BACKGROUND: Individuals at clinical high risk (CHR) for psychosis have become a major focus for research designed to explore early predictors of transition to full psychosis. Characterizing differences in neurocognitive (NC) functioning between psychosis converters (CHR-C) and non-converters (CHR-NC) might contribute to the identification of specific NC predictors of psychosis onset. Therefore, the aim of the present meta-analysis was to compare the baseline NC performance between CHR-C and CHR-NC. METHOD: PubMed (MEDLINE), Web of Science, Embase and reference lists were searched for studies reporting baseline cognitive data of CHR-C and CHR-NC. Included NC tests were classified within the MATRICS - Measurement and Treatment Research to Improve Cognition in Schizophrenia - cognitive domains. RESULTS: Of 95 studies assessed for eligibility, 9 studies comprising 583 CHR subjects (N CHR-C=195, N CHR-NC=388) met all the inclusion criteria. CHR-C performed significantly worse compared to CHR-NC on 2 MATRICS domains namely working memory (ES=-0.29, 95% CI=-0.53 to -0.05) and visual learning (ES=-0.40, 95% CI=-0.68 to -0.13). For the remaining 4 domains (processing speed, attention/vigilance, verbal learning, reasoning/problem solving) no significant differences between CHR-C and CHR-NC were observed. CONCLUSION: Based on the current meta-analytic data we might conclude that it is possible to differentiate between CHR-C and CHR-NC with respect to working memory and visual learning. The addition of visual learning and working memory tasks to psychosis regression models might contribute to the predictive power of these models.
BACKGROUND: Individuals at clinical high risk (CHR) for psychosis have become a major focus for research designed to explore early predictors of transition to full psychosis. Characterizing differences in neurocognitive (NC) functioning between psychosis converters (CHR-C) and non-converters (CHR-NC) might contribute to the identification of specific NC predictors of psychosis onset. Therefore, the aim of the present meta-analysis was to compare the baseline NC performance between CHR-C and CHR-NC. METHOD: PubMed (MEDLINE), Web of Science, Embase and reference lists were searched for studies reporting baseline cognitive data of CHR-C and CHR-NC. Included NC tests were classified within the MATRICS - Measurement and Treatment Research to Improve Cognition in Schizophrenia - cognitive domains. RESULTS: Of 95 studies assessed for eligibility, 9 studies comprising 583 CHR subjects (N CHR-C=195, N CHR-NC=388) met all the inclusion criteria. CHR-C performed significantly worse compared to CHR-NC on 2 MATRICS domains namely working memory (ES=-0.29, 95% CI=-0.53 to -0.05) and visual learning (ES=-0.40, 95% CI=-0.68 to -0.13). For the remaining 4 domains (processing speed, attention/vigilance, verbal learning, reasoning/problem solving) no significant differences between CHR-C and CHR-NC were observed. CONCLUSION: Based on the current meta-analytic data we might conclude that it is possible to differentiate between CHR-C and CHR-NC with respect to working memory and visual learning. The addition of visual learning and working memory tasks to psychosis regression models might contribute to the predictive power of these models.
Authors: S Kristian Hill; Alison Buchholz; Hayley Amsbaugh; James L Reilly; Leah H Rubin; James M Gold; Richard S E Keefe; Godfrey D Pearlson; Matcheri S Keshavan; Carol A Tamminga; John A Sweeney Journal: Schizophr Res Date: 2015-05-23 Impact factor: 4.939
Authors: André Schmidt; Renata Smieskova; Andor Simon; Paul Allen; Paolo Fusar-Poli; Philip K McGuire; Kerstin Bendfeldt; Jacqueline Aston; Undine E Lang; Marc Walter; Ernst-Wilhelm Radue; Anita Riecher-Rössler; Stefan J Borgwardt Journal: J Psychiatry Neurosci Date: 2014-07 Impact factor: 6.186
Authors: Jimmy Choi; Cheryl M Corcoran; Joanna M Fiszdon; Michael Stevens; Daniel C Javitt; Melissa Deasy; Lawrence C Haber; Michael J Dewberry; Godfrey D Pearlson Journal: Psychiatr Rehabil J Date: 2016-08-25