Background: Virtual reality-based cognitive remediation therapy (VR-CRT) offers an ecologically valid approach to enhance real-world cognitive functioning in mood disorders (MD) or schizophrenia spectrum disorders (SSD). This study investigated baseline cognitive, clinical, and neural predictors of VR-CRT response in MD and SSD.
Methods: Sixty-two MD and SSD participants were randomized to receive four-week VR-CRT or control with assessments at baseline, treatment completion (week 5), and follow-up (week 17). Univariate general linear models examined predictors of VR-CRT improvement on daily-life cognitive skills, assessed using the Cognition Assessment in Virtual Reality (CAVIR). Predictors included age, diagnosis, baseline cognition, IQ-cognition discrepancy, dorsal prefrontal cortex (dPFC) activation during a working memory task, functional connectivity within the dorsal attention (DAN) and salience (SAL) networks, subjective cognition, and technological acceptance.
Results: Higher IQ-cognition discrepancy at baseline (i.e., better cognitive performance than expected from premorbid IQ) predicted greater treatment efficacy at treatment completion (β = 0.17, p = 0.045) and follow-up (β = 0.21, p = 0.008), while baseline cognition was not associated with treatment response (ps ≥ 0.15). Higher baseline dPFC activity predicted more improvements at both times (β = 2.27 p = 0.03; β = 1.82; p = 0.048, respectively). Higher DAN-SAL connectivity predicted improvements at treatment completion (β = 2.81 p = 0.047), but not at follow-up (p = 0.38). Age, sex, diagnosis, subjective cognition, and technological acceptance were not associated with cognitive change.
Conclusions: Better cognitive performance than expected based on IQ, possibly reflecting higher cognitive fitness, and greater task-related engagement of dPFC may enhance VR-CRT responsiveness. This profile may indicate greater readiness for change and propensity to translate cognitive strategies into daily life.
Keywords: Bipolar disorder; Cognitive remediation; Depression; Mood disorders; Schizophrenia; Treatment predictors; Virtual reality.
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