Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations
Erik Peterson, Burke Q. Rosen, Ayşenil Belger, Bradley Voytek, Alana Campbell
Abstract
Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/f X ) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better-predicted group status than traditional band-limited oscillatory power in classification analysis. Aperiodic activity also outperformed the predictions made using participants’ behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls.