The @entity716 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 @entity1032 ( @entity1032 ), first @entity716 (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 @entity1032 , FEP and HC (19 subjects/group). The HC and @entity1032 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 @entity1032 vs. FEP (p  =  0.62), respectively. Without mask, accuracy was 65.8% for HC vs. @entity1032 (p  =  0.03) and 65.8% for HC vs. FEP (p  =  0.0047), and 57.9% for @entity1032 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 @entity1032 cannot be accurately separated in small samples under these conditions. However, @entity1032 can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the @entity1032 .

Classifying individuals at high-risk for XXXX based on functional brain activity during working memory processing.

@entity1032 :: ('MESH:D008607', 'Disease') :: ['at-risk mental state', 'ARMS'] @entity716 :: ('MESH:D011605', 'Disease') :: ['psychosis', 'episode psychosis']
@entity716 :: (MESH:D011605,Disease) :: ['psychosis']
