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Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants

GF21-14727K [Registered results] 2021 - 2024

The concept of synchronization of nonlinear dynamical systems will serve as basis for development of mathematical methods and computer algorithms for detection and characterization of interactions and dependence in multivariate nonlinear time series. Directional links and causal relations will be quantified using the tools of information theory. The developed methods will be tailored to specific properties of scalp electroencephalogram (EEG). Overall structure of EEG synchronization, reflecting the functional integration within and across different spatial and temporal scales, will be classified in machine learning algorithms as a candidate method for description of brain states and their changes due to mental disorders. In particular, synchronization and its changes in EEG of depressive patients will be tested as predictors of antidepressant therapeutic efficacy. The developed methods will be applicable not only in analysis of electrophysiological signals in neurology and psychiatry, but generally in analysis of complex multivariate and multiscale signals.