Deficits in metacognition within the narratives of persons with schizophrenia are linked with symptoms, quality of life, neurocognition and poorer awareness of illness.

Cognitive remediation is now widely recognized as an effective treatment for cognitive deficits in schizophrenia. Its effects are meaningful, durable, and related to improvements in everyday functional outcomes. As with many therapies, the evolution of cognitive remediation has resulted in treatment programs that use a variety of specific techniques, yet share common core principles. This paper is the product of a cognitive remediation expert working group consensus meeting to identify core features of the treatment and produce recommendations for its design, conduct, reporting, and implementation. Four techniques were identified as core features of cognitive remediation: facilitation by a therapist, cognitive exercise, procedures to develop problem-solving strategies, and procedures to facilitate transfer to real world functioning. Treatment techniques within each of these core features are presented to facilitate decisions for clinical trials and implementation in clinical settings.

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BackgroundWith millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.MethodsSchizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses.ResultsSWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function.ConclusionsEEG features derived by SVM are consistent with literature reports of gamma’s role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.