1/f neural noise is a better predictor of schizophrenia than neural oscillations

Peterson, E. J.; Rosen, B. Q.; Campbell, A. M.; Belger, A.; Bradley, B.

doi:10.1101/113449

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bioRxiv · 10.1101/113449

1/f neural noise is a better predictor of schizophrenia than neural oscillations

Peterson, E. J.·Rosen, B. Q.·Campbell, A. M.·Belger, A.·Bradley, B.

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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/fX) 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 a 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. Significance statementUnderstanding the neurobiological origins of schizophrenia and identifying reliable and consistent biomarkers are of critical importance to improving treatment of that disease. Numerous studies have reported disruptions to neural oscillations in patients with schizophrenia. This has, in part, led to schizophrenia being characterized as a disease of disrupted neural coordination, reflected by changes in frequency band power. We report however that changes in the aperiodic signal can also predict clinical status. Unlike band-limited power though, aperiodic activity predicts status better than participants own behavioral performance and acts as a consistent predictor across all electrodes. Alterations in the aperiodic signal are consistent with well-established inhibitory neuron dysfunctions associated with schizophrenia, allowing for a direct link between noninvasive EEG and chronic, widespread, neurobiological deficits.

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Peterson, E. J., Rosen, B. Q., Campbell, A. M., Belger, A., Bradley, B.. 2017-03-03. 1/f neural noise is a better predictor of schizophrenia than neural oscillations. https://doi.org/10.1101/113449

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