Micro-states based dynamic brain connectivity in understanding the commonality and differences in gender-specific emotion processing

Abstract

In this paper, we present a data-driven micro-states based complex network analysis on cortical surface data to understand the connectives in modeling elicited emotion. In particular, we focus on processing arousal in identifying the differences and similarities between males and females. Micro-states are transient, patterned, quasi-stable states of a time series (e.g., Electroencephalography recording) that allows visualizing dynamic coupling and possibly time-varying levels of correlated or mutually informed activity between brain regions. To obtain cortical surface data from EEG recording, we use source localization method from Brainstorm. We adopted t-distributed stochastic neighbor embedding (t-SNE), for better visualization and finding the optimal number of clusters using different algorithms (e.g., Gaussian Mixture Models(GMM), K-means, etc). Centroids of these clusters are considered as micro-states. We used Hidden Markov Model (HMM) to compute the transition probabilities among micro-states. Subsequently, p-values on graph theoretic measures (e.g., modularity, small-worldness, etc) computed from micro-sites were used to determine the significantly distinguishable, highly segregated and densely integrated network of brain connectivity. Empirical analysis using DEAP dataset reveals that males and females have mostly complimentary micro-states with some commonalities. Males are more likely to stay in specific stable state and females are more likely to stay in transient states. Both groups utilize highly segregated and densely integrated network structure among brain regions in processing arousal.

Publication Title

Proceedings of the International Joint Conference on Neural Networks

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