Brain network modules of meaningful and meaningless objects

TL;DR

This study uses high-temporal-resolution EEG to compare brain network modularity during the processing of meaningful versus meaningless visual objects, revealing greater modularity and specific connectivity patterns for meaningful stimuli.

Contribution

It introduces a method to analyze dynamic brain network reconfiguration at millisecond resolution during object recognition tasks.

Findings

Networks of meaningful objects are more modular than those of meaningless objects.

Ventral visual pathway is activated in both conditions.

Strong occipitotemporal connectivity is observed for meaningful objects.

Abstract

Network modularity is a key feature for efficient information processing in the human brain. This information processing is however dynamic and networks can reconfigure at very short time period, few hundreds of millisecond. This requires neuroimaging techniques with sufficient time resolution. Here we use the dense electroencephalography, EEG, source connectivity methods to identify cortical networks with excellent time resolution, in the order of millisecond. We identify functional networks during picture naming task. Two categories of visual stimuli were presented, meaningful (tools, animals) and meaningless (scrambled) objects. In this paper, we report the reconfiguration of brain network modularity for meaningful and meaningless objects. Results showed mainly that networks of meaningful objects were more modular than those of meaningless objects. Networks of the ventral visual pathway were activated in both cases. However a strong occipitotemporal functional connectivity appeared for meaningful object but not for meaningless object. We believe that this approach will give new insights into the dynamic behavior of the brain networks during fast information processing.