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Conference Papers Year : 2016

Brain network modules of meaningful and meaningless 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 the dense electroencephalography (EEG) source connectivity methods were used to identify cortical networks with excellent time resolution (in the order of millisecond). Functional networks were identified 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 occipito-temporal 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.

Domains

Bioengineering
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Dates and versions

hal-01479226 , version 1 (28-02-2017)

Identifiers

  • HAL Id : hal-01479226 , version 1

Cite

J. Rizkallah, P. Benquet, F. Wendling, M. Khalil, Ahmad Mheich, et al.. Brain network modules of meaningful and meaningless objects. 3rd Middle East Conference on Biomedical Engineering (MECBME), Oct 2016, Beirut, Lebanon. pp.34--37. ⟨hal-01479226⟩
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