State- and trait-like perceptual predictive learning abnormalities in schizophrenia - the absence of a clear understanding of mechanisms that drive the symptoms of schizophrenia represents a…

The National Institute of Mental Health awarded $191,910 to the University of Manitoba on July 1, 2026, under the Mental Health Research Grants program (CFDA 93.242) to characterize neural abnormalities associated with aberrant predictive learning in schizophrenia and their relationships with specific illness features over time.

The recipient will conduct an electroencephalography (EEG) study of auditory predictive learning using a Bayesian computational framework. The study enrolls 90 adults with schizophrenia (60 of whom experience active auditory hallucinations at baseline) and 30 non-psychiatric comparison participants. Participants with schizophrenia will complete EEG assessment three times over a six-month follow-up period; comparison participants will be assessed at baseline only. The research examines the stability of EEG predictive learning indices and their associations with auditory hallucinations, distinguishing between abnormalities at different levels of the predictive learning hierarchy—low-level sensory perception versus high-level beliefs about the world—to understand their differential relationships to specific symptom features and their stability.

Performance takes place at 2323 Riverside Drive, Ottawa, Ontario, Canada. The period of performance runs from July 1, 2026, through June 30, 2028. The assistance type is a Project Grant.

State- and trait-like perceptual predictive learning abnormalities in schizophrenia - the absence of a clear understanding of mechanisms that drive the symptoms of schizophrenia represents a critical need, hindering our ability to develop effective interventions for this debilitating condition. Bayesian predictive learning offers a formal computational perspective on how the brain processes and integrates incoming sensory information to form higher-level beliefs about the world. Abnormal predictive learning has emerged as a key candidate mechanism to explain core features of schizophrenia, including auditory hallucinations and impaired cognition. Notably, abnormalities occurring at different levels of the predictive learning hierarchy (i.e., low-level sensory perception VS. High-level beliefs about the state of the world) may differ in terms of their stability and their relationships to specific illness features (e.g., auditory hallucinations, impaired cognition). Building upon promising preliminary data and in alignment with the nimh strategic plan, we will use an innovative electroencephalography (EEG) paradigm to characterize specific neural abnormalities associated with aberrant predictive learning in schizophrenia and relationships with specific illness features over time. Ninety adults with schizophrenia, 60 of whom experience active auditory hallucinations at the baseline assessment, will complete an EEG assessment of auditory predictive learning three times over a six-month follow-up period. Thirty non-psychiatric comparison participants will be assessed at baseline only. Stability of the EEG predictive learning indices and associations with auditory hallucinations will be assessed over the follow-up period. Leveraging the specialized scientific expertise and the extraordinary clinical research and participant recruitment infrastructure available at the university of manitoba and the strong domestic scientific leadership and veteran representation available at the minneapolis VA, we anticipate that the project will contribute to the health of the american public by identifying key biological markers and clarifying our understanding of the pathophysiology of this debilitating mental health condition in order to support the development of new treatments.