In-silico EEG biomarkers of reduced inhibition in human cortical microcircuits 
in depression

Mazza, Frank; Guet-McCreight, Alexandre; Valiante, Taufik A.; Griffiths, John D.; Hay, Etay; Cagnan, Hayriye

doi:10.1371/journal.pcbi.1010986

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Journal Article

In-silico EEG biomarkers of reduced inhibition in human cortical microcircuits in depression

MPS-Authors

Valiante, Taufik A.
External Organizations;
Max Planck - University of Toronto Centre for Neural Science and Technology, Max Planck Institute of Microstructure Physics, Max Planck Society;

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https://zenodo.org/badge/latestdoi/610353511
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https://doi.org/10.1371/journal.pcbi.1010986
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Citation

Mazza, F., Guet-McCreight, A., Valiante, T. A., Griffiths, J. D., & Hay, E. (2023). In-silico EEG biomarkers of reduced inhibition in human cortical microcircuits in depression. PLoS Computational Biology, 19: e1010986. doi:10.1371/journal.pcbi.1010986.

Cite as: https://hdl.handle.net/21.11116/0000-000D-8E6E-9

Abstract

Reduced cortical inhibition by somatostatin-expressing (SST) interneurons has been strongly associated with treatment-resistant depression. However, due to technical limitations it is impossible to establish experimentally in humans whether the effects of reduced SST interneuron inhibition on microcircuit activity have signatures detectable in clinically-relevant brain signals such as electroencephalography (EEG). To overcome these limitations, we simulated resting-state activity and EEG using detailed models of human cortical microcircuits with normal (healthy) or reduced SST interneuron inhibition (depression), and found that depression microcircuits exhibited increased theta, alpha and low beta power (4–16 Hz). The changes in depression involved a combination of an aperiodic broadband and periodic theta components. We then demonstrated the specificity of the EEG signatures of reduced SST interneuron inhibition by showing they were distinct from those corresponding to reduced parvalbumin-expressing (PV) interneuron inhibition. Our study thus links SST interneuron inhibition level to distinct features in EEG simulated from detailed human microcircuits, which can serve to better identify mechanistic subtypes of depression using EEG, and non-invasively monitor modulation of cortical inhibition.