scholarly journals Pupillometer-based neurofeedback cognitive training to improve processing speed and social functioning in individuals at clinical high risk for psychosis.

2017 ◽  
Vol 40 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Jimmy Choi ◽  
Cheryl M. Corcoran ◽  
Joanna M. Fiszdon ◽  
Michael Stevens ◽  
Daniel C. Javitt ◽  
...  
Keyword(s):  
High Risk ◽  
Social Functioning ◽  
Cognitive Training ◽  
Processing Speed ◽  
Clinical High Risk

Bullying and social functioning, schemas, and beliefs among youth at clinical high risk for psychosis

2021 ◽  
Author(s):  
Amy Braun ◽  
Olga Santesteban‐Echarri ◽  
Kristin S. Cadenhead ◽  
Barbara A. Cornblatt ◽  
Eric Granholm ◽  
...  
Keyword(s):  
High Risk ◽  
Social Functioning ◽  
Clinical High Risk

Clinical high risk for psychosis: gender differences in symptoms and social functioning

2015 ◽  
Vol 11 (4) ◽  
pp. 306-313 ◽  
Author(s):  
Liz Rietschel ◽  
Martin Lambert ◽  
Anne Karow ◽  
Mathias Zink ◽  
Hendrik Müller ◽  
...  
Keyword(s):  
Gender Differences ◽  
High Risk ◽  
Social Functioning ◽  
Clinical High Risk

scholarly journals Modeling the role of negative symptoms in determining social functioning in individuals at clinical high risk of psychosis

2015 ◽  
Vol 169 (1-3) ◽  
pp. 204-208 ◽  
Author(s):  
Danielle A. Schlosser ◽  
Timothy R. Campellone ◽  
Bruno Biagianti ◽  
Kevin L. Delucchi ◽  
David E. Gard ◽  
...  
Keyword(s):  
High Risk ◽  
Social Functioning ◽  
Negative Symptoms ◽  
Clinical High Risk

scholarly journals A pilot study of cognitive training in clinical high risk for psychosis: Initial evidence of cognitive benefit

2014 ◽  
Vol 157 (1-3) ◽  
pp. 314-316 ◽  
Author(s):  
Christine I. Hooker ◽  
Emily E. Carol ◽  
T.J. Eisenstein ◽  
Hong Yin ◽  
Sarah Hope Lincoln ◽  
...  
Keyword(s):  
Pilot Study ◽  
High Risk ◽  
Cognitive Training ◽  
Clinical High Risk ◽  
Cognitive Benefit

The impact of emotion awareness and regulation on social functioning in individuals at clinical high risk for psychosis

2016 ◽  
Vol 46 (14) ◽  
pp. 2907-2918 ◽  
Author(s):  
D. Kimhy ◽  
K. E. Gill ◽  
G. Brucato ◽  
J. Vakhrusheva ◽  
L. Arndt ◽  
...  
Keyword(s):  
Emotion Regulation ◽  
High Risk ◽  
Social Functioning ◽  
Clinical High Risk ◽  
Emotion Regulation Strategies ◽  
Global Functioning ◽  
Cross Sectional ◽  
Hierarchical Multiple Regression ◽  
Regulation Strategies ◽  
The Impact

BackgroundSocial functioning (SF) difficulties are ubiquitous among individuals at clinical high risk for psychosis (CHR), but it is not yet clear why. One possibility is suggested by the observation that effective SF requires adaptive emotion awareness and regulation. Previous reports have documented deficits in emotion awareness and regulation in individuals with schizophrenia, and have shown that such deficits predicted SF. However, it is unknown whether these deficits are present prior to the onset of psychosis or whether they are linked to SF in CHR individuals.MethodWe conducted a cross-sectional comparison of emotion awareness and regulation in 54 individuals at CHR, 87 with schizophrenia and 50 healthy controls (HC). Then, within the CHR group, we examined links between emotion awareness, emotion regulation and SF as indexed by the Global Functioning Scale: Social (Cornblatt et al. 2007).ResultsGroup comparisons indicated significant differences between HC and the two clinical groups in their ability to identify and describe feelings, as well as the use of suppression and reappraisal emotion-regulation strategies. Specifically, the CHR and schizophrenia groups displayed comparable deficits in all domains of emotion awareness and emotion regulation. A hierarchical multiple regression analysis indicated that difficulties describing feelings accounted for 23.2% of the SF variance.ConclusionsThe results indicate that CHR individuals display substantial emotion awareness and emotion-regulation deficits, at severity comparable with those observed in individuals with schizophrenia. Such deficits, in particular difficulties describing feelings, predate the onset of psychosis and contribute significantly to poor SF in this population.

scholarly journals S64. COGNITIVE IMPAIRMENTS AND PREDICTION OF FUNCTIONAL OUTCOME IN INDIVIDUALS AT CLINICAL HIGH-RISK FOR PSYCHOSIS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S57-S58
Author(s):  
Kate Haining ◽  
Gina Brunner ◽  
Ruchika Gajwani ◽  
Joachim Gross ◽  
Andrew Gumley ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Functional Outcome ◽  
Processing Speed ◽  
Cross Validation ◽  
Clinical High Risk ◽  
Role Functioning ◽  
Gaf Scores ◽  
Fold Cross Validation

Abstract Background Research in individuals at clinical-high risk for psychosis (CHR-P) has focused on developing algorithms to predict transition to psychosis. However, it is becoming increasingly important to address other outcomes, such as the level of functioning of CHR-P participants. To address this important question, this study investigated the relationship between baseline cognitive performance and functional outcome between 6–12 months in a sample of CHR-P individuals using a machine-learning approach to identify features that are predictive of long-term functional impairments. Methods Data was available for 111 CHR-P individuals at 6–12 months follow-up. In addition, 47 CHR-negative (CHR-N) participants who did not meet CHR criteria and 55 healthy controls (HCs) were recruited. CHR-P status was assessed using the Comprehensive Assessment of At-Risk Mental States (CAARMS) and the Schizophrenia Proneness Instrument, Adult version (SPI-A). Cognitive assessments included the Brief Assessment of Cognition in Schizophrenia (BACS) and the Penn Computerized Neurocognitive Battery (CNB). Global, social and role functioning scales were used to measure functional status. CHR-P individuals were divided into good functional outcome (GFO, GAF ≥ 65) and poor functional outcome groups (PFO, GAF < 65). Feature selection was performed using LASSO regression with the LARS algorithm and 10-fold cross validation with GAF scores at baseline as the outcome variable. The following features were identified as predictors of GAF scores at baseline: verbal memory, verbal fluency, attention, emotion recognition, social and role functioning and SPI-A distress. This model explained 47% of the variance in baseline GAF scores. In the next step, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF) classifiers with 10-fold cross validation were then trained on those features with GAF category at follow-up used as the binary label column. Models were compared using a calculated score incorporating area under the curve (AUC), accuracy, and AUC consistency across runs, whereby AUC was given a higher weighting than accuracy due to class imbalance. Results CHR-P individuals had slower motor speed, reduced attention and processing speed and increased emotion recognition reaction times (RTs) compared to HCs and reduced attention and processing speed compared to CHR-Ns. At follow-up, 66% of CHR-P individuals had PFO. LDA emerged as the strongest classifier, showing a mean AUC of 0.75 (SD = 0.15), indicating acceptable classification performance for GAF category at follow-up. PFO was detected with a sensitivity of 75% and specificity of 58%, with a total mean weighted accuracy of 68%. Discussion The CHR-P state was associated with significant impairments in cognition, highlighting the importance of interventions such as cognitive remediation in this population. Our data suggest that the development of features using machine learning approaches is effective in predicting functional outcomes in CHR-P individuals. Greater levels of accuracy, sensitivity and specificity might be achieved by increasing training sets and validating the classifier with external data sets. Indeed, machine learning methods have potential given that trained classifiers can easily be shared online, thus enabling clinical professionals to make individualised predictions.

Processing speed and brain volume in individuals at clinical high-risk for psychosis with comorbid eating disorders: A brief report

2020 ◽  
Author(s):  
Cansu Sarac ◽  
Zarina R. Bilgrami ◽  
Shalaila S. Haas ◽  
Shaynna N. Herrera ◽  
Jonathan J. Myers ◽  
...  
Keyword(s):  
Eating Disorders ◽  
High Risk ◽  
Processing Speed ◽  
Brain Volume ◽  
Clinical High Risk
Sign in / Sign up

Export Citation Format

Share Document