Combining EEG source connectivity and network similarity: Application to object categorization in the human brain

TL;DR

This study combines dense-EEG source connectivity and network similarity algorithms to analyze brain network dynamics during object categorization, revealing distinct patterns associated with visual recognition processes.

Contribution

It introduces a novel approach integrating dense-EEG source connectivity with network similarity analysis to study rapid brain network changes during object categorization.

Findings

High network similarity within categories

Low similarity between categories

Significant differences at 120-190ms related to visual recognition

Abstract

A major challenge in cognitive neuroscience is to evaluate the ability of the human brain to categorize or group visual stimuli based on common features. This categorization process is very fast and occurs in few hundreds of millisecond time scale. However, an accurate tracking of the spatiotemporal dynamics of large-scale brain networks is still an unsolved issue. Here, we show the combination of recently developed method called dense-EEG source connectivity to identify functional brain networks with excellent temporal and spatial resolutions and an algorithm, called SimNet, to compute brain networks similarity. Two categories of visual stimuli were analysed in this study: immobile and mobile. Networks similarity was assessed within each category (intra-condition) and between categories (inter-condition). Results showed high similarity within each category and low similarity between the two categories. A significant difference between similarities computed in the intra and inter-conditions was observed at the period of 120-190ms supposed to be related to visual recognition and memory access. We speculate that these observations will be very helpful toward understanding the object categorization in the human brain from a network perspective.