The EEG spectrum as a marker of severity, burden, and psychiatric diagnosis

Summary There is an urgent need for objective and robust diagnostic indices related to psychiatric illnesses, including obsessive-compulsive and anxiety disorders. Brain-based indices are particularly suited for this purpose, but many of the available brain-based markers have limited scope, require specialized equipment, or have unknown reliability and validity. The proposed research aims to examine the value of data-based, quantitative, and objective markers of dysfunctional electrocortical processes associated with obsessive-compulsive and anxiety psychopathology. The proposed research aims to establish advanced computational indices based on resting electroencephalogram (EEG) recordings that are capable of (1) discriminating between diagnostic categories but also (2) predict transdiagnostic variables such as severity and comorbidity. Specifically, we use state-of-the- art data transformations to extract mechanistically informative indices of spectral shape and alpha-frequency phenomena inherent in EEG recordings during resting states. We will then establish their reliability and internal consistency—prerequisites for using them as markers of inter-individual differences. The indices will then be related to clinical data collected in a large sample of individuals presenting with symptoms on the obsessive- compulsive and anxiety spectrum. A Bayesian Hierarchical Model will be used to aid in data reduction and to measure and heighten reliability of the EEG-derived variables. Finding reliable and valid biomarkers of electrocortical processes has the potential of transforming diagnostic assessment by providing continuous indices of cortical dysfunction. If the goals of this application are met, then reliable and valid indices of electrocortical (dys)function may help to significantly shift clinical practice: In assessment, objective measures of EEG alpha reactivity could be used, for example, to objectively identify patients with perception/attention dysfunction, versus those with generally delayed oscillatory activity and thus more general cortical dysfunction. These inter-individual differences may in the future guide how patients are assigned to individualized treatment protocols as well as for predicting treatment outcome. Project Number: 1R03MH143166-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of Mental Health (NIMH) | Principal Investigator: Carol Mathews (+1 co-PI) | Institution: UNIVERSITY OF FLORIDA, GAINESVILLE, FL | Award Amount: $151,761 | Activity Code: R03 | Study Section: Neural Basis of Psychopathology, Addictions and Sleep Disorders Study Section[NPAS] View on NIH RePORTER: https://reporter.nih.gov/project-details/11292597