledge of the behavioral and biological effects of PTSD the field is poised to provide more definitive answers to questions of causality. First, we discuss methods to improve causal inference using the observational data most often used in studies of PTSD and health, with particular reference to issues of temporality and confounding. Second, we consider recent work linking PTSD with specific behaviors and biological processes, and evaluate whether these may plausibly serve as mechanisms by which PTSD leads to cardiometabolic disease. Third, we evaluate how looking more comprehensively into the PTSD phenotype provides insight into whether specific aspects of PTSD phenomenology are particularly relevant to cardiometabolic disease. Finally, we discuss new areas of research that are feasible and could enhance understanding of the PTSD-cardiometabolic relationship, such as testing whether treatment of PTSD can halt or even reverse the cardiometabolic risk factors causally related to CVD and T2D.

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

Science.gov (United States)

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

Benthic Light Availability Improves Predictions of Riverine Primary Production

Science.gov (United States)

Kirk, L.; Cohen, M. J.

2017-12-01

Light is a fundamental control on photosynthesis, and often the only control strongly correlated with gross primary production (GPP) in streams and rivers; yet it has received far less attention than nutrients. Because benthic light is difficult to measure in situ, surrogates such as open sky irradiance are often used. Several studies have now refined methods to quantify canopy and water column attenuation of open sky light in order to estimate the amount of light that actually reaches the benthos. Given the additional effort that measuring benthic light requires, we should ask if benthic light always improves our predictions of GPP compared to just open sky irradiance. We use long-term, high-resolution dissolved oxygen, turbidity, dissolved organic matter (fDOM), and irradiance data from streams and rivers in north-central Florida, US across gradients of size and color to build statistical models of benthic light that predict GPP. Preliminary results on a large, clear river show only modest model improvements over open sky irradiance, even in heavily canopied reaches with pulses of tannic water. However, in another spring-fed river with greater connectivity to adjacent wetlands - and hence larger, more frequent pulses of tannic water - the model improved dramatically with the inclusion of fDOM (model R2 improved from 0.28 to 0.68). River shade modeling efforts also suggest that knowing benthic light will greatly enhance our ability to predict GPP in narrower, forested streams flowing in particular directions. Our objective is to outline conditions where an assessment of benthic light conditions would be necessary for riverine metabolism studies or management strategies.

A Study of the Predictive Validity of the Children's Depression Inventory for Major Depression Disorder in Puerto Rican Adolescents

Science.gov (United States)

Rivera-Medina, Carmen L.; Bernal, Guillermo; Rossello, Jeannette; Cumba-Aviles, Eduardo

2010-01-01

This study aims to evaluate the predictive validity of the Children's Depression Inventory items for major depression disorder (MDD) in an outpatient clinic sample of Puerto Rican adolescents. The sample consisted of 130 adolescents, 13 to 18 years old. The five most frequent symptoms of the Children's Depression Inventory that best predict the…

Which dimension of parenting predicts the change of callous unemotional traits in children with disruptive behavior disorder?

Science.gov (United States)

Muratori, Pietro; Lochman, John E; Lai, Elisa; Milone, Annarita; Nocentini, Annalaura; Pisano, Simone; Righini, Elisabetta; Masi, Gabriele

2016-08-01

Several studies suggested that in addition to child-driven factors (i.e., temperamental style), parenting behavior can, at least in part, influence the maintenance of Callous Unemotional (CU) traits in children; however, more information is needed to distinguish which styles (negative parenting or lack of positive parenting) predict increased levels of CU traits. The aim of the present treatment study was to examine which components of parenting are longitudinally associated with levels of CU traits in children with a disruptive behavior disorder diagnosis. The current study examined cross-lagged reciprocal effects models between positive and negative parenting practices, and the levels of child CU traits over three time points, including both positive and negative dimensions of parenting in the same model. Participants were 126 Italian children with diagnosis of disruptive behavior disorder (oppositional defiant disorder or conduct disorder), 113 boys and 13 girls, 110 Caucasian, 48 with conduct disorder, and 78 with oppositional defiant disorder, treated with a multi-component intervention, based on cognitive behavioral principles and practices. Participants were all 9-10 years of age at the beginning of the study, and were followed-up until the age of 11-12 years (24 months in total, the first 12 under treatment) using parent report (Alabama Parenting Questionnaire and Child Behavior Check List) and child report (Inventory of Callous-Unemotional Traits) measures. No significant cross-lagged path was found between negative parenting and CU traits; these two variables were also unrelated when positive parenting was considered in the same model. In contrast, reciprocal effects between positive parenting and CU were found: higher levels of positive parenting predicted lower levels of CU traits. The current findings suggest that the positive dimension of parenting may need to be targeted in the treatment of DBD children with higher CU traits. Copyright © 2016

Predicting Clinical Outcomes and Lost Work in Patients with Work-Related Upper Extremity Disorders

Science.gov (United States)

1998-02-13

a double-sided message which can promote 24 somatization and exaggerated "pain behavior" which is then often interpreted as evidence ofrnalingering...specific psychiatric populations this may have some value (e.g... hypochondriasis , somati7ation disorder), it may not be an appropriate indicator for...subscale specificaDy measuring somatic complaintc;.was not significantly predictive ofsubjective report ofback injury. In addition, studies ofworkers with

The Coronary Health Improvement Projects Impact on Lowering Eating, Sleep, Stress, and Depressive Disorders

Science.gov (United States)

Merrill, Ray M.; Aldana, Stephen G.; Greenlaw, Roger L.; Diehl, Hans A.

2008-01-01

Background: The Coronary Health Improvement Project (CHIP) is designed to lower cardiovascular risk factors among a group of generally healthy individuals through health education. Purpose: This study will evaluate the efficacy of the CHIP intervention at improving eating, sleep, stress, and depressive disorders. Methods: A health education…

Predictors of outcome for cognitive behaviour therapy in binge eating disorder.

Science.gov (United States)

Lammers, Mirjam W; Vroling, Maartje S; Ouwens, Machteld A; Engels, Rutger C M E; van Strien, Tatjana

2015-05-01

The aim of this naturalistic study was to identify pretreatment predictors of response to cognitive behaviour therapy in treatment-seeking patients with binge eating disorder (BED; N = 304). Furthermore, we examined end-of-treatment factors that predict treatment outcome 6 months later (N = 190). We assessed eating disorder psychopathology, general psychopathology, personality characteristics and demographic variables using self-report questionnaires. Treatment outcome was measured using the bulimia subscale of the Eating Disorder Inventory 1. Predictors were determined using hierarchical linear regression analyses. Several variables significantly predicted outcome, four of which were found to be both baseline predictors of treatment outcome and end-of-treatment predictors of follow-up: Higher levels of drive for thinness, higher levels of interoceptive awareness, lower levels of binge eating pathology and, in women, lower levels of body dissatisfaction predicted better outcome in the short and longer term. Based on these results, several suggestions are made to improve treatment outcome for BED patients. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

Science.gov (United States)

Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

2010-07-01

PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

Long-time predictability in disordered spin systems following a deep quench.

Science.gov (United States)

Ye, J; Gheissari, R; Machta, J; Newman, C M; Stein, D L

2017-04-01

We study the problem of predictability, or "nature vs nurture," in several disordered Ising spin systems evolving at zero temperature from a random initial state: How much does the final state depend on the information contained in the initial state, and how much depends on the detailed history of the system? Our numerical studies of the "dynamical order parameter" in Edwards-Anderson Ising spin glasses and random ferromagnets indicate that the influence of the initial state decays as dimension increases. Similarly, this same order parameter for the Sherrington-Kirkpatrick infinite-range spin glass indicates that this information decays as the number of spins increases. Based on these results, we conjecture that the influence of the initial state on the final state decays to zero in finite-dimensional random-bond spin systems as dimension goes to infinity, regardless of the presence of frustration. We also study the rate at which spins "freeze out" to a final state as a function of dimensionality and number of spins; here the results indicate that the number of "active" spins at long times increases with dimension (for short-range systems) or number of spins (for infinite-range systems). We provide theoretical arguments to support these conjectures, and also study analytically several mean-field models: the random energy model, the uniform Curie-Weiss ferromagnet, and the disordered Curie-Weiss ferromagnet. We find that for these models, the information contained in the initial state does not decay in the thermodynamic limit-in fact, it fully determines the final state. Unlike in short-range models, the presence of frustration in mean-field models dramatically alters the dynamical behavior with respect to the issue of predictability.

Long-time predictability in disordered spin systems following a deep quench

Science.gov (United States)

Ye, J.; Gheissari, R.; Machta, J.; Newman, C. M.; Stein, D. L.

2017-04-01

We study the problem of predictability, or "nature vs nurture," in several disordered Ising spin systems evolving at zero temperature from a random initial state: How much does the final state depend on the information contained in the initial state, and how much depends on the detailed history of the system? Our numerical studies of the "dynamical order parameter" in Edwards-Anderson Ising spin glasses and random ferromagnets indicate that the influence of the initial state decays as dimension increases. Similarly, this same order parameter for the Sherrington-Kirkpatrick infinite-range spin glass indicates that this information decays as the number of spins increases. Based on these results, we conjecture that the influence of the initial state on the final state decays to zero in finite-dimensional random-bond spin systems as dimension goes to infinity, regardless of the presence of frustration. We also study the rate at which spins "freeze out" to a final state as a function of dimensionality and number of spins; here the results indicate that the number of "active" spins at long times increases with dimension (for short-range systems) or number of spins (for infinite-range systems). We provide theoretical arguments to support these conjectures, and also study analytically several mean-field models: the random energy model, the uniform Curie-Weiss ferromagnet, and the disordered Curie-Weiss ferromagnet. We find that for these models, the information contained in the initial state does not decay in the thermodynamic limit—in fact, it fully determines the final state. Unlike in short-range models, the presence of frustration in mean-field models dramatically alters the dynamical behavior with respect to the issue of predictability.

Hemostatic system changes predictive value in patients with ischemic brain disorders

Directory of Open Access Journals (Sweden)

Raičević Ranko

2002-01-01

Full Text Available The aim of this research was to determine the importance of tracking the dynamics of changes of the hemostatic system factors (aggregation of thrombocytes, D-dimer, PAI-1, antithrombin III, protein C and protein S, factor VII and factor VIII, fibrin degradation products, euglobulin test and the activated partial thromboplastin time – aPTPV in relation to the level of the severity of ischemic brain disorders (IBD and the level of neurological and functional deficiency in the beginning of IBD manifestation from 7 to 10 days, 19 to 21 day, and after 3 to 6 months. The research results confirmed significant predictive value of changes of hemostatic system with the predomination of procoagulant factors, together with the insufficiency of fibrinolysis. Concerning the IBD severity and it's outcome, the significant predictive value was shown in the higher levels of PAI-1 and the lower level of antithrombin III, and borderline significant value was shown in the accelerated aggregation of thrombocytes and the increased concentration of D-dimer. It could be concluded that the tracking of the dynamics of changes in parameters of hemostatic system proved to be an easily accessible method with the significant predictive value regarding the development of more severe. IBD cases and the outcome of the disease itself.

Improving Permafrost Hydrology Prediction Through Data-Model Integration

Science.gov (United States)

Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

2017-12-01

The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

Impact of dissociation on treatment of depressive and anxiety spectrum disorders with and without personality disorders

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Prasko J

2016-10-01

questionnaires. The patients’ mean ratings on all measurements were significantly reduced during the treatment. Also, 67.5% reached at least minimal improvement (42.4% showed moderate and more improvement, 35.3% of the patients reached remission. The patients without comorbid personality disorder improved more significantly in the reduction of depressive symptoms than those with comorbid personality disorder. However, there were no significant differences in change in anxiety levels and severity of the mental issues between the patients with and without personality disorders. Higher degree of dissociation at the beginning of the treatment predicted minor improvement, and also, higher therapeutic change was connected to greater reduction of the dissociation level.Conclusion: Dissociation is an important factor that influences the treatment effectiveness in anxiety/depression patients with or without personality disorders resistant to previous treatment. Targeting dissociation in the treatment of these disorders may be beneficial. Keywords: depression, anxiety disorders, treatment resistance, panic disorder, GAD, OCD, social phobia, PTSD, adjustment disorders, personality disorders

Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning.

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Månsson, K N T; Frick, A; Boraxbekk, C-J; Marquand, A F; Williams, S C R; Carlbring, P; Andersson, G; Furmark, T

2015-03-17

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a substantial proportion relapse after treatment has ended. Predicting an individual's long-term clinical response therefore remains an important challenge. This study aimed at assessing neural predictors of long-term treatment outcome in participants with SAD 1 year after completion of Internet-delivered CBT (iCBT). Twenty-six participants diagnosed with SAD underwent iCBT including attention bias modification for a total of 13 weeks. Support vector machines (SVMs), a supervised pattern recognition method allowing predictions at the individual level, were trained to separate long-term treatment responders from nonresponders based on blood oxygen level-dependent (BOLD) responses to self-referential criticism. The Clinical Global Impression-Improvement scale was the main instrument to determine treatment response at the 1-year follow-up. Results showed that the proportion of long-term responders was 52% (12/23). From multivariate BOLD responses in the dorsal anterior cingulate cortex (dACC) together with the amygdala, we were able to predict long-term response rate of iCBT with an accuracy of 92% (confidence interval 95% 73.2-97.6). This activation pattern was, however, not predictive of improvement in the continuous Liebowitz Social Anxiety Scale-Self-report version. Follow-up psychophysiological interaction analyses revealed that lower dACC-amygdala coupling was associated with better long-term treatment response. Thus, BOLD response patterns in the fear-expressing dACC-amygdala regions were highly predictive of long-term treatment outcome of iCBT, and the initial coupling between these regions differentiated long-term responders from nonresponders. The SVM-neuroimaging approach could be of particular clinical value as it allows for accurate prediction of treatment outcome at the level of the individual.

Do improvements after inpatient dialectial behavioral therapy persist in the long term? A naturalistic follow-up in patients with borderline personality disorder.

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Kleindienst, Nikolaus; Limberger, Matthias F; Schmahl, Christian; Steil, Regina; Ebner-Priemer, Ulrich W; Bohus, Martin

2008-11-01

Three months of inpatient dialectical behavior therapy proved to be highly effective in patients with borderline personality disorder. This study investigates whether the effects of DBT persist after the patients returned to their usual lives. Thirty-one patients with a diagnosis of borderline personality disorder (DSM-IV) were prospectively followed-up for an observation period of 21 months after discharge from the DBT program, under naturalistic conditions.Improvements as observed after discharge persisted over the full follow-up period. This is reflected in a steady rate of remitted patients and in a broad range of psychopathology showing statistically and clinically significant effect-sizes ranging from 0.70 to 1.71. Analyses of courses over time revealed a high intraindividual concordance, indicating that short term treatment response predicted remission after 2 years follow-up. The effects of inpatient dialectical behavior therapy seem to persist after patients returned to their usual lives.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

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Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

Combining gene signatures improves prediction of breast cancer survival.

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Xi Zhao

Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

Can biomechanical variables predict improvement in crouch gait?

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Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

2011-01-01

Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

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Wang, Xun-Heng; Jiao, Yun; Li, Lihua

2017-10-24

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken asan intra-ICN feature, and phase synchronization (PS) was used asan inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r=0.79 (p<10 -8 ), and the performance of the predictive model for impulsivity is r=0.48 (p<10 -8 ). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Support Vector Machine Analysis of Functional Magnetic Resonance Imaging of Interoception Does Not Reliably Predict Individual Outcomes of Cognitive Behavioral Therapy in Panic Disorder with Agoraphobia

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Benedikt Sundermann

2017-06-01

Full Text Available BackgroundThe approach to apply multivariate pattern analyses based on neuro imaging data for outcome prediction holds out the prospect to improve therapeutic decisions in mental disorders. Patients suffering from panic disorder with agoraphobia (PD/AG often exhibit an increased perception of bodily sensations. The purpose of this investigation was to assess whether multivariate classification applied to a functional magnetic resonance imaging (fMRI interoception paradigm can predict individual responses to cognitive behavioral therapy (CBT in PD/AG.MethodsThis analysis is based on pretreatment fMRI data during an interoceptive challenge from a multicenter trial of the German PANIC-NET. Patients with DSM-IV PD/AG were dichotomized as responders (n = 30 or non-responders (n = 29 based on the primary outcome (Hamilton Anxiety Scale Reduction ≥50% after 6 weeks of CBT (2 h/week. fMRI parametric maps were used as features for response classification with linear support vector machines (SVM with or without automated feature selection. Predictive accuracies were assessed using cross validation and permutation testing. The influence of methodological parameters and the predictive ability for specific interoception-related symptom reduction were further evaluated.ResultsSVM did not reach sufficient overall predictive accuracies (38.0–54.2% for anxiety reduction in the primary outcome. In the exploratory analyses, better accuracies (66.7% were achieved for predicting interoception-specific symptom relief as an alternative outcome domain. Subtle information regarding this alternative response criterion but not the primary outcome was revealed by post hoc univariate comparisons.ConclusionIn contrast to reports on other neurofunctional probes, SVM based on an interoception paradigm was not able to reliably predict individual response to CBT. Results speak against the clinical applicability of this technique.

Response to adrenocorticotropic in attention deficit hyperactivity disorder-like symptoms in electrical status epilepticus in sleep syndrome is related to electroencephalographic improvement: A retrospective study.

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Altunel, Attila; Altunel, Emine Özlem; Sever, Ali

2017-09-01

Encephalopathy with electrical status epilepticus in sleep (ESES) syndrome is a rare epilepsy syndrome of childhood that is characterized by sleep-induced epileptiform discharges and problems with cognition or behavior. The neuropsychiatric symptoms in ESES syndrome, among which the ADHD-like symptoms are prominent, bear a close resemblance to symptoms in various developmental disorders. Positive response to adrenocorticotropic hormone (ACTH) is associated with the normalization of the EEG and improvement of neuropsychiatric function. This study aimed to determine the improvement in ADHD-like symptoms in response to ACTH and establish a relationship between improvement in clinical symptoms and EEG parameters. Seventy-five patients with ESES syndrome, who had clinically displayed ADHD-like symptoms, had been treated with ACTH for ESES, and their medical records were retrospectively reviewed. Sleep EEGs were recorded at referral and follow-up visits, and short courses of ACTH were administered when spike-wave index (SWI) was ≥15%. The assessment of treatment effectiveness was based on reduction in SWI and the clinician-reported improvement in ADHD-like symptoms. Statistical analyses were conducted in order to investigate the relationship between the clinical and EEG parameters. Following treatment with ACTH, a reduction in SWI in all the patients was accompanied by a mean improvement of 67% in ADHD-like symptoms. Disappearance/reduction of foci and cessation/reduction of seizures were achieved in patients with formerly antiepileptic-resistant seizures. Multiple linear regressions established that pretreatment SWI and treatment delay predicted posttreatment SWI, while reduction in SWI, treatment delay, and the presence of foci predicted improvement in ADHD-like symptoms. Improvement in ADHD-like symptoms showed high correlation and was timely with the resolution of ESES. It is suggested that ESES and ADHD may be the two different expressions of a common

Improving Saliency Models by Predicting Human Fixation Patches

KAUST Repository

Dubey, Rachit

2015-04-16

There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

Improving Saliency Models by Predicting Human Fixation Patches

KAUST Repository

Dubey, Rachit; Dave, Akshat; Ghanem, Bernard

2015-01-01

There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

Predicting Voice Disorder Status From Smoothed Measures of Cepstral Peak Prominence Using Praat and Analysis of Dysphonia in Speech and Voice (ADSV).

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Sauder, Cara; Bretl, Michelle; Eadie, Tanya

2017-09-01

The purposes of this study were to (1) determine and compare the diagnostic accuracy of a single acoustic measure, smoothed cepstral peak prominence (CPPS), to predict voice disorder status from connected speech samples using two software systems: Analysis of Dysphonia in Speech and Voice (ADSV) and Praat; and (2) to determine the relationship between measures of CPPS generated from these programs. This is a retrospective cross-sectional study. Measures of CPPS were obtained from connected speech recordings of 100 subjects with voice disorders and 70 nondysphonic subjects without vocal complaints using commercially available ADSV and freely downloadable Praat software programs. Logistic regression and receiver operating characteristic (ROC) analyses were used to evaluate and compare the diagnostic accuracy of CPPS measures. Relationships between CPPS measures from the programs were determined. Results showed acceptable overall accuracy rates (75% accuracy, ADSV; 82% accuracy, Praat) and area under the ROC curves (area under the curve [AUC] = 0.81, ADSV; AUC = 0.91, Praat) for predicting voice disorder status, with slight differences in sensitivity and specificity. CPPS measures derived from Praat were uniquely predictive of disorder status above and beyond CPPS measures from ADSV (χ 2 (1) = 40.71, P disorder status using either program. Clinicians may consider using CPPS to complement clinical voice evaluation and screening protocols. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Improved hybrid optimization algorithm for 3D protein structure prediction.

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Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Current psychiatric disorders in patients with epilepsy are predicted by maltreatment experiences during childhood.

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Labudda, Kirsten; Illies, Dominik; Herzig, Cornelia; Schröder, Katharina; Bien, Christian G; Neuner, Frank

2017-09-01

Childhood maltreatment has been shown to be a risk factor for the development of psychiatric disorders. Although the prevalence of psychiatric disorders is high in epilepsy patients, it is unknown if childhood maltreatment experiences are elevated compared to the normal population and if early maltreatment is a risk factor for current psychiatric comorbidities in epilepsy patients. This is the main purpose of this study. Structured interviews were used to assess current Axis I diagnoses in 120 epilepsy patients from a tertiary Epilepsy Center (34 TLE patients, 86 non-TLE patients). Childhood maltreatment in the family and peer victimization were assessed with validated questionnaires. Patients' maltreatment scores were compared with those of a representative matched control group. Logistic regression analysis was conducted to assess the potential impact of childhood maltreatment on current psychiatric comorbidity in epilepsy patients. Compared to a matched control group, epilepsy patients had higher emotional and sexual maltreatment scores. Patients with a current psychiatric diagnosis reported more family and peer maltreatment than patients without a psychiatric disorder. Family maltreatment scores predicted the likelihood of a current psychiatric disorder. TLE patients did not differ from non-TLE patients according to maltreatment experiences and rates of current psychiatric disorders. Our findings suggest that in epilepsy patients emotional and sexual childhood maltreatment is experienced more often than in the normal population and that early maltreatment is a general risk factor for psychiatric comorbidities in this group. Copyright © 2017 Elsevier B.V. All rights reserved.

Taxometric analyses and predictive accuracy of callous-unemotional traits regarding quality of life and behavior problems in non-conduct disorder diagnoses

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Herpers, P.C.M.; Klip, H.; Rommelse, N.N.J.; Taylor, M.J.; Greven, C.U.; Buitelaar, J.K.

2017-01-01

Callous-unemotional (CU) traits have mainly been studied in relation to conduct disorder (CD), but can also occur in other disorder groups. However, it is unclear whether there is a clinically relevant cut-off value of levels of CU traits in predicting reduced quality of life (QoL) and clinical

Maintained improvement in neurocognitive function in major depressive disorders 6 months after ECT

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Christine Mohn

2016-12-01

Full Text Available Both impaired and improved cognitive function after electroconvulsive treatment (ECT in major depressive disorder (MDD patients may occur. We have previously found improved cognitive function 6 weeks after ECT in this group. The aim of this study was to report 6-month follow-up results from the same prospective project monitoring cognitive effects of ECT. Thirty-one patients with major depressive disorder were assessed with the MATRICS Consensus Cognitive Battery (MCCB, the Everyday Memory Questionnaire (EMQ, and the Montgomery-Åsberg Depression Rating Scale (MADRS prior to, 6 weeks, and 6 months after ECT.Compared to baseline, the Speed of Processing, Attention/Vigilance, and Reasoning/Problem Solving test results were significantly improved. The depression score was significantly reduced. There were no changes in subjective memory complaint. There were no significant relationship between the EMQ and the MCCB subtests, but a significant correlation between current depression level and the EMQ.Six months after ECT the cognitive improvement reported at 6 weeks follow-up was maintained and extended. The corresponding decrease in depressive symptoms and stability in subjectively reported memory complaints suggests that the antidepressant effects of ECT do not occur at the expense of cognitive function.

Innovative predictive maintenance concepts to improve life cycle management

NARCIS (Netherlands)

Tinga, Tiedo

2014-01-01

For naval systems with typically long service lives, high sustainment costs and strict availability requirements, an effective and efficient life cycle management process is very important. In this paper four approaches are discussed to improve that process: physics of failure based predictive

Solar radio proxies for improved satellite orbit prediction

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Yaya, Philippe; Hecker, Louis; Dudok de Wit, Thierry; Fèvre, Clémence Le; Bruinsma, Sean

2017-12-01

Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.

Solar radio proxies for improved satellite orbit prediction

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Yaya Philippe

2017-01-01

Full Text Available Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model performs better with (past and predicted values of the 30 cm radio flux than with the 10.7 flux.

A two-stage approach for improved prediction of residue contact maps

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Pollastri Gianluca

2006-03-01

Full Text Available Abstract Background Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts, the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. Results We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35

Long-term oxytocin administration improves social behaviors in a girl with autistic disorder.

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Kosaka, Hirotaka; Munesue, Toshio; Ishitobi, Makoto; Asano, Mizuki; Omori, Masao; Sato, Makoto; Tomoda, Akemi; Wada, Yuji

2012-08-13

Patients with autism spectrum disorders (ASDs) exhibit core autistic symptoms including social impairments from early childhood and mostly show secondary disabilities such as irritability and aggressive behavior based on core symptoms. However, there are still no radical treatments of social impairments in these patients. Oxytocin has been reported to play important roles in multiple social behaviors dependent on social recognition, and has been expected as one of the effective treatments of social impairments of patients with ASDs. We present a case of a 16-year-old girl with autistic disorder who treated by long-term administration of oxytocin nasal spray. Her autistic symptoms were successfully treated by two month administration; the girl's social interactions and social communication began to improve without adverse effects. Her irritability and aggressive behavior also improved dramatically with marked decreases in aberrant behavior checklist scores from 69 to 7. This case is the first to illustrate long-term administration of oxytocin nasal spray in the targeted treatment of social impairments in a female with autistic disorder. This case suggests that long-term nasal oxytocin spray is promising and well-tolerated for treatment of social impairments of patients with ASDs.

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

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Peng Lu

2018-01-01

Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

Healthy, wealthy, and wise: retirement planning predicts employee health improvements.

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Gubler, Timothy; Pierce, Lamar

2014-09-01

Are poor physical and financial health driven by the same underlying psychological factors? We found that the decision to contribute to a 401(k) retirement plan predicted whether an individual acted to correct poor physical-health indicators revealed during an employer-sponsored health examination. Using this examination as a quasi-exogenous shock to employees' personal-health knowledge, we examined which employees were more likely to improve their health, controlling for differences in initial health, demographics, job type, and income. We found that existing retirement-contribution patterns and future health improvements were highly correlated. Employees who saved for the future by contributing to a 401(k) showed improvements in their abnormal blood-test results and health behaviors approximately 27% more often than noncontributors did. These findings are consistent with an underlying individual time-discounting trait that is both difficult to change and domain interdependent, and that predicts long-term individual behaviors in multiple dimensions. © The Author(s) 2014.

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

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Adhikari, Badri; Hou, Jie; Cheng, Jianlin

2018-05-01

Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction. In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length. The web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11 and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/. chengji@missouri.edu. Supplementary data are available at Bioinformatics online.

Validation of the Seven Up Seven Down Inventory in bipolar offspring : screening and prediction of mood disorders. Findings from the Dutch Bipolar Offspring Study

NARCIS (Netherlands)

Mesman, E; Youngstrom, E A; Juliana, N K; Nolen, W A; Hillegers, M H J

2016-01-01

OBJECTIVE: To validate the Seven Up Seven Down (7U7D), an abbreviated version of the General Behavior Inventory (GBI), as screener for mood disorders and test its ability to predict mood disorders over time in individuals at risk for bipolar disorder (BD). METHODS: Bipolar offspring (n=108) were

Validation of the Seven Up Seven Down Inventory in bipolar offspring : screening and prediction of mood disorders. Findings from the Dutch Bipolar Offspring Study

NARCIS (Netherlands)

Mesman, Esther; Youngstrom, E. A.; Juliana, N. K.; Nolen, W. A.; Hillegers, M. H. J.

2017-01-01

Objective: To validate the Seven Up Seven Down (7U7D), an abbreviated version of the General Behavior Inventory (GBI), as screener for mood disorders and test its ability to predict mood disorders over time in individuals at risk for bipolar disorder (BD). Methods: Bipolar offspring (n=108) were

Predictive validity of neurotic disorders

DEFF Research Database (Denmark)

Jepsen, Peter Winning; Butler, Birgitte; Rasmussen, Stig

2014-01-01

association with the other two categories of neurosis than would be expected by chance. CONCLUSION: Anxiety neurosis and obsessive-compulsive neurosis are more severe disorders than hysterical neurosis, both in terms of symptom profile and depression, including suicidal behaviour. The identified suicides were...

Do Patient Characteristics Predict Outcome of Psychodynamic Psychotherapy for Social Anxiety Disorder?

Directory of Open Access Journals (Sweden)

Jörg Wiltink

Full Text Available Little is known about patient characteristics as predictors for outcome in manualized short term psychodynamic psychotherapy (PDT. No study has addressed which patient variables predict outcome of PDT for social anxiety disorder.In the largest multicenter trial on psychotherapy of social anxiety (SA to date comparing cognitive therapy, PDT and wait list condition N = 230 patients were assigned to receive PDT, of which N = 166 completed treatment. Treatment outcome was assessed based on diverse parameters such as endstate functioning, remission, response, and drop-out. The relationship between patient characteristics (demographic variables, mental co-morbidity, personality, interpersonal problems and outcome was analysed using logistic and linear regressions.Pre-treatment SA predicted up to 39 percent of variance of outcome. Only few additional baseline characteristics predicted better treatment outcome (namely, lower comorbidity and interpersonal problems with a limited proportion of incremental variance (5.5 to 10 percent, while, e.g., shame, self-esteem or harm avoidance did not.We argue that the central importance of pre-treatment symptom severity for predicting outcomes should advocate alternative treatment strategies (e.g. longer treatments, combination of psychotherapy and medication in those who are most disturbed. Given the relatively small amount of variance explained by the other patient characteristics, process variables and patient-therapist interaction should additionally be taken into account in future research.Controlled-trials.com/ISRCTN53517394.

Improved prediction of signal peptides: SignalP 3.0

DEFF Research Database (Denmark)

Bendtsen, Jannick Dyrløv; Nielsen, Henrik; von Heijne, G.

2004-01-01

We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the ...

Treating insomnia improves mood state, sleep, and functioning in bipolar disorder: a pilot randomized controlled trial.

Science.gov (United States)

Harvey, Allison G; Soehner, Adriane M; Kaplan, Kate A; Hein, Kerrie; Lee, Jason; Kanady, Jennifer; Li, Descartes; Rabe-Hesketh, Sophia; Ketter, Terence A; Neylan, Thomas C; Buysse, Daniel J

2015-06-01

To determine if a treatment for interepisode bipolar disorder I patients with insomnia improves mood state, sleep, and functioning. Alongside psychiatric care, interepisode bipolar disorder I participants with insomnia were randomly allocated to a bipolar disorder-specific modification of cognitive behavior therapy for insomnia (CBTI-BP; n = 30) or psychoeducation (PE; n = 28) as a comparison condition. Outcomes were assessed at baseline, the end of 8 sessions of treatment, and 6 months later. This pilot was conducted to determine initial feasibility and generate effect size estimates. During the 6-month follow-up, the CBTI-BP group had fewer days in a bipolar episode relative to the PE group (3.3 days vs. 25.5 days). The CBTI-BP group also experienced a significantly lower hypomania/mania relapse rate (4.6% vs. 31.6%) and a marginally lower overall mood episode relapse rate (13.6% vs. 42.1%) compared with the PE group. Relative to PE, CBTI-BP reduced insomnia severity and led to higher rates of insomnia remission at posttreatment and marginally higher rates at 6 months. Both CBTI-BP and PE showed statistically significant improvement on selected sleep and functional impairment measures. The effects of treatment were well sustained through follow-up for most outcomes, although some decline on secondary sleep benefits was observed. CBTI-BP was associated with reduced risk of mood episode relapse and improved sleep and functioning on certain outcomes in bipolar disorder. Hence, sleep disturbance appears to be an important pathway contributing to bipolar disorder. The need to develop bipolar disorder-specific sleep diary scoring standards is highlighted. (c) 2015 APA, all rights reserved).

Predictable information in neural signals during resting state is reduced in autism spectrum disorder.

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Brodski-Guerniero, Alla; Naumer, Marcus J; Moliadze, Vera; Chan, Jason; Althen, Heike; Ferreira-Santos, Fernando; Lizier, Joseph T; Schlitt, Sabine; Kitzerow, Janina; Schütz, Magdalena; Langer, Anne; Kaiser, Jochen; Freitag, Christine M; Wibral, Michael

2018-04-04

The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients. © 2018 Wiley Periodicals, Inc.

Genome-Wide Prediction of Intrinsic Disorder; Sequence Alignment of Intrinsically Disordered Proteins

Science.gov (United States)

Midic, Uros

2012-01-01

Intrinsic disorder (ID) is defined as a lack of stable tertiary and/or secondary structure under physiological conditions in vitro. Intrinsically disordered proteins (IDPs) are highly abundant in nature. IDPs possess a number of crucial biological functions, being involved in regulation, recognition, signaling and control, e.g. their functional…

Improvement of a patient's circadian rhythm sleep disorders by aripiprazole was associated with stabilization of his bipolar illness.

Science.gov (United States)

Tashiro, Tetsuo

2017-04-01

Splitting of the behavioural activity phase has been found in nocturnal rodents with suprachiasmatic nucleus (SCN) coupling disorder. A similar phenomenon was observed in the sleep phase in the diurnal human discussed here, suggesting that there are so-called evening and morning oscillators in the SCN of humans. The present case suffered from bipolar disorder refractory to various treatments, and various circadian rhythm sleep disorders, such as delayed sleep phase, polyphasic sleep, separation of the sleep bout resembling splitting and circabidian rhythm (48 h), were found during prolonged depressive episodes with hypersomnia. Separation of sleep into evening and morning components and delayed sleep-offset (24.69-h cycle) developed when lowering and stopping the dose of aripiprazole (APZ). However, resumption of APZ improved these symptoms in 2 weeks, accompanied by improvement in the patient's depressive state. Administration of APZ may improve various circadian rhythm sleep disorders, as well as improve and prevent manic-depressive episodes, via augmentation of coupling in the SCN network. © 2017 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.

Sleep quality predicts treatment outcome in CBT for social anxiety disorder.

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Zalta, Alyson K; Dowd, Sheila; Rosenfield, David; Smits, Jasper A J; Otto, Michael W; Simon, Naomi M; Meuret, Alicia E; Marques, Luana; Hofmann, Stefan G; Pollack, Mark H

2013-11-01

Sleep quality may be an important, yet relatively neglected, predictor of treatment outcome in cognitive-behavioral therapy (CBT) for anxiety disorders. Specifically, poor sleep quality may impair memory consolidation of in-session extinction learning. We therefore examined sleep quality as a predictor of treatment outcome in CBT for social anxiety disorder and the impact of d-cycloserine (DCS) on this relationship. One hundred sixty-nine participants with a primary diagnosis of DSM-IV generalized social anxiety disorder were recruited across three sites. Participants were enrolled in 12 weeks of group CBT. Participants randomly received 50 mg of DCS (n = 87) or pill placebo (n = 82) 1 hr prior to sessions 3-7. Participants completed a baseline measure of self-reported sleep quality and daily diaries recording subjective feelings of being rested upon wakening. Outcome measures including social anxiety symptoms and global severity scores were assessed at each session. Poorer baseline sleep quality was associated with slower improvement and higher posttreatment social anxiety symptom and severity scores. Moreover, patients who felt more "rested" after sleeping the night following a treatment session had lower levels of symptoms and global severity at the next session, controlling for their symptoms and severity scores the previous session. Neither of these effects were moderated by DCS condition. Our findings suggest that poor sleep quality diminishes the effects of CBT for social anxiety disorder and this relation is not attenuated by DCS administration. Therapeutic attention to sleep quality prior to initiation of CBT and during the acute treatment phase may be clinically indicated. © 2013 Wiley Periodicals, Inc.

Nutritional Improvement and Energy Intake Are Associated with Functional Recovery in Patients after Cerebrovascular Disorders.

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Nii, Maria; Maeda, Keisuke; Wakabayashi, Hidetaka; Nishioka, Shinta; Tanaka, Atsuko

2016-01-01

Malnutrition affects the activities of daily living (ADLs) in convalescent patients with cerebrovascular disorders. We investigated the relationship between nutritional improvement, energy intake at admission, and recovery of ADLs. We evaluated 67 patients with cerebrovascular disorders admitted to our rehabilitation hospital between April 2013 and April 2015. These patients received interventions from the rehabilitation nutritional support team according to the following criteria: weight loss of 2 kg or more and body mass index of 19 kg/m(2) or lower. Exclusion criteria included a body mass index of 25 kg/m(2) or higher, duration of intervention of less than 14 days, or transfer to an acute care hospital because of clinical deterioration. We assessed nutritional status using the Geriatric Nutritional Risk Index (GNRI) and ADL using the Functional Independence Measure (FIM) score, FIM gain, and FIM efficiency. The mean age of the patients was 78.7 ± 8.0 years. The numbers of patients in each category of cerebrovascular disorder were 39 with cerebral infarction, 16 with intracerebral hemorrhage, 8 with subarachnoid hemorrhage, and 4 others. Compared with the counterpart group, the group with an improvement in GNRI had a greater gain in FIM (median 17 and 20, respectively; P = .036) and a higher FIM efficiency (.14 and .22, respectively; P = .020). Multivariate stepwise regression analysis showed that an improvement in GNRI, increasing energy intake at admission, and intracerebral hemorrhage were associated independently with greater FIM efficiency. This study suggested that nutritional improvement and energy intake at admission are associated with recovery of ADL after cerebrovascular disorders. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Auditory evoked potentials: predicting speech therapy outcomes in children with phonological disorders

Directory of Open Access Journals (Sweden)

Renata Aparecida Leite

2014-03-01

Full Text Available OBJECTIVES: This study investigated whether neurophysiologic responses (auditory evoked potentials differ between typically developed children and children with phonological disorders and whether these responses are modified in children with phonological disorders after speech therapy. METHODS: The participants included 24 typically developing children (Control Group, mean age: eight years and ten months and 23 children clinically diagnosed with phonological disorders (Study Group, mean age: eight years and eleven months. Additionally, 12 study group children were enrolled in speech therapy (Study Group 1, and 11 were not enrolled in speech therapy (Study Group 2. The subjects were submitted to the following procedures: conventional audiological, auditory brainstem response, auditory middle-latency response, and P300 assessments. All participants presented with normal hearing thresholds. The study group 1 subjects were reassessed after 12 speech therapy sessions, and the study group 2 subjects were reassessed 3 months after the initial assessment. Electrophysiological results were compared between the groups. RESULTS: Latency differences were observed between the groups (the control and study groups regarding the auditory brainstem response and the P300 tests. Additionally, the P300 responses improved in the study group 1 children after speech therapy. CONCLUSION: The findings suggest that children with phonological disorders have impaired auditory brainstem and cortical region pathways that may benefit from speech therapy.

Short report: Improving record-review surveillance of young children with an autism spectrum disorder.

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Wiggins, Lisa D; Robins, Diana L; Yeargin-Allsopp, Marshalyn

2013-09-01

Records-based autism spectrum disorder surveillance developed at the Centers for Disease Control and Prevention has been extended to younger cohorts, although the utility of additional record sources has not been examined. We therefore conducted a pilot project to describe whether Centers for Disease Control and Prevention surveillance could identify younger children with an autism spectrum disorder evaluated as part of an ongoing screening study at Georgia State University. In all, 31 families of children who screened positive for autism spectrum disorder and received a clinical evaluation at Georgia State University agreed to participate in the project. Of these, 10 children lived inside the surveillance area and had records abstracted and reviewed for this project. Centers for Disease Control and Prevention surveillance results (i.e. autism spectrum disorder or non-autism spectrum disorder) were compared with Georgia State University evaluation results (i.e. autism spectrum disorder or non-autism spectrum disorder). In all, 4 of the 10 children were diagnosed with an autism spectrum disorder after the Georgia State University evaluation. None of the 4 children with an autism spectrum disorder were identified by current Centers for Disease Control and Prevention surveillance methods but all 4 children were identified by Centers for Disease Control and Prevention surveillance methods when additional record sources were included (i.e. records from the statewide early intervention program and Georgia State University evaluation). These findings suggest that partnering with early intervention programs and encouraging early autism spectrum disorder screening might improve autism spectrum disorder surveillance among young children.

Infinite ensemble of support vector machines for prediction of ...

African Journals Online (AJOL)

Many researchers have demonstrated the use of artificial neural networks (ANNs) to predict musculoskeletal disorders risk associated with occupational exposures. In order to improve the accuracy of LBDs risk classification, this paper proposes to use the support vector machines (SVMs), a machine learning algorithm used ...

Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-12-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http

The PROSECCO server for chemical shift predictions in ordered and disordered proteins.

Science.gov (United States)

Sanz-Hernández, Máximo; De Simone, Alfonso

2017-11-01

The chemical shifts measured in solution-state and solid-state nuclear magnetic resonance (NMR) are powerful probes of the structure and dynamics of protein molecules. The exploitation of chemical shifts requires methods to correlate these data with the protein structures and sequences. We present here an approach to calculate accurate chemical shifts in both ordered and disordered proteins using exclusively the information contained in their sequences. Our sequence-based approach, protein sequences and chemical shift correlations (PROSECCO), achieves the accuracy of the most advanced structure-based methods in the characterization of chemical shifts of folded proteins and improves the state of the art in the study of disordered proteins. Our analyses revealed fundamental insights on the structural information carried by NMR chemical shifts of structured and unstructured protein states.

Functional outcomes of child and adolescent mental disorders. Current disorder most important but psychiatric history matters as well.

Science.gov (United States)

Ormel, J; Oerlemans, A M; Raven, D; Laceulle, O M; Hartman, C A; Veenstra, R; Verhulst, F C; Vollebergh, W; Rosmalen, J G M; Reijneveld, S A; Oldehinkel, A J

2017-05-01

Various sources indicate that mental disorders are the leading contributor to the burden of disease among youth. An important determinant of functioning is current mental health status. This study investigated whether psychiatric history has additional predictive power when predicting individual differences in functional outcomes. We used data from the Dutch TRAILS study in which 1778 youths were followed from pre-adolescence into young adulthood (retention 80%). Of those, 1584 youths were successfully interviewed, at age 19, using the World Health Organization Composite International Diagnostic Interview (CIDI 3.0) to assess current and past CIDI-DSM-IV mental disorders. Four outcome domains were assessed at the same time: economic (e.g. academic achievement, social benefits, financial difficulties), social (early motherhood, interpersonal conflicts, antisocial behavior), psychological (e.g. suicidality, subjective well-being, loneliness), and health behavior (e.g. smoking, problematic alcohol, cannabis use). Out of the 19 outcomes, 14 were predicted by both current and past disorders, three only by past disorders (receiving social benefits, psychiatric hospitalization, adolescent motherhood), and two only by current disorder (absenteeism, obesity). Which type of disorders was most important depended on the outcome. Adjusted for current disorder, past internalizing disorders predicted in particular psychological outcomes while externalizing disorders predicted in particular health behavior outcomes. Economic and social outcomes were predicted by a history of co-morbidity of internalizing and externalizing disorder. The risk of problematic cannabis use and alcohol consumption dropped with a history of internalizing disorder. To understand current functioning, it is necessary to examine both current and past psychiatric status.

The Role of Parental Perceptions of Tic Frequency and Intensity in Predicting Tic-Related Functional Impairment in Youth with Chronic Tic Disorders

Science.gov (United States)

Espil, Flint M.; Capriotti, Matthew R.; Conelea, Christine A.; Woods, Douglas W.

2014-01-01

Tic severity is composed of several dimensions. Tic frequency and intensity are two such dimensions, but little empirical data exist regarding their relative contributions to functional impairment in those with Chronic Tic Disorders (CTD). The present study examined the relative contributions of these dimensions in predicting tic-related impairment across several psychosocial domains. Using data collected from parents of youth with CTD, multivariate regression analyses revealed that both tic frequency and intensity predicted tic-related impairment in several areas; including family and peer relationships, school interference, and social endeavors, even when controlling for the presence of comorbid anxiety symptoms and Attention Deficit Hyperactivity Disorder diagnostic status. Results showed that tic intensity predicted more variance across more domains than tic frequency. PMID:24395287

The role of parental perceptions of tic frequency and intensity in predicting tic-related functional impairment in youth with chronic tic disorders.

Science.gov (United States)

Espil, Flint M; Capriotti, Matthew R; Conelea, Christine A; Woods, Douglas W

2014-12-01

Tic severity is composed of several dimensions. Tic frequency and intensity are two such dimensions, but little empirical data exist regarding their relative contributions to functional impairment in those with chronic tic disorders (CTD). The present study examined the relative contributions of these dimensions in predicting tic-related impairment across several psychosocial domains. Using data collected from parents of youth with CTD, multivariate regression analyses revealed that both tic frequency and intensity predicted tic-related impairment in several areas; including family and peer relationships, school interference, and social endeavors, even when controlling for the presence of comorbid anxiety symptoms and Attention Deficit Hyperactivity Disorder diagnostic status. Results showed that tic intensity predicted more variance across more domains than tic frequency.

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

Science.gov (United States)

Cao, Han; Ng, Marcus C. K.; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W. I.

2017-09-01

α-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD PHP, MySQL and Apache, with all major browsers supported.

The Influence of Comorbid Disorders on the Episodicity of Bipolar Disorder in Youth

Science.gov (United States)

Yen, Shirley; Stout, Robert; Hower, Heather; Killam, Matthew A.; Weinstock, Lauren M.; Topor, David R.; Dickstein, Daniel P.; Hunt, Jeffrey I.; Gill, Mary Kay; Goldstein, Tina R.; Goldstein, Benjamin I.; Ryan, Neal D.; Strober, Michael; Sala, Regina; Axelson, David A.; Birmaher, Boris; Keller, Martin B.

2015-01-01

Objective Bipolar Disorder (BP) frequently co-occurs with other psychiatric disorders. We examine whether course of anxiety disorders (ANX), attention deficit hyperactivity disorder (ADHD), disruptive behavior disorders (DBD), and substance use disorders (SUD) influence likelihood of recovery and recurrence of depression and mania in BP youth. Method Weekly ratings of psychiatric disorder intensity were obtained from 413 participants of the Course and Outcome of BP Youth project, followed for an average of 7.75 years. Multiple-event Cox proportional hazards regression analyses examined worsening of comorbid disorders as predictors of mood episode recovery and recurrence. Results Increased severity in ANX and SUD predicted longer time to recovery and less time to next depressive episode, and less time to next manic episode. Multivariate models with ANX and SUD found that significant effects of ANX remained, but SUD only predicted longer time to depression recovery. Increased severity of ADHD and DBD predicted shorter time to recurrence for depressive and manic episodes. Conclusion There are significant time-varying relationships between the course of comorbid disorders and episodicity of depression and mania in BP youth. Worsening of comorbid conditions may present as a precursor to mood episode recurrence or warn of mood episode protraction. PMID:26475572

The influence of comorbid disorders on the episodicity of bipolar disorder in youth.

Science.gov (United States)

Yen, S; Stout, R; Hower, H; Killam, M A; Weinstock, L M; Topor, D R; Dickstein, D P; Hunt, J I; Gill, M K; Goldstein, T R; Goldstein, B I; Ryan, N D; Strober, M; Sala, R; Axelson, D A; Birmaher, B; Keller, M B

2016-04-01

Bipolar disorder (BP) frequently co-occurs with other psychiatric disorders. We examine whether course of anxiety disorders (ANX), attention deficit hyperactivity disorder (ADHD), disruptive behavior disorders (DBD), and substance use disorders (SUD) influence likelihood of recovery and recurrence of depression and mania in BP youth. Weekly ratings of psychiatric disorder intensity were obtained from 413 participants of the Course and Outcome of BP Youth project, followed for an average of 7.75 years. Multiple-event Cox proportional hazards regression analyses examined worsening of comorbid disorders as predictors of mood episode recovery and recurrence. Increased severity in ANX and SUD predicted longer time to recovery and less time to next depressive episode, and less time to next manic episode. Multivariate models with ANX and SUD found that significant effects of ANX remained, but SUD only predicted longer time to depression recovery. Increased severity of ADHD and DBD predicted shorter time to recurrence for depressive and manic episodes. There are significant time-varying relationships between the course of comorbid disorders and episodicity of depression and mania in BP youth. Worsening of comorbid conditions may present as a precursor to mood episode recurrence or warn of mood episode protraction. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Improving recognition of late life anxiety disorders in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition: observations and recommendations of the Advisory Committee to the Lifespan Disorders Work Group.

Science.gov (United States)

Mohlman, Jan; Bryant, Christina; Lenze, Eric J; Stanley, Melinda A; Gum, Amber; Flint, Alastair; Beekman, Aartjan T F; Wetherell, Julie Loebach; Thorp, Steven R; Craske, Michelle G

2012-06-01

Recognition of the significance of anxiety disorders in older adults is growing. The revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM) provides a timely opportunity to consider potential improvements to diagnostic criteria for psychiatric disorders for use with older people. The authors of this paper comprise the Advisory Committee to the DSM5 Lifespan Disorders Work Group, the purpose of which was to generate informative responses from individuals with clinical and research expertise in the field of late-life anxiety disorders. This paper reviews the unique features of anxiety in later life and synthesizes the work of the Advisory Committee. Suggestions are offered for refining our understanding of the effects of aging on anxiety and other disorders (e.g., mood disorders) and changes to the DSM5 criteria and text that could facilitate more accurate recognition and diagnosis of anxiety disorders in older adults. Several of the recommendations are not limited to the study of anxiety but rather are applicable across the broader field of geriatric mental health. DSM5 should provide guidelines for the thorough assessment of avoidance, excessiveness, and comorbid conditions (e.g., depression, medical illness, cognitive impairment) in anxious older adults. Copyright © 2011 John Wiley & Sons, Ltd.

Anxiety disorders and childhood maltreatment as predictors of outcome in bipolar disorder.

Science.gov (United States)

Pavlova, Barbara; Perroud, Nader; Cordera, Paolo; Uher, Rudolf; Alda, Martin; Dayer, Alexandre; Aubry, Jean-Michel

2018-01-01

Comorbid anxiety disorders and childhood maltreatment have each been linked with unfavourable outcomes in people with bipolar disorder. Because childhood maltreatment is associated with anxiety disorders in this population, their respective predictive value remains to be determined. In 174 adults with bipolar disorder, we assessed childhood maltreatment using the Childhood Trauma Questionnaire and lifetime anxiety disorders with the MINI International Neuropsychiatric Interview. We constructed an overall index of severity of bipolar disorder as a sum of six indicators (unemployment, psychotic symptoms, more than five manic episodes, more than five depressive episodes, suicide attempt, and hospital admission). We tested the relationship between childhood maltreatment, the number of anxiety disorders and the overall severity index using ordered logistic regression. The number of lifetime anxiety disorders was associated with the overall severity index (OR = 1.43, 95%CI = 1.01-2.04, p = 0.047). This relationship was only slightly attenuated when controlled for childhood maltreatment (OR = 1.39, 95%CI = 0.97-2.00, p = 0.069). The relationship between childhood maltreatment and the overall severity index was not statistically significant (OR = 1.26, 95%CI = 0.92-1.74, p = 0.151). Secondary analyses revealed that childhood maltreatment was associated with suicide attempts (OR = 1.70, 95%CI = 1.15-2.51, p = 0.008) and obsessive compulsive disorder was associated with the overall severity index (OR = 9.56, 95%CI = 2.20-41.47, p = 0.003). This was a cross-sectional study with a moderate-sized sample recruited from a specialist program. While comorbid anxiety disorders are associated with the overall severity of bipolar disorder, childhood maltreatment is specifically associated with suicide attempts. Clinicians should systematically assess both factors. Interventions to improve outcomes of people with bipolar disorder with comorbid anxiety disorders and history of childhood

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Science.gov (United States)

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Cai, T; Ebert, D D; Hwang, I; Li, J; de Jonge, P; Nierenberg, A A; Petukhova, M V; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2016-10-01

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Abdominoplasty Improves Quality of Life, Psychological Distress, and Eating Disorder Symptoms: A Prospective Study

Directory of Open Access Journals (Sweden)

Kai M. M. Saariniemi

2014-01-01

Full Text Available Background. Only some studies provide sufficient data regarding the effects of nonpostbariatric (aesthetic abdominoplasty on various aspects of quality of life. Nevertheless, when considering the effects on eating habits, publications are lacking. Therefore we decided to assess the effects of nonpostbariatric abdominoplasty on eating disorder symptoms, psychological distress, and quality of life. Materials and Methods. 64 consecutive women underwent nonpostbariatric abdominoplasty. Three outcome measures were completed: the Eating Disorder Inventory (EDI, Raitasalo’s modification of the Beck Depression Inventory (RBDI, and the 15D general quality of life questionnaire. Results. The mean age at baseline was 42 years and the mean body mass index (BMI 26.4. Fifty-three (83% women completed all the outcome measures with a mean follow-up time of 5 months. A significant improvement from baseline to follow-up was noted in women’s overall quality of life, body satisfaction, effectiveness, sexual functioning, and self-esteem. The women were significantly less depressive and had significantly less drive for thinness as well as bulimia, and their overall risk of developing an eating disorder also decreased significantly. Conclusions. Abdominoplasty results in significantly improved quality of life, body satisfaction, effectiveness, sexual functioning, self-esteem, and mental health. The risk of developing an eating disorder is decreased significantly. This trial is registered with Clinicaltrials.gov NCT02151799.

Anterior capsulotomy improves persistent developmental stuttering with a psychiatric disorder: a case report and literature review

Directory of Open Access Journals (Sweden)

Zhang SZ

2014-04-01

Full Text Available Shizhen Zhang,* Peng Li,* Zhujun Zhang, Wei WangDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People's Republic of China *These authors contributed equally to this workAbstract: Stuttering is characterized by disrupted fluency of verbal expression, and occurs mostly in children. Persistent developmental stuttering (PDS may occur in adults. Reports of the surgical management of PDS are limited. Here we present the case of a 28-year-old man who had had PDS since the age of 7 years, was diagnosed with depression and anxiety disorder at the age of 24 years, and had physical concomitants. He underwent a bilateral anterior capsulotomy 4 years after the diagnosis. Over one year of follow-up, his physical concomitants resolved, and significant improvements in his psychiatric disorders and PDS were observed. To the best of our knowledge, this is the first report of simultaneous improvement in a patient's PDS and psychiatric disorder after a bilateral anterior capsulotomy.Keywords: persistent developmental stuttering, psychiatric disorders, anterior capsulotomy

Weight Suppression But Not Symptom Improvement Predicts Weight Gain During Inpatient Treatment for Bulimia Nervosa.

Science.gov (United States)

Hessler, Johannes Baltasar; Diedrich, Alice; Greetfeld, Martin; Schlegl, Sandra; Schwartz, Caroline; Voderholzer, Ulrich

2018-03-01

Fear of gaining weight is a common obstacle to seeking treatment for bulimia nervosa (BN). We investigated changes in body mass index (BMI) during inpatient treatment for BN in relation to treatment outcome and weight suppression (WS). Female inpatients of a specialized eating disorders clinic were grouped as deteriorated/unchanged, reliably improved, and clinically significantly improved based on Eating Disorder Inventory-2 scores. Repeated measures ANOVA was employed to examine changes in BMI between admission and discharge depending on treatment outcome and WS. One-hundred seventy-nine patients were included. Overall, the average BMI significantly increased by 0.54 kg/m 2 (SD = 1.24). Repeated measures ANOVA revealed no association of change in BMI with treatment outcome [F(df) = 1.13 (2166), p = 0.327] but with WS [F(df) = 2.76 (3166), p Bulimia nervosa can be successfully treated without causing excessive weight gain. Patients with higher WS might expect somewhat more weight gain. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association.

Schizoaffective disorder

Science.gov (United States)

... or do not improve with treatment Thoughts of suicide or of harming others Alternative Names Mood disorder - schizoaffective disorder; Psychosis - schizoaffective disorder Images Schizoaffective disorder ...

Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

Science.gov (United States)

Ickes, Jacob C.

Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form

Predicting DMS-IV cluster B personality disorder criteria from MMPI-2 and Rorschach data: a test of incremental validity.

Science.gov (United States)

Blais, M A; Hilsenroth, M J; Castlebury, F; Fowler, J C; Baity, M R

2001-02-01

Despite their frequent conjoint clinical use, the incremental validity of Rorschach (Rorschach, 1921/1942) and MMPI (Hathaway & McKinley, 1943) data has not been adequately established, nor has any study to date explored the incremental validity of these tests for predicting Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM-IV]; American Psychiatric Association, 1994) personality disorders (PDs). In a reanalysis of existing data, we used select Rorschach variables and the MMPI PD scales to predict DSM-IV antisocial, borderline, histrionic, and narcissistic PD criteria in a sample of treatment-seeking outpatients. The correlational findings revealed alimited relation between Rorschach and MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) variables, with only 5 of 30 correlations reaching significance (p psychological characteristics of the DSM-IV Cluster B PDs.

Targeting Treatments to Improve Cognitive Function in Mood Disorder

DEFF Research Database (Denmark)

Miskowiak, Kamilla Woznica; Rush, A. John; Gerds, Thomas A.

2016-01-01

Learning Test (RAVLT) total recall with multiple logistic regression adjusted for diagnosis, age, gender, symptom severity, and education levels. RESULTS: We included 79 patients with an ICD-10 diagnosis of unipolar or bipolar disorder, of whom 39 received EPO and 40 received placebo (saline). For EPO......-treated patients with objective memory dysfunction at baseline (n = 16) (defined as RAVLT total recall ≤ 43), the odds of a clinically relevant memory improvement were increased by a factor of 290.6 (95% CI, 2.7-31,316.4; P = .02) compared to patients with no baseline impairment (n = 23). Subjective cognitive...

Generalized Anxiety Disorder and Hypoglycemia Symptoms Improved with Diet Modification.

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Aucoin, Monique; Bhardwaj, Sukriti

2016-01-01

Observational evidence suggests that a relationship may exist between high glycemic index diets and the development of anxiety and depression symptoms; however, as no interventional studies assessing this relationship in a psychiatric population have been completed, the possibility of a causal link is unclear. AB is a 15-year-old female who presented with concerns of generalized anxiety disorder and hypoglycemia symptoms. Her diet consisted primarily of refined carbohydrates. The addition of protein, fat, and fiber to her diet resulted in a substantial decrease in anxiety symptoms as well as a decrease in the frequency and severity of hypoglycemia symptoms. A brief return to her previous diet caused a return of her anxiety symptoms, followed by improvement when she restarted the prescribed diet. This case strengthens the hypothesis that dietary glycemic index may play a role in the pathogenesis or progression of mental illnesses such as generalized anxiety disorder and subsequently that dietary modification as a therapeutic intervention in the treatment of mental illness warrants further study.

Improved prediction of aerodynamic noise from wind turbines

Energy Technology Data Exchange (ETDEWEB)

Guidati, G.; Bareiss, R.; Wagner, S. [Univ. of Stuttgart, Inst. of Aerodynamics and Gasdynamics, Stuttgart (Germany)

1997-12-31

This paper focuses on an improved prediction model for inflow-turbulence noise which takes the true airfoil shape into account. Predictions are compared to the results of acoustic measurements on three 2D-models of 0.25 m chord. Two of the models have NACA-636xx airfoils of 12% and 18% relative thickness. The third airfoil was acoustically optimized by using the new prediction model. In the experiments the turbulence intensity of the flow was strongly increased by mounting a grid with 60 mm wide meshes and 12 mm thick rods onto the tunnel exhaust nozzle. The sound radiated from the airfoil was distinguished by the tunnel background noise by using an acoustic antenna consisting of a cross array of 36 microphones in total. An application of a standard beam-forming algorithm allows to determine how much noise is radiated from different parts of the models. This procedure normally results in a peak at the leading and trailing edge of the airfoil. The strength of the leading-edge peak is taken as the source strength for inflow-turbulence noise. (LN) 14 refs.

BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

Directory of Open Access Journals (Sweden)

Mihaela Simionescu

2014-12-01

Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: A Preliminary Study

Science.gov (United States)

Ruocco, Anthony C.; Rodrigo, Achala H.; McMain, Shelley F.; Page-Gould, Elizabeth; Ayaz, Hasan; Links, Paul S.

2016-01-01

Self-harm is a potentially lethal symptom of borderline personality disorder (BPD) that often improves with dialectical behavior therapy (DBT). While DBT is effective for reducing self-harm in many patients with BPD, a small but significant number of patients either does not improve in treatment or ends treatment prematurely. Accordingly, it is crucial to identify factors that may prospectively predict which patients are most likely to benefit from and remain in treatment. In the present preliminary study, 29 actively self-harming patients with BPD completed brain-imaging procedures probing activation of the prefrontal cortex (PFC) during impulse control prior to beginning DBT and after 7 months of treatment. Patients that reduced their frequency of self-harm the most over treatment displayed lower levels of neural activation in the bilateral dorsolateral prefrontal cortex (DLPFC) prior to beginning treatment, and they showed the greatest increases in activity within this region after 7 months of treatment. Prior to starting DBT, treatment non-completers demonstrated greater activation than treatment-completers in the medial PFC and right inferior frontal gyrus. Reductions in self-harm over the treatment period were associated with increases in activity in right DLPFC even after accounting for improvements in depression, mania, and BPD symptom severity. These findings suggest that pre-treatment patterns of activation in the PFC underlying impulse control may be prospectively associated with improvements in self-harm and treatment attrition for patients with BPD treated with DBT. PMID:27242484

Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: A Preliminary Study

Directory of Open Access Journals (Sweden)

Anthony Charles Ruocco

2016-05-01

Full Text Available Self-harm is a potentially lethal symptom of borderline personality disorder (BPD that often improves with dialectical behavior therapy (DBT. While DBT is effective for reducing self-harm in many patients with BPD, a small but significant number of patients either does not improve in treatment or ends treatment prematurely. Accordingly, it is crucial to identify factors that may prospectively predict which patients are most likely to benefit from and remain in treatment. In the present preliminary study, twenty-nine actively self-harming patients with BPD completed brain-imaging procedures probing activation of the prefrontal cortex during impulse control prior to beginning DBT and after seven months of treatment. Patients that reduced their frequency of self-harm the most over treatment displayed lower levels of neural activation in the bilateral dorsolateral prefrontal cortex prior to beginning treatment, and they showed the greatest increases in activity within this region after seven months of treatment. Prior to starting DBT, treatment non-completers demonstrated greater activation than treatment-completers in the medial prefrontal cortex and right inferior frontal gyrus. Reductions in self-harm over the treatment period were associated with increases in activity in right dorsolateral prefrontal cortex even after accounting for improvements in depression, mania, and BPD symptom severity. These findings suggest that pre-treatment patterns of activation in the prefrontal cortex underlying impulse control may be prospectively associated with improvements in self-harm and treatment attrition for patients with BPD treated with DBT.

Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: A Preliminary Study.

Science.gov (United States)

Ruocco, Anthony C; Rodrigo, Achala H; McMain, Shelley F; Page-Gould, Elizabeth; Ayaz, Hasan; Links, Paul S

2016-01-01

Self-harm is a potentially lethal symptom of borderline personality disorder (BPD) that often improves with dialectical behavior therapy (DBT). While DBT is effective for reducing self-harm in many patients with BPD, a small but significant number of patients either does not improve in treatment or ends treatment prematurely. Accordingly, it is crucial to identify factors that may prospectively predict which patients are most likely to benefit from and remain in treatment. In the present preliminary study, 29 actively self-harming patients with BPD completed brain-imaging procedures probing activation of the prefrontal cortex (PFC) during impulse control prior to beginning DBT and after 7 months of treatment. Patients that reduced their frequency of self-harm the most over treatment displayed lower levels of neural activation in the bilateral dorsolateral prefrontal cortex (DLPFC) prior to beginning treatment, and they showed the greatest increases in activity within this region after 7 months of treatment. Prior to starting DBT, treatment non-completers demonstrated greater activation than treatment-completers in the medial PFC and right inferior frontal gyrus. Reductions in self-harm over the treatment period were associated with increases in activity in right DLPFC even after accounting for improvements in depression, mania, and BPD symptom severity. These findings suggest that pre-treatment patterns of activation in the PFC underlying impulse control may be prospectively associated with improvements in self-harm and treatment attrition for patients with BPD treated with DBT.

Mother chair reparation to decrease subjective disorders in exclusive breast-feeding period

Science.gov (United States)

Santiana, M. A.; Yusuf, M.; Lokantara, W. D.

2018-01-01

Exclusive breastfeeding is the responsibility of the mother after childbirth. A specific constraint arise for the mother when during the breastfeeding process, the place is not in accordance with the physiological condition of the mother's body. A not physiologically corrected lactation place will cause subjective disorders for breastfeeding mothers. Complaints that arise include quick tiredness, with certain muscles sore and pain, which will ultimately decrease the motivation of the mothers to perform exclusive breastfeeding especially in the first six months of the baby's birth. An improved ergonomic designed chair, this research used experimental method with group within treatment (treatment by subject) to solve the problem. The study took place in Maternity Clinic “CB” Badung regency, Bali. Subjective disorders are measured based on general fatigue and musculoskeletal disorders mothers breastfeeding. Fatigue is predicted using 30 items of questionnaires while musculoskeletal compaints are predicted from the Nordic Body Map questionnaire. Data were analyzed descriptively and inferentially in an experiment condition using using t-pair test. The results showed that there were significant differences in fatigue in general and skeletal musculoskeletal disorders between treatment 1 (using old chair) with treatment 2 (using repaired seats) in breastfeeding mothers. Fatigue in general decreased by 35.6% and skeletal musculoskeletal disorders decreased by 26.8%. It was concluded that improved breastfeeding mothers' seats may decrease subjective disorders during exclusive breastfeeding. It is therefore advisable for breastfeeding mothers to use seats that match their anthropometry.

Monoamine Oxidase-A Genetic Variants and Childhood Abuse Predict Impulsiveness in Borderline Personality Disorder.

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Kolla, Nathan J; Meyer, Jeffrey; Sanches, Marcos; Charbonneau, James

2017-11-30

Impulsivity is a core feature of borderline personality disorder (BPD) and antisocial personality disorder (ASPD) that likely arises from combined genetic and environmental influences. The interaction of the low activity variant of the monoamine oxidase-A (MAOA-L) gene and early childhood adversity has been shown to predict aggression in clinical and non-clinical populations. Although impulsivity is a risk factor for aggression in BPD and ASPD, little research has investigated potential gene-environment (G×E) influences impacting its expression in these conditions. Moreover, G×E interactions may differ by diagnosis. Full factorial analysis of variance was employed to investigate the influence of monoamine oxidase-A (MAO-A) genotype, childhood abuse, and diagnosis on Barratt Impulsiveness Scale-11 (BIS-11) scores in 61 individuals: 20 subjects with BPD, 18 subjects with ASPD, and 23 healthy controls. A group×genotype×abuse interaction was present (F(2,49)=4.4, p =0.018), such that the interaction of MAOA-L and childhood abuse predicted greater BIS-11 motor impulsiveness in BPD. Additionally, BPD subjects reported higher BIS-11 attentional impulsiveness versus ASPD participants (t(1,36)=2.3, p =0.025). These preliminary results suggest that MAOA-L may modulate the impact of childhood abuse on impulsivity in BPD. Results additionally indicate that impulsiveness may be expressed differently in BPD and ASPD.

A novel method for improved accuracy of transcription factor binding site prediction

KAUST Repository

Khamis, Abdullah M.; Motwalli, Olaa Amin; Oliva, Romina; Jankovic, Boris R.; Medvedeva, Yulia; Ashoor, Haitham; Essack, Magbubah; Gao, Xin; Bajic, Vladimir B.

2018-01-01

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

A novel method for improved accuracy of transcription factor binding site prediction

KAUST Repository

Khamis, Abdullah M.

2018-03-20

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

Predicting Dropout from Intensive Outpatient Cognitive Behavioural Therapy for Binge Eating Disorder Using Pre-treatment Characteristics: A Naturalistic Study.

Science.gov (United States)

Vroling, Maartje S; Wiersma, Femke E; Lammers, Mirjam W; Noorthoorn, Eric O

2016-11-01

Dropout rates in binge eating disorder (BED) treatment are high (17-30%), and predictors of dropout are unknown. Participants were 376 patients following an intensive outpatient cognitive behavioural therapy programme for BED, 82 of whom (21.8%) dropped out of treatment. An exploratory logistic regression was performed using eating disorder variables, general psychopathology, personality and demographics to identify predictors of dropout. Binge eating pathology, preoccupations with eating, shape and weight, social adjustment, agreeableness, and social embedding appeared to be significant predictors of dropout. Also, education showed an association to dropout. This is one of the first studies investigating pre-treatment predictors for dropout in BED treatment. The total explained variance of the prediction model was low, yet the model correctly classified 80.6% of cases, which is comparable to other dropout studies in eating disorders. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.

Acute Bouts of Exercising Improved Mood, Rumination and Social Interaction in Inpatients With Mental Disorders

Science.gov (United States)

Brand, Serge; Colledge, Flora; Ludyga, Sebastian; Emmenegger, Raphael; Kalak, Nadeem; Sadeghi Bahmani, Dena; Holsboer-Trachsler, Edith; Pühse, Uwe; Gerber, Markus

2018-01-01

Background: Studies at the macro level (such as longer-term interventions) showed that physical activity impacts positively on cognitive-emotional processes of patients with mental disorders. However, research focusing on the immediate impact of acute bouts of exercise (micro level) are missing. The aim of the present study was therefore to investigate whether and to what extent single bouts of moderately intense exercise can influence dimensions of psychological functioning in inpatients with mental disorders. Method: 129 inpatients (mean age: 38.16 years; 50.4% females) took part and completed a questionnaire both immediately before and immediately after exercising. Thirty inpatients completed the questionnaires a second time in the same week. The questionnaire covered socio-demographic and illness-related information. Further, the questionnaire asked about current psychological states such as mood, rumination, social interactions, and attention, tiredness, and physical strengths as a proxy of physiological states. Results: Psychological states improved from pre- to post-session. Improvements were observed for mood, social interactions, attention, and physical strengths. Likewise, rumination and tiredness decreased. Mood, rumination, and tiredness further improved, when patients completed the questionnaires the second time in the same week. Conclusion: At micro level, single bouts of exercise impacted positively on cognitive-emotional processes such as mood, rumination, attention and social interactions, and physiological states of tiredness and physical strengths among inpatients with mental disorders. In addition, further improvements were observed, if patients participated in physical activities a second time. PMID:29593592

Acute Bouts of Exercising Improved Mood, Rumination and Social Interaction in Inpatients With Mental Disorders

Directory of Open Access Journals (Sweden)

Serge Brand

2018-03-01

Full Text Available Background: Studies at the macro level (such as longer-term interventions showed that physical activity impacts positively on cognitive-emotional processes of patients with mental disorders. However, research focusing on the immediate impact of acute bouts of exercise (micro level are missing. The aim of the present study was therefore to investigate whether and to what extent single bouts of moderately intense exercise can influence dimensions of psychological functioning in inpatients with mental disorders.Method: 129 inpatients (mean age: 38.16 years; 50.4% females took part and completed a questionnaire both immediately before and immediately after exercising. Thirty inpatients completed the questionnaires a second time in the same week. The questionnaire covered socio-demographic and illness-related information. Further, the questionnaire asked about current psychological states such as mood, rumination, social interactions, and attention, tiredness, and physical strengths as a proxy of physiological states.Results: Psychological states improved from pre- to post-session. Improvements were observed for mood, social interactions, attention, and physical strengths. Likewise, rumination and tiredness decreased. Mood, rumination, and tiredness further improved, when patients completed the questionnaires the second time in the same week.Conclusion: At micro level, single bouts of exercise impacted positively on cognitive-emotional processes such as mood, rumination, attention and social interactions, and physiological states of tiredness and physical strengths among inpatients with mental disorders. In addition, further improvements were observed, if patients participated in physical activities a second time.

Acute Bouts of Exercising Improved Mood, Rumination and Social Interaction in Inpatients With Mental Disorders.

Science.gov (United States)

Brand, Serge; Colledge, Flora; Ludyga, Sebastian; Emmenegger, Raphael; Kalak, Nadeem; Sadeghi Bahmani, Dena; Holsboer-Trachsler, Edith; Pühse, Uwe; Gerber, Markus

2018-01-01

Background: Studies at the macro level (such as longer-term interventions) showed that physical activity impacts positively on cognitive-emotional processes of patients with mental disorders. However, research focusing on the immediate impact of acute bouts of exercise (micro level) are missing. The aim of the present study was therefore to investigate whether and to what extent single bouts of moderately intense exercise can influence dimensions of psychological functioning in inpatients with mental disorders. Method: 129 inpatients (mean age: 38.16 years; 50.4% females) took part and completed a questionnaire both immediately before and immediately after exercising. Thirty inpatients completed the questionnaires a second time in the same week. The questionnaire covered socio-demographic and illness-related information. Further, the questionnaire asked about current psychological states such as mood, rumination, social interactions, and attention, tiredness, and physical strengths as a proxy of physiological states. Results: Psychological states improved from pre- to post-session. Improvements were observed for mood, social interactions, attention, and physical strengths. Likewise, rumination and tiredness decreased. Mood, rumination, and tiredness further improved, when patients completed the questionnaires the second time in the same week. Conclusion: At micro level, single bouts of exercise impacted positively on cognitive-emotional processes such as mood, rumination, attention and social interactions, and physiological states of tiredness and physical strengths among inpatients with mental disorders. In addition, further improvements were observed, if patients participated in physical activities a second time.

Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

International Nuclear Information System (INIS)

Wurzbach, Richard N.

2002-01-01

On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

Personality and Defense Styles: Clinical Specificities and Predictive Factors of Alcohol Use Disorder in Women.

Science.gov (United States)

Ribadier, Aurélien; Dorard, Géraldine; Varescon, Isabelle

2016-01-01

This study investigated personality traits and defense styles in order to determine clinical specificities and predictive factors of alcohol use disorders (AUDs) in women. A female sample, composed of AUD outpatients (n = 48) and a control group (n = 50), completed a sociodemographic self-report and questionnaires assessing personality traits (BFI), defense mechanisms and defense styles (DSQ-40). Comparative and correlational analyses, as well as univariate and multivariate logistic regressions, were performed. AUD women presented with higher neuroticism and lower extraversion and conscientiousness. They used less mature and more neurotic and immature defense styles than the control group. Concerning personality traits, high neuroticism and lower conscientiousness were predictive of AUD, as well as low mature, high neurotic, and immature defense styles. Including personality traits and defense styles in a logistic model, high neuroticism was the only AUD predictive factor. AUD women presented clinical specificities and predictive factors in personality traits and defense styles that must be taken into account in AUD studies. Implications for specific treatment for women are discussed.

Stereotype confirmation concerns predict dropout from cognitive behavioral therapy for social anxiety disorder.

Science.gov (United States)

Johnson, Suzanne; Price, Matthew; Mehta, Natasha; Anderson, Page L

2014-08-19

There are high attrition rates observed in efficacy studies for social anxiety disorder, and research has not identified consistent nor theoretically meaningful predictors of dropout. Pre-treatment symptom severity and demographic factors, such as age and gender, are sometimes predictive of dropout. The current study examines a theoretically meaningful predictor of attrition based on experiences associated with social group membership rather than differences between social group categories--fear of confirming stereotypes. This is a secondary data analysis of a randomized controlled trial comparing two cognitive behavioral treatments for social anxiety disorder: virtual reality exposure therapy and exposure group therapy. Participants (N = 74) with a primary diagnosis of social anxiety disorder who were eligible to participate in the parent study and who self-identified as either "African American" (n = 31) or "Caucasian" (n = 43) completed standardized self-report measures of stereotype confirmation concerns (SCC) and social anxiety symptoms as part of a pre-treatment assessment battery. Hierarchical logistic regression showed that greater stereotype confirmation concerns were associated with higher dropout from therapy--race, age, gender, and pre-treatment symptom severity were not. Group treatment also was associated with higher dropout. These findings urge further research on theoretically meaningful predictors of attrition and highlight the importance of addressing cultural variables, such as the experience of stereotype confirmation concerns, during treatment of social anxiety to minimize dropout from therapy.

Differentiating normal and disordered personality using the General Assessment of Personality Disorder (GAPD).

Science.gov (United States)

Hentschel, Annett G; John Livesley, W

2013-05-01

Criteria to differentiate personality disorder from extremes of normal personality variations are important given growing interest in dimensional classification because an extreme level of a personality dimension does not necessarily indicate disorder. The DSM-5 proposed classification of personality disorder offers a definition of general personality disorder based on chronic interpersonal and self/identity pathology. The ability of this approach to differentiate personality disorder from other mental disorders was evaluated using a self-report questionnaire, the General Assessment of Personality Disorder (GAPD). This measure was administered to a sample of psychiatric patients (N = 149) from different clinical sub-sites. Patients were divided into personality disordered and non-personality disordered groups on the basis of the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II). The results showed a hit rate of 82% correct identified patients and a good accuracy of the predicted model. There was a substantial agreement between SCID-II interview and GAPD personality disorder diagnoses. The GAPD appears to predict personality disorder in general, which provides support of the DSM-5 general diagnostic criteria of personality disorder. Copyright © 2012 John Wiley & Sons, Ltd.

Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

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Choi, Jonghwan; Park, Sanghyun; Yoon, Youngmi; Ahn, Jaegyoon

2017-11-15

Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. https://github.com/mathcom/CPR. jgahn@inu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Stability and Change in Personality Disorder Symptoms in 1-Year Follow-up of Depressed Adolescent Outpatients.

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Strandholm, Thea; Kiviruusu, Olli; Karlsson, Linnea; Pankakoski, Maiju; Pelkonen, Mirjami; Marttunen, Mauri

2017-01-01

We investigated stability and change in personality disorder (PD) symptoms and whether depression severity, comorbid clinical psychiatric disorders, and social support predict changes in personality pathology among adolescent outpatients. The 1-year outcome of PD symptoms among consecutive adolescent psychiatric outpatients with depressive disorders (N = 189) was investigated with symptom count of depression, comorbid psychiatric disorders, and perceived social support as predictors. An overall decrease in PD symptoms in most PD categories was observed. Decreases in depression severity and in number of comorbid diagnoses correlated positively with decreases in PD symptoms of most PD categories. Social support from close friends predicted a decrease in schizotypal and narcissistic, whereas support from family predicted a decrease in paranoid symptoms. Our results suggest that among depressed adolescent outpatients, PD symptoms are relatively unstable, changes co-occuring with changes/improvement in overall psychopathology. Social support seems a possibly effective point for intervention efforts regarding positive outcome of PD symptoms.

Health-related quality of life 6 months after burns among hospitalized patients: Predictive importance of mental disorders and burn severity.

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Palmu, Raimo; Partonen, Timo; Suominen, Kirsi; Saarni, Samuli I; Vuola, Jyrki; Isometsä, Erkki

2015-06-01

Major burns are likely to have a strong impact on health-related quality of life (HRQoL). We investigated the level of and predictors for quality of life at 6 months after acute burn. Consecutive acute adult burn patients (n=107) admitted to the Helsinki Burn Centre were examined with a structured diagnostic interview (SCID) at baseline, and 92 patients (86%) were re-examined at 6 months after injury. During follow-up 55% (51/92) suffered from at least one mental disorder. The mean %TBSA was 9. TBSA of men did not differ from that of women. Three validated instruments (RAND-36, EQ-5, 15D) were used to evaluate the quality of life at 6 months. All the measures (RAND-36, EQ-5, 15D) consistently indicated mostly normal HRQoL at 6 months after burn. In the multivariate linear regression model, %TBSA predicted HRQoL in one dimension (role limitations caused by physical health problems, p=0.039) of RAND-36. In contrast, mental disorders overall and particularly major depressive disorder (MDD) during follow-up (p-values of 0.001-0.002) predicted poor HRQoL in all dimensions of RAND-36. HRQoL of women was worse than that of men. Self-perceived HRQoL among acute burn patients at 6 months after injury seems to be mostly as good as in general population studies in Finland. The high standard of acute treatment and the inclusion of small burns (%TBSAburn itself on HRQoL. Mental disorders strongly predicted HRQoL at 6 months. Copyright © 2014 Elsevier Ltd and ISBI. All rights reserved.

Relationship between personality disorder functioning styles and the emotional states in bipolar I and II disorders.

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Jiashu Yao

Full Text Available Bipolar disorder types I (BD I and II (BD II behave differently in clinical manifestations, normal personality traits, responses to pharmacotherapies, biochemical backgrounds and neuroimaging activations. How the varied emotional states of BD I and II are related to the comorbid personality disorders remains to be settled.We therefore administered the Plutchick - van Praag Depression Inventory (PVP, the Mood Disorder Questionnaire (MDQ, the Hypomanic Checklist-32 (HCL-32, and the Parker Personality Measure (PERM in 37 patients with BD I, 34 BD II, and in 76 healthy volunteers.Compared to the healthy volunteers, patients with BD I and II scored higher on some PERM styles, PVP, MDQ and HCL-32 scales. In BD I, the PERM Borderline style predicted the PVP scale; and Antisocial predicted HCL-32. In BD II, Borderline, Dependent, Paranoid (- and Schizoid (- predicted PVP; Borderline predicted MDQ; Passive-Aggressive and Schizoid (- predicted HCL-32. In controls, Borderline and Narcissistic (- predicted PVP; Borderline and Dependent (- predicted MDQ.Besides confirming the different predictability of the 11 functioning styles of personality disorder to BD I and II, we found that the prediction was more common in BD II, which might underlie its higher risk of suicide and poorer treatment outcome.

Relationship between Personality Disorder Functioning Styles and the Emotional States in Bipolar I and II Disorders

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Yao, Jiashu; Xu, You; Qin, Yanhua; Liu, Jing; Shen, Yuedi; Wang, Wei; Chen, Wei

2015-01-01

Background Bipolar disorder types I (BD I) and II (BD II) behave differently in clinical manifestations, normal personality traits, responses to pharmacotherapies, biochemical backgrounds and neuroimaging activations. How the varied emotional states of BD I and II are related to the comorbid personality disorders remains to be settled. Methods We therefore administered the Plutchick – van Praag Depression Inventory (PVP), the Mood Disorder Questionnaire (MDQ), the Hypomanic Checklist-32 (HCL-32), and the Parker Personality Measure (PERM) in 37 patients with BD I, 34 BD II, and in 76 healthy volunteers. Results Compared to the healthy volunteers, patients with BD I and II scored higher on some PERM styles, PVP, MDQ and HCL-32 scales. In BD I, the PERM Borderline style predicted the PVP scale; and Antisocial predicted HCL-32. In BD II, Borderline, Dependant, Paranoid (-) and Schizoid (-) predicted PVP; Borderline predicted MDQ; Passive-Aggressive and Schizoid (-) predicted HCL-32. In controls, Borderline and Narcissistic (-) predicted PVP; Borderline and Dependant (-) predicted MDQ. Conclusion Besides confirming the different predictability of the 11 functioning styles of personality disorder to BD I and II, we found that the prediction was more common in BD II, which might underlie its higher risk of suicide and poorer treatment outcome. PMID:25625553

Attitudes to emotional expression and personality in predicting post-traumatic stress disorder.

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Nightingale, J; Williams, R M

2000-09-01

To test hypotheses derived from a suggestion of Williams (1989) that negative attitudes towards emotional expression act as a predisposing or maintaining factor for post-traumatic stress reactions following a traumatic event. The study employed a prospective design in which attitudes to emotional expression, the 'Big Five' personality factors (Costa & McCrae, 1992a) and initial symptoms and injury severity within 1 week of a road traffic accident were used to predict the development of post-traumatic stress disorder 6 weeks post-accident. Sixty victims of road traffic accidents randomly selected from attenders at a large A&E department were assessed by questionnaire and interview. Measures comprised a 4-item scale relating to emotional expression, standardized scales for intrusion and avoidance features of traumatic experiences, and for anxiety and depression and the NEO-FFI Five Factor Personality Inventory. Forty-five of these participants responded to a postal questionnaire follow-up. In this survey the battery was repeated and also included a self-report diagnostic measure of post-traumatic stress disorder (PTSD). The percentage of the sample meeting DSM-IV diagnostic criteria for PTSD at 6 weeks post-trauma was 30.8%. A small but significant relationship was found for negative attitudes to emotional expression at 1 week to predict intrusive symptoms and diagnosis at 6 weeks, over and above the independent relationships of initial symptoms, initial injury severity, personality and coping. The emotional expression measure was largely stable between the two points of measurement. More negative attitudes to emotional expression were related to less openness, extraversion and agreeableness personality domains. Some support for the hypotheses was found in relation to the development of PTSD and for the status of attitudes to emotion as a stable trait related to personality factors. The potential importance of attitudes to emotional expression in therapy and other

Formal thought disorder in autism spectrum disorder predicts future symptom severity, but not psychosis prodrome

NARCIS (Netherlands)

Eussen, M.L.J.M.; de Bruin, E.I.; van Gool, A.R.; Louwerse, E.S.; van der Ende, J.; Verheij, F.; Verhulst, F.C.; Greaves-Lord, K.

2015-01-01

Formal thought disorder (FTD) is a disruption in the flow of thought, which is inferred from disorganisation of spoken language. FTD in autism spectrum disorders (ASD) might be a precursor of psychotic disorders or a manifestation of ASD symptom severity. The current longitudinal study is a

Improvement in cerebral function with treatment of posttraumatic stress disorder.

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Roy, Michael J; Francis, Jennifer; Friedlander, Joshua; Banks-Williams, Lisa; Lande, Raymond G; Taylor, Patricia; Blair, James; McLellan, Jennifer; Law, Wendy; Tarpley, Vanita; Patt, Ivy; Yu, Henry; Mallinger, Alan; Difede, Joann; Rizzo, Albert; Rothbaum, Barbara

2010-10-01

Posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are signature illnesses of the Iraq and Afghanistan wars, but current diagnostic and therapeutic measures for these conditions are suboptimal. In our study, functional magnetic resonance imaging (fMRI) is used to try to differentiate military service members with: PTSD and mTBI, PTSD alone, mTBI alone, and neither PTSD nor mTBI. Those with PTSD are then randomized to virtual reality exposure therapy or imaginal exposure. fMRI is repeated after treatment and along with the Clinician-Administered PTSD Scale (CAPS) and Clinical Global Impression (CGI) scores to compare with baseline. Twenty subjects have completed baseline fMRI scans, including four controls and one mTBI only; of 15 treated for PTSD, eight completed posttreatment scans. Most subjects have been male (93%) and Caucasian (83%), with a mean age of 34. Significant improvements are evident on fMRI scans, and corroborated by CGI scores, but CAPS scores improvements are modest. In conclusion, CGI scores and fMRI scans indicate significant improvement in PTSD in both treatment arms, though CAPS score improvements are less robust. © 2010 Association for Research in Nervous and Mental Disease.

Incorporating Scale-Dependent Fracture Stiffness for Improved Reservoir Performance Prediction

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Crawford, B. R.; Tsenn, M. C.; Homburg, J. M.; Stehle, R. C.; Freysteinson, J. A.; Reese, W. C.

2017-12-01

We present a novel technique for predicting dynamic fracture network response to production-driven changes in effective stress, with the potential for optimizing depletion planning and improving recovery prediction in stress-sensitive naturally fractured reservoirs. A key component of the method involves laboratory geomechanics testing of single fractures in order to develop a unique scaling relationship between fracture normal stiffness and initial mechanical aperture. Details of the workflow are as follows: tensile, opening mode fractures are created in a variety of low matrix permeability rocks with initial, unstressed apertures in the micrometer to millimeter range, as determined from image analyses of X-ray CT scans; subsequent hydrostatic compression of these fractured samples with synchronous radial strain and flow measurement indicates that both mechanical and hydraulic aperture reduction varies linearly with the natural logarithm of effective normal stress; these stress-sensitive single-fracture laboratory observations are then upscaled to networks with fracture populations displaying frequency-length and length-aperture scaling laws commonly exhibited by natural fracture arrays; functional relationships between reservoir pressure reduction and fracture network porosity, compressibility and directional permeabilities as generated by such discrete fracture network modeling are then exported to the reservoir simulator for improved naturally fractured reservoir performance prediction.

Current Status of Animal Models of Posttraumatic Stress Disorder: Behavioral and Biological Phenotypes, and Future Challenges in Improving Translation.

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Deslauriers, Jessica; Toth, Mate; Der-Avakian, Andre; Risbrough, Victoria B

2018-05-15

Increasing predictability of animal models of posttraumatic stress disorder (PTSD) has required active collaboration between clinical and preclinical scientists. Modeling PTSD is challenging, as it is a heterogeneous disorder with ≥20 symptoms. Clinical research increasingly utilizes objective biological measures (e.g., imaging, peripheral biomarkers) or nonverbal behaviors and/or physiological responses to complement verbally reported symptoms. This shift toward more-objectively measurable phenotypes enables refinement of current animal models of PTSD, and it supports the incorporation of homologous measures across species. We reviewed >600 articles to examine the ability of current rodent models to probe biological phenotypes of PTSD (e.g., sleep disturbances, hippocampal and fear-circuit dysfunction, inflammation, glucocorticoid receptor hypersensitivity) in addition to behavioral phenotypes. Most models reliably produced enduring generalized anxiety-like or depression-like behaviors, as well as hyperactive fear circuits, glucocorticoid receptor hypersensitivity, and response to long-term selective serotonin reuptake inhibitors. Although a few paradigms probed fear conditioning/extinction or utilized peripheral immune, sleep, and noninvasive imaging measures, we argue that these should be incorporated more to enhance translation. Data on female subjects, on subjects at different ages across the life span, or on temporal trajectories of phenotypes after stress that can inform model validity and treatment study design are needed. Overall, preclinical (and clinical) PTSD researchers are increasingly incorporating homologous biological measures to assess markers of risk, response, and treatment outcome. This shift is exciting, as we and many others hope it not only will support translation of drug efficacy from animal models to clinical trials but also will potentially improve predictability of stage II for stage III clinical trials. Published by Elsevier Inc.

Generalized Anxiety Disorder and Hypoglycemia Symptoms Improved with Diet Modification

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Monique Aucoin

2016-01-01

Full Text Available Observational evidence suggests that a relationship may exist between high glycemic index diets and the development of anxiety and depression symptoms; however, as no interventional studies assessing this relationship in a psychiatric population have been completed, the possibility of a causal link is unclear. AB is a 15-year-old female who presented with concerns of generalized anxiety disorder and hypoglycemia symptoms. Her diet consisted primarily of refined carbohydrates. The addition of protein, fat, and fiber to her diet resulted in a substantial decrease in anxiety symptoms as well as a decrease in the frequency and severity of hypoglycemia symptoms. A brief return to her previous diet caused a return of her anxiety symptoms, followed by improvement when she restarted the prescribed diet. This case strengthens the hypothesis that dietary glycemic index may play a role in the pathogenesis or progression of mental illnesses such as generalized anxiety disorder and subsequently that dietary modification as a therapeutic intervention in the treatment of mental illness warrants further study.

Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

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Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-06-23

Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd

Improving the accuracy of protein secondary structure prediction using structural alignment

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Gallin Warren J

2006-06-01

Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction

Science.gov (United States)

2017-12-01

19 NIH Exploiting drivers of androgen receptor signaling negative prostate cancer for precision medicine Goal(s): Identify novel potential drivers...AWARD NUMBER: W81XWH-14-1-0466 TITLE: Clonal evaluation of prostate cancer by ERG/SPINK1 status to improve prognosis prediction PRINCIPAL...Sept 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction 5b

The predictive power of family history measures of alcohol and drug problems and internalizing disorders in a college population.

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Kendler, Kenneth S; Edwards, Alexis; Myers, John; Cho, Seung Bin; Adkins, Amy; Dick, Danielle

2015-07-01

A family history (FH) of psychiatric and substance use problems is a potent risk factor for common internalizing and externalizing disorders. In a large web-based assessment of mental health in college students, we developed a brief set of screening questions for a FH of alcohol problems (AP), drug problems (DP) and depression-anxiety in four classes of relatives (father, mother, aunts/uncles/grandparents, and siblings) as reported by the student. Positive reports of a history of AP, DP, and depression-anxiety were substantially correlated within relatives. These FH measures predicted in the student, in an expected pattern, dimensions of personality and impulsivity, alcohol consumption and problems, smoking and nicotine dependence, use of illicit drugs, and symptoms of depression and anxiety. Using the mean score from the four classes of relatives was more predictive than using a familial/sporadic dichotomy. Interactions were seen between the FH of AP, DP, and depression-anxiety and peer deviance in predicting symptoms of alcohol and tobacco dependence. As the students aged, the FH of AP became a stronger predictor of alcohol problems. While we cannot directly assess the validity of these FH reports, the pattern of findings suggest that our brief screening items were able to assess, with some accuracy, the FH of substance misuse and internalizing psychiatric disorders in relatives. If correct, these measures can play an important role in the creation of developmental etiologic models for substance and internalizing psychiatric disorders which constitute one of the central goals of the overall project. © 2015 Wiley Periodicals, Inc.

Attention–memory training yields behavioral and academic improvements in children diagnosed with attention-deficit hyperactivity disorder comorbid with a learning disorder

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Farias AC

2017-07-01

Full Text Available Antonio Carlos Farias,1–4 Mara L Cordeiro,1,2,5 Erico PG Felden,6 Tiago S Bara,1,2 Cássia R Benko,1,2 Daniele Coutinho,1,2 Leandra F Martins,2 Rosilda TC Ferreira,1,2 James T McCracken5 1Faculdades Pequeno Príncipe, 2Neurosciences Core, Pelé Pequeno Príncipe Research Institute, Curitiba, 3Department of Neuropediatrics, Children’s Hospital, Pequeno Príncipe, 4School of Medicine, University Positivo, Curitiba, Brazil; 5Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, US; 6Center for Health Science Research, Santa Catarina State University, Florianópolis, Brazil Background: Recent studies have suggested that children with attention-deficit hyperactivity disorder (ADHD may benefit from computerized cognitive training. Therapy implementation is especially complicated when ADHD is associated with learning disorders (LDs. This study tested the efficacy of a computer-based cognitive training program, namely, computerized cognitive training (CCT, in children with ADHD comorbid with an LD (ADHD-LD, with or without psychostimulant medication. Materials and methods: After diagnostic evaluations, 27 children with ADHD-LD (8 unmedicated and 19 medicated participated in CCT, which is intended to improve attention, memory, reasoning, visual processing, and executive functioning. The participants completed 24 1-hour sessions over 3 months. Neuropsychometric and standardized academic test results before and after training were compared to assess treatment efficacy. Shapiro–Wilk normality tests were applied, and subsequent Wilcoxon tests were used to identify significant differences in pre- versus post-training performance. Results: After CAT, children diagnosed with ADHD-LD showed 1 improvements in trained skills, measured directly within the software and indirectly by external psychometric tests; 2 improvements in

Self-mutilation and suicide attempts: relationships to bipolar disorder, borderline personality disorder, temperament and character.

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Joyce, Peter R; Light, Katrina J; Rowe, Sarah L; Cloninger, C Robert; Kennedy, Martin A

2010-03-01

Self-mutilation has traditionally been associated with borderline personality disorder, and seldom examined separately from suicide attempts. Clinical experience suggests that self-mutilation is common in bipolar disorder. A family study was conducted on the molecular genetics of depression and personality, in which the proband had been treated for depression. All probands and parents or siblings were interviewed with a structured interview and completed the Temperament and Character Inventory. Fourteen per cent of subjects interviewed reported a history of self-mutilation, mostly by wrist cutting. Self-mutilation was more common in bipolar I disorder subjects then in any other diagnostic groups. In multiple logistic regression self-mutilation was predicted by mood disorder diagnosis and harm avoidance, but not by borderline personality disorder. Furthermore, the relatives of non-bipolar depressed probands with self-mutilation had higher rates of bipolar I or II disorder and higher rates of self-mutilation. Sixteen per cent of subjects reported suicide attempts and these were most common in those with bipolar I disorder and in those with borderline personality disorder. On multiple logistic regression, however, only mood disorder diagnosis and harm avoidance predicted suicide attempts. Suicide attempts, unlike self-mutilation, were not familial. Self-mutilation and suicide attempts are only partially overlapping behaviours, although both are predicted by mood disorder diagnosis and harm avoidance. Self-mutilation has a particularly strong association with bipolar disorder. Clinicians need to think of bipolar disorder, not borderline personality disorder, when assessing an individual who has a history of self-mutilation.

Prediction of incidence and stability of alcohol use disorders by latent internalizing psychopathology risk profiles in adolescence and young adulthood.

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Behrendt, Silke; Bühringer, Gerhard; Höfler, Michael; Lieb, Roselind; Beesdo-Baum, Katja

2017-10-01

Comorbid internalizing mental disorders in alcohol use disorders (AUD) can be understood as putative independent risk factors for AUD or as expressions of underlying shared psychopathology vulnerabilities. However, it remains unclear whether: 1) specific latent internalizing psychopathology risk-profiles predict AUD-incidence and 2) specific latent internalizing comorbidity-profiles in AUD predict AUD-stability. To investigate baseline latent internalizing psychopathology risk profiles as predictors of subsequent AUD-incidence and -stability in adolescents and young adults. Data from the prospective-longitudinal EDSP study (baseline age 14-24 years) were used. The study-design included up to three follow-up assessments in up to ten years. DSM-IV mental disorders were assessed with the DIA-X/M-CIDI. To investigate risk-profiles and their associations with AUD-outcomes, latent class analysis with auxiliary outcome variables was applied. AUD-incidence: a 4-class model (N=1683) was identified (classes: normative-male [45.9%], normative-female [44.2%], internalizing [5.3%], nicotine dependence [4.5%]). Compared to the normative-female class, all other classes were associated with a higher risk of subsequent incident alcohol dependence (p<0.05). AUD-stability: a 3-class model (N=1940) was identified with only one class (11.6%) with high probabilities for baseline AUD. This class was further characterized by elevated substance use disorder (SUD) probabilities and predicted any subsequent AUD (OR 8.5, 95% CI 5.4-13.3). An internalizing vulnerability may constitute a pathway to AUD incidence in adolescence and young adulthood. In contrast, no indication for a role of internalizing comorbidity profiles in AUD-stability was found, which may indicate a limited importance of such profiles - in contrast to SUD-related profiles - in AUD stability. Copyright © 2017 Elsevier B.V. All rights reserved.

Exercise therapy improves aerobic capacity of inpatients with major depressive disorder.

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Kerling, Arno; von Bohlen, Anne; Kück, Momme; Tegtbur, Uwe; Grams, Lena; Haufe, Sven; Gützlaff, Elke; Kahl, Kai G

2016-06-01

Unipolar depression is one of the most common diseases worldwide and is associated with a higher cardiovascular risk partly due to reduced aerobic capacity. Therefore, the aim of our study was to examine whether a structured aerobic training program can improve aerobic capacity in inpatients with MDD (major depressive disorder). Overall, 25 patients (13 women, 12 men) diagnosed with MDD were included in the study. Parameters of aerobic capacity, such as maximum performance, maximum oxygen consumption, and VAT (ventilatory anaerobic threshold), were assessed on a bicycle ergometer before and 6 weeks after a training period (three times per week for 45 min on two endurance machines). In addition, a constant load test was carried out at 50% of the maximum performance prior to and after the training period. The performance data were compared with 25 healthy controls matched for sex, age, and body mass index before and after the training period. Compared to controls, patients with MDD had significantly lower aerobic capacity. After training, there was a significant improvement in their performance data. A significant difference remained only for VAT between patients with MDD and healthy controls. With regard to the coincidence of MDD with cardiovascular and cardiometabolic disorders, a structured supervised exercise program carried out during hospitalization is a useful supplement for patients with MDD.

Ketogenic diets improve behaviors associated with autism spectrum disorder in a sex-specific manner in the EL mouse.

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Ruskin, David N; Fortin, Jessica A; Bisnauth, Subrina N; Masino, Susan A

2017-01-01

The core symptoms of autism spectrum disorder are poorly treated with current medications. Symptoms of autism spectrum disorder are frequently comorbid with a diagnosis of epilepsy and vice versa. Medically-supervised ketogenic diets are remarkably effective nonpharmacological treatments for epilepsy, even in drug-refractory cases. There is accumulating evidence that supports the efficacy of ketogenic diets in treating the core symptoms of autism spectrum disorders in animal models as well as limited reports of benefits in patients. This study tests the behavioral effects of ketogenic diet feeding in the EL mouse, a model with behavioral characteristics of autism spectrum disorder and comorbid epilepsy. Male and female EL mice were fed control diet or one of two ketogenic diet formulas ad libitum starting at 5weeks of age. Beginning at 8weeks of age, diet protocols continued and performance of each group on tests of sociability and repetitive behavior was assessed. A ketogenic diet improved behavioral characteristics of autism spectrum disorder in a sex- and test-specific manner; ketogenic diet never worsened relevant behaviors. Ketogenic diet feeding improved multiple measures of sociability and reduced repetitive behavior in female mice, with limited effects in males. Additional experiments in female mice showed that a less strict, more clinically-relevant diet formula was equally effective in improving sociability and reducing repetitive behavior. Taken together these results add to the growing number of studies suggesting that ketogenic and related diets may provide significant relief from the core symptoms of autism spectrum disorder, and suggest that in some cases there may be increased efficacy in females. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

International Nuclear Information System (INIS)

Xu, Bo; Dan, Han-Cheng; Li, Liang

2017-01-01

Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

Skin picking disorder with co-occurring body dysmorphic disorder

DEFF Research Database (Denmark)

Grant, Jon E; Redden, Sarah A; Leppink, Eric W

2015-01-01

There is clinical overlap between skin picking disorder (SPD) and body dysmorphic disorder (BDD), but little research has examined clinical and cognitive correlates of the two disorders when they co-occur. Of 55 participants with SPD recruited for a neurocognitive study and two pharmacological st...... unique clinical and cognitive aspects of SPD may be more pronounced. Future work should explore possible subgroups in SPD and whether these predict different treatment outcomes....

Cloud prediction of protein structure and function with PredictProtein for Debian.

Science.gov (United States)

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

2013-01-01

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

Improved Trust Prediction in Business Environments by Adaptive Neuro Fuzzy Inference Systems

Directory of Open Access Journals (Sweden)

Ali Azadeh

2015-06-01

Full Text Available Trust prediction turns out to be an important challenge when cooperation among intelligent agents with an impression of trust in their mind, is investigated. In other words, predicting trust values for future time slots help partners to identify the probability of continuing a relationship. Another important case to be considered is the context of trust, i.e. the services and business commitments for which a relationship is defined. Hence, intelligent agents should focus on improving trust to provide a stable and confident context. Modelling of trust between collaborating parties seems to be an important component of the business intelligence strategy. In this regard, a set of metrics have been considered by which the value of confidence level for predicted trust values has been estimated. These metrics are maturity, distance and density (MD2. Prediction of trust for future mutual relationships among agents is a problem that is addressed in this study. We introduce a simulation-based model which utilizes linguistic variables to create various scenarios. Then, future trust values among agents are predicted by the concept of adaptive neuro-fuzzy inference system (ANFIS. Mean absolute percentage errors (MAPEs resulted from ANFIS are compared with confidence levels which are determined by applying MD2. Results determine the efficiency of MD2 for forecasting trust values. This is the first study that utilizes the concept of MD2 for improvement of business trust prediction.

Language profiles in young children with autism spectrum disorder: A community sample using multiple assessment instruments.

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Nevill, Rose; Hedley, Darren; Uljarević, Mirko; Sahin, Ensu; Zadek, Johanna; Butter, Eric; Mulick, James A

2017-11-01

This study investigated language profiles in a community-based sample of 104 children aged 1-3 years who had been diagnosed with autism spectrum disorder using Diagnostic and Statistical Manual of Mental Disorders (5th ed.) diagnostic criteria. Language was assessed with the Mullen scales, Preschool Language Scale, fifth edition, and Vineland-II parent-report. The study aimed to determine whether the receptive-to-expressive language profile is independent from the assessment instrument used, and whether nonverbal cognition, early communicative behaviors, and autism spectrum disorder symptoms predict language scores. Receptive-to-expressive language profiles differed between assessment instruments and reporters, and Preschool Language Scale, fifth edition profiles were also dependent on developmental level. Nonverbal cognition and joint attention significantly predicted receptive language scores, and nonverbal cognition and frequency of vocalizations predicted expressive language scores. These findings support the administration of multiple direct assessment and parent-report instruments when evaluating language in young children with autism spectrum disorder, for both research and in clinical settings. Results also support that joint attention is a useful intervention target for improving receptive language skills in young children with autism spectrum disorder. Future research comparing language profiles of young children with autism spectrum disorder to children with non-autism spectrum disorder developmental delays and typical development will add to our knowledge of early language development in children with autism spectrum disorder.

Screen for Disordered Eating: Improving the accuracy of eating disorder screening in primary care.

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Maguen, Shira; Hebenstreit, Claire; Li, Yongmei; Dinh, Julie V; Donalson, Rosemary; Dalton, Sarah; Rubin, Emma; Masheb, Robin

To develop a primary care eating disorder screen with greater accuracy and greater potential for generalizability, compared to existing screens. Cross-sectional survey to assess discriminative accuracy of a new screen, Screen for Disordered Eating (SDE), compared to Eating Disorders Screen for Primary Care (EDS-PC) and SCOFF screener, using prevalence rates of Binge Eating Disorder (BED), Bulimia Nervosa (BN), Anorexia Nervosa (AN), and Any Eating Disorder (AED), as measured by the Eating Disorder Examination Questionnaire (EDE-Q). The SDE correctly classified 87.2% (CI: 74.3%-95.2%) of BED cases, all cases of BN and AN, and 90.5% (CI: 80.4%-96.4%) of AED cases. Sensitivity estimates were higher than the SCOFF, which correctly identified 69.6% (CI: 54.2%-82.3%) of BED, 77.8% (CI: 40.0%-97.2%) of BN, 37.5% (CI: 8.52%-75.5%) of AN, and 66.1% (CI: 53%-77.7%) of AED. While the EDS-PC had slightly higher sensitivity than the SDE, the SDE had better specificity. The SDE outperformed the SCOFF in classifying true cases, the EDS-PC in classifying true non-cases, and the EDS-PC in distinguishing cases from non-cases. The SDE is the first screen, inclusive of BED, valid for detecting eating disorders in primary care. Findings have broad implications to address eating disorder screening in primary care settings. Published by Elsevier Inc.

Preschool Speech Error Patterns Predict Articulation and Phonological Awareness Outcomes in Children with Histories of Speech Sound Disorders

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Preston, Jonathan L.; Hull, Margaret; Edwards, Mary Louise

2013-01-01

Purpose: To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost 4 years later. Method: Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 (years;months) and were followed up…

Did the DSM-5 Improve the Traumatic Stressor Criterion?: Association of DSM-IV and DSM-5 Criterion A with Posttraumatic Stress Disorder Symptoms.

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Larsen, Sadie E; Berenbaum, Howard

2017-01-01

A recent meta-analysis found that DSM-III- and DSM-IV-defined traumas were associated with only slightly higher posttraumatic stress disorder (PTSD) symptoms than nontraumatic stressors. The current study is the first to examine whether DSM-5-defined traumas were associated with higher levels of PTSD than DSM-IV-defined traumas. Further, we examined theoretically relevant event characteristics to determine whether characteristics other than those outlined in the DSM could predict PTSD symptoms. One hundred six women who had experienced a trauma or significant stressor completed questionnaires assessing PTSD, depression, impairment, and event characteristics. Events were rated for whether they qualified as DSM-IV and DSM-5 trauma. There were no significant differences between DSM-IV-defined traumas and stressors. For DSM-5, effect sizes were slightly larger but still nonsignificant (except for significantly higher hyperarousal following traumas vs. stressors). Self-reported fear for one's life significantly predicted PTSD symptoms. Our results indicate that the current DSM-5 definition of trauma, although a slight improvement from DSM-IV, is not highly predictive of who develops PTSD symptoms. Our study also indicates the importance of individual perception of life threat in the prediction of PTSD. © 2017 S. Karger AG, Basel.

Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate

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Andreassen, Ole A; Thompson, Wesley K; Schork, Andrew J

2013-01-01

are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability...... associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders....

Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

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Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Attention-deficit/hyperactivity disorder in adolescence predicts onset of major depressive disorder through early adulthood.

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Meinzer, Michael C; Lewinsohn, Peter M; Pettit, Jeremy W; Seeley, John R; Gau, Jeff M; Chronis-Tuscano, Andrea; Waxmonsky, James G

2013-06-01

The aim of this study was to examine the prospective relationship between a history of attention-deficit/hyperactivity disorder (ADHD) assessed in mid-adolescence and the onset of major depressive disorder (MDD) through early adulthood in a large school-based sample. A secondary aim was to examine whether this relationship was robust after accounting for comorbid psychopathology and psychosocial impairment. One thousand five hundred seven participants from the Oregon Adolescent Depression Project completed rating scales in adolescence and structured diagnostic interviews up to four times from adolescence to age 30. Adolescents with a lifetime history of ADHD were at significantly higher risk of MDD through early adulthood relative to those with no history of ADHD. ADHD remained a significant predictor of MDD after controlling for gender, lifetime history of other psychiatric disorders in adolescence, social and academic impairment in adolescence, stress and coping in adolescence, and new onset of other psychiatric disorders through early adulthood (hazard ratio, 1.81; 95% confidence interval, 1.04, 3.06). Additional significant, robust predictors of MDD included female gender, a lifetime history of an anxiety disorder, and poor coping skills in mid-adolescence, as well as the onset of anxiety, oppositional defiant disorder, and substance-use disorder after mid-adolescence. A history of ADHD in adolescence was associated with elevated risk of MDD through early adulthood and this relationship remained significant after controlling for psychosocial impairment in adolescence and co-occurring psychiatric disorders. Additional work is needed to identify the mechanisms of risk and to inform depression prevention programs for adolescents with ADHD. © 2013 Wiley Periodicals, Inc.

Biomarkers improve mortality prediction by prognostic scales in community-acquired pneumonia.

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Menéndez, R; Martínez, R; Reyes, S; Mensa, J; Filella, X; Marcos, M A; Martínez, A; Esquinas, C; Ramirez, P; Torres, A

2009-07-01

Prognostic scales provide a useful tool to predict mortality in community-acquired pneumonia (CAP). However, the inflammatory response of the host, crucial in resolution and outcome, is not included in the prognostic scales. The aim of this study was to investigate whether information about the initial inflammatory cytokine profile and markers increases the accuracy of prognostic scales to predict 30-day mortality. To this aim, a prospective cohort study in two tertiary care hospitals was designed. Procalcitonin (PCT), C-reactive protein (CRP) and the systemic cytokines tumour necrosis factor alpha (TNFalpha) and interleukins IL6, IL8 and IL10 were measured at admission. Initial severity was assessed by PSI (Pneumonia Severity Index), CURB65 (Confusion, Urea nitrogen, Respiratory rate, Blood pressure, > or = 65 years of age) and CRB65 (Confusion, Respiratory rate, Blood pressure, > or = 65 years of age) scales. A total of 453 hospitalised CAP patients were included. The 36 patients who died (7.8%) had significantly increased levels of IL6, IL8, PCT and CRP. In regression logistic analyses, high levels of CRP and IL6 showed an independent predictive value for predicting 30-day mortality, after adjustment for prognostic scales. Adding CRP to PSI significantly increased the area under the receiver operating characteristic curve (AUC) from 0.80 to 0.85, that of CURB65 from 0.82 to 0.85 and that of CRB65 from 0.79 to 0.85. Adding IL6 or PCT values to CRP did not significantly increase the AUC of any scale. When using two scales (PSI and CURB65/CRB65) and CRP simultaneously the AUC was 0.88. Adding CRP levels to PSI, CURB65 and CRB65 scales improves the 30-day mortality prediction. The highest predictive value is reached with a combination of two scales and CRP. Further validation of that improvement is needed.

Improving consensus contact prediction via server correlation reduction.

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Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

2009-05-06

Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Improving consensus contact prediction via server correlation reduction

Directory of Open Access Journals (Sweden)

Xu Jinbo

2009-05-01

Full Text Available Abstract Background Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. Results In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Conclusion Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Poor fine-motor and visuospatial skills predict persistence of pediatric-onset obsessive-compulsive disorder into adulthood.

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Bloch, Michael H; Sukhodolsky, Denis G; Dombrowski, Philip A; Panza, Kaitlyn E; Craiglow, Brittany G; Landeros-Weisenberger, Angeli; Leckman, James F; Peterson, Bradley S; Schultz, Robert T

2011-09-01

Half of pediatric-onset OCD cases remit by adulthood. Studies have demonstrated that initial response to pharmacotherapy, age of onset, prominent hoarding symptoms, and the presence of comorbid tic disorders are associated with long-term outcome. Our goal was to examine the association between childhood performance on neuropsychological testing and persistence of OCD into adulthood. Twenty-four children with OCD were followed for an average of 7.5 years into early adulthood. Neuropsychological performance in childhood (childhood performance on the Purdue pegboard task and the block design subscale of WISC-III was associated with persistence of OCD symptoms into adulthood. IQ, VMI, and nonverbal memory performance did not predict significantly the persistence of OCD. These results suggest that visuospatial and fine-motor skill deficits are predictive of poor long-term outcome in pediatric-onset OCD. Future longitudinal studies are needed to chart the course of these deficits relative to the course of symptoms in OCD and to determine whether the association of these neuropsychiatric deficits with long-term outcome is specific to pediatric-onset OCD or generalizes to other psychiatric disorders. © 2011 The Authors. Journal of Child Psychology and Psychiatry © 2011 Association for Child and Adolescent Mental Health.

Dissecting the Yale-Brown Obsessive-Compulsive Scale severity scale to understand the routes for symptomatic improvement in obsessive-compulsive disorder.

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Costa, Daniel L da Conceição; Barbosa, Veronica S; Requena, Guaraci; Shavitt, Roseli G; Pereira, Carlos A de Bragança; Diniz, Juliana B

2017-10-01

We aimed to investigate which items of the Yale-Brown Obsessive-Compulsive Severity Scale best discriminate the reduction in total scores in obsessive-compulsive disorder patients after 4 and 12 weeks of pharmacological treatment. Data from 112 obsessive-compulsive disorder patients who received fluoxetine (⩽80 mg/day) for 12 weeks were included. Improvement indices were built for each Yale-Brown Obsessive-Compulsive Severity Scale item at two timeframes: from baseline to week 4 and from baseline to week 12. Indices for each item were correlated with the total scores for obsessions and compulsions and then ranked by correlation coefficient. A correlation coefficient ⩾0.7 was used to identify items that contributed significantly to reducing obsessive-compulsive disorder severity. At week 4, the distress items reached the threshold of 0.7 for improvement on the obsession and compulsion subscales although, contrary to our expectations, there was greater improvement in the control items than in the distress items. At week 12, there was greater improvement in the time, interference, and control items than in the distress items. The use of fluoxetine led first to reductions in distress and increases in control over symptoms before affecting the time spent on, and interference from, obsessions and compulsions. Resistance did not correlate with overall improvement. Understanding the pathway of improvement with pharmacological treatment in obsessive-compulsive disorder may provide clues about how to optimize the effects of medication.

Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information

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Aghakouchak, Amir; Tourian, Mohammad J.

2015-04-01

Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014

Duloxetine in the treatment of generalized anxiety disorder

Directory of Open Access Journals (Sweden)

Alan Wright

2009-08-01

Full Text Available Alan Wright, Chad VanDenBergCenter for Clinical Research, Mercer University, Atlanta, GA, USAAbstract: Duloxetine is a serotonin-norepinephrine reuptake inhibitor (SNRI which is FDA approved for the treatment of generalized anxiety disorder (GAD in doses of 30 mg to 120 mg daily. Duloxetine has been shown to significantly improve symptoms of GAD as measured through the Hamilton Anxiety Rating Scale (HAMA, the Clinical Global Impressions Scale (CGI-I, and other various outcome measures in several placebo-controlled, randomized, double blind, multi-center studies. Symptom improvement began within the first few weeks, and continued for the duration of the studies. In addition, duloxetine has also been shown to improve outcomes in elderly patients with GAD, and in GAD patients with clinically significant pain symptoms. Duloxetine was noninferior compared with venlafaxine XR. Duloxetine was found to have a good tolerability profile which was predictable and similar to another SNRI, venlafaxine. Adverse events (AEs such as nausea, constipation, dry mouth, and insomnia were mild and transient, and occurred at relatively low rates. It was found to have a low frequency of drug interactions. In conclusion, duloxetine, a selective inhibitor for the serotonin and norepinephrine transporters, is efficacious in the treatment of GAD, and has a predictable tolerability profile, with AEs generally being mild to moderate.Keywords: duloxetine, generalized anxiety disorder, anxiety, GAD

EEG does not predict response to valproate treatment of aggression in patients with borderline and antisocial personality disorders.

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Reeves, Roy R; Struve, Frederick A; Patrick, Gloria

2003-04-01

Previous investigations of the role of EEG in predicting response of aggressive patients to valproate therapy have yielded mixed results. In this study of borderline and antisocial personality disorder patients hospitalized with aggressive behavior, EEGs were obtained prior to treatment with valproate. Eight of 22 (36.4%) patients subsequently responsive to valproate had nonepileptiform EEG abnormalities, while 5 of 20 (25%) patients not responsive to valproate had nonepileptiform EEG abnormalities. Although more of the valproate responders than nonresponders had EEG abnormalities, the presence of nonepileptiform EEG abnormalities was not a statistically significant (X2 = 0.213, df = 1, p = 0.64) predictor of valproate response in personality disorder patients with aggression.

What should be done with antisocial personality disorder in the new edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V)?

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