An EEG based ‐ stochastic dynamical systems model of brain dynamics

Abstract

[en] How does the human brain work? How do different brain areas interact with each other when performing specific function? These questions have sharply increased in interest over the last decades, as the more it is known about human cognition and cognitive process distribution the more accurate some procedures will be, such as neuro-pathologies diagnosis, prediction of reaction to stimuli or influence of motivation/rewards on decisions. To analyze human cognition, neuroimaging techniques are commonly used, like Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) or Electroencephalograms (EEGs). The aim of this project is to build a theoretical model, able to capture the neural dynamics of cortical interactions, which we referred to as effective connectivity. Neural data are high-density EEGs, recorded during a decision-making task (Cos et al. 2022). This approach overcomes the limitations that are presented when directly using correlation based connectivity metrics. The framework we created consists of a model-based whole-brain effective connectivity, based on the multivariate Ornstein-Uhlenbeck (MOU) process (MOU-EC). The goal of the model, once fitted, is to provide a directed connectivity estimate that reflects the dynamical state of the EEG signals and a method to generate signals that follow the connectivity.