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The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.

Original publication

DOI

10.1016/j.nicl.2015.09.015

Type

Journal article

Journal

Neuroimage Clin

Publication Date

2015

Volume

9

Pages

555 - 563

Keywords

Classification, Machine learning, Magnetic resonance imaging, Risk factors, Schizophrenia, Working memory, Adolescent, Analysis of Variance, Brain, Brain Mapping, Child, Female, Follow-Up Studies, Humans, Machine Learning, Magnetic Resonance Imaging, Male, Memory Disorders, Memory, Short-Term, Neuropsychological Tests, Prodromal Symptoms, Psychiatric Status Rating Scales, Psychotic Disorders, Risk Factors