bioRxivNeurosciencePreprint
Aberrant E-I Balance and Brain Criticality in Major Depressive Disorder
Asako Mitsuto NagaseiD, Abhimanyu Bhardwaj, Linden ParkesiD, Fatima Faruqi, Linda L. CarpenteriD, Andrew WestbrookiD
Abstract
Brain criticality and complexity are increasingly recognized as promising biomarkers for psychiatric disorders. In Major Depressive Disorder (MDD), disordered neural dynamics have been reported, but their nature and consistency remain incompletely understood. Here, we study brain criticality, excitation-inhibition (E/I) balance, combined excitation- inhibition strength (E+I), and complexity of brain dynamics associated with MDD. Using resting-state EEG from 183 patients with MDD and 133 healthy controls (HC), we identified disruptions of critical dynamics and excitation-inhibition balance which discriminate groups. We found that amplitude bistability is lower, and long-range temporal correlations are weaker in MDD, implying deviation from criticality. Excitation-inhibition metrics show frequency-specific alterations in MDD. Estimates of excitation-inhibition ratios (E/I) derived from the statistical properties of amplitude fluctuations show higher values in HC than MDD in the {theta} band, indicating relative over-excitation, and lower values in the {gamma} band, indicating relative over-inhibition. An excitation- inhibition strength index reflecting combined excitatory and inhibitory drive (E+I), was decreased in {theta} through {beta} bands and increased in {gamma} in MDD. Collectively, excitation-inhibition measures suggest decreased inhibitory drive in the mechanisms underlying {theta} oscillations in MDD and increased inhibitory drive in the mechanisms underlying {gamma} oscillations. Classification using least absolute shrinkage and selection operator (LASSO) regression achieved high accuracy and the predictive feature set includes measures of criticality, E/I ratios, and combined E+I strength. These findings elucidate pathological alterations of brain dynamics in MDD and define a complex system fingerprint, supporting the development of biomarkers for diagnosis and treatment.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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