Beta Oscillations as a Mechanistic Target for Predictive Processing Deficits in Psychosis

Background: Predictive processing abnormalities offer a unifying account of perceptual and expressive disturbances in psychosis, yet classical predictive coding frameworks remain difficult to translate due to limited neurophysiological grounding. Emerging evidence positions beta-band oscillations and their transient burst dynamics as a biologically plausible mechanism for implementing top-down predictions that stabilize internal models. Study Design: This narrative review synthesizes evidence from electrophysiology, laminar physiology, computational modelling, language research, and clinical neuroimaging to evaluate beta oscillations as a mechanistic target for predictive processing deficits in psychosis. We integrate data from modified predictive routing frameworks and dendritic computation models to clarify how beta rhythms prepare cortical pathways for predicted inputs. Study Results: Across sensory, motor, cognitive, and language domains, schizophrenia features impaired generation, timing, and contextual deployment of beta activity. These include attenuated post-movement beta rebound, reduced or mistimed beta bursts during working memory and inhibition, abnormal beta-gamma interactions during perception, and weakened beta-mediated contextual guidance during language comprehension. Laminar and computational findings indicate that beta bursts arise from the integration of apical (contextual) and basal (sensory) dendritic inputs in layer 5 pyramidal neurons, providing a mechanistic substrate for top-down predictions. Beta disruptions, therefore, offer a parsimonious account of disorganization, psychomotor slowing, and failures of contextual maintenance. Early neuromodulation, pharmacologic, and neurofeedback studies suggest that beta dynamics are modifiable. Conclusions: Beta oscillations provide a tractable and mechanistically grounded target for predictive processing deficits in psychosis. Standardizing burst metrics and developing individualized, closed-loop approaches will be critical for advancing beta-based interventions.