B C Castelluccio1, J G Kenney2,3, J K Johannesen2,3. 1. Department of Psychiatry and Human Behavior, Brown University Alpert Medical School, Providence, RI 02903, USA. 2. Psychology Service, VA Connecticut Healthcare System, West Haven, CT 06516, USA. 3. Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA.
Abstract
OBJECTIVE: Meta-analyses report moderate effects across cognitive remediation (CR) trials in schizophrenia. However, individual responses are variable, with some participants showing no appreciable gain in cognitive performance. Furthermore, reasons for heterogeneous outcome are undetermined. We examine the extent to which CR outcome is attributable to near learning-direct gains in trained cognitive tasks-while also exploring factors influencing far transfer of gains during training to external cognitive measures. METHOD: Thirty-seven schizophrenia outpatients were classified as CR responders and non-responders according to change in MATRICS Consensus Cognitive Battery composite score following 20 sessions of computer-based training. Metrics of near learning during training, as well as baseline demographic, clinical, cognitive, and electroencephalographic (EEG) measures, were examined as predictors of responder status. RESULTS: Significant post-training improvement in cognitive composite score (Cohen's d = .41) was observed across the sample, with n = 21 and n = 16 classified as responders and non-responders, respectively. Near learning was evidenced by significant improvement on each training exercise with practice; however, learning did not directly predict responder status. Group-wise comparison of responders and non-responders identified two factors favoring responders: higher EEG individual alpha frequency (IAF) and lower antipsychotic dosing. Tested in moderation analyses, IAF interacted with learning to predict improvement in cognitive outcome. CONCLUSION: CR outcome in schizophrenia is not directly explained by learning during training and appears to depend on latent factors influencing far transfer of trained abilities. Further understanding of factors influencing transfer of learning is needed to optimize CR efficacy.
OBJECTIVE: Meta-analyses report moderate effects across cognitive remediation (CR) trials in schizophrenia. However, individual responses are variable, with some participants showing no appreciable gain in cognitive performance. Furthermore, reasons for heterogeneous outcome are undetermined. We examine the extent to which CR outcome is attributable to near learning-direct gains in trained cognitive tasks-while also exploring factors influencing far transfer of gains during training to external cognitive measures. METHOD: Thirty-seven schizophrenia outpatients were classified as CR responders and non-responders according to change in MATRICS Consensus Cognitive Battery composite score following 20 sessions of computer-based training. Metrics of near learning during training, as well as baseline demographic, clinical, cognitive, and electroencephalographic (EEG) measures, were examined as predictors of responder status. RESULTS: Significant post-training improvement in cognitive composite score (Cohen's d = .41) was observed across the sample, with n = 21 and n = 16 classified as responders and non-responders, respectively. Near learning was evidenced by significant improvement on each training exercise with practice; however, learning did not directly predict responder status. Group-wise comparison of responders and non-responders identified two factors favoring responders: higher EEG individual alpha frequency (IAF) and lower antipsychotic dosing. Tested in moderation analyses, IAF interacted with learning to predict improvement in cognitive outcome. CONCLUSION:CR outcome in schizophrenia is not directly explained by learning during training and appears to depend on latent factors influencing far transfer of trained abilities. Further understanding of factors influencing transfer of learning is needed to optimize CR efficacy.
Authors: Thomas H Grandy; Markus Werkle-Bergner; Christian Chicherio; Martin Lövdén; Florian Schmiedek; Ulman Lindenberger Journal: Neuroimage Date: 2013-04-25 Impact factor: 6.556
Authors: Veronica B Perez; Melissa Tarasenko; Makoto Miyakoshi; Sean T Pianka; Scott D Makeig; David L Braff; Neal R Swerdlow; Gregory A Light Journal: Neuropsychopharmacology Date: 2017-01-31 Impact factor: 7.853