Abstract

Extracting information from our brain activities in a non-invasive way is of utmost importance to be able to implement preventive strategies or to monitor the efficacy of  treatment on multifactorial pathologies such as psychiatric and behavioral disorders but also on more common conditions such as anxiety and depression. High-density electroencephalography (hd-EEG) provides an accessible indirect method to record spatio-temporal brain activity with potential for disease diagnosis and monitoring. However, due to their highly multidimensional nature, extracting useful information from hd-EEG recordings is a complex task. Network representations have been shown to provide an intuitive picture of the spatial connectivity underlying an electroencephalogram recording, although some information is lost in the projection. We will discuss our recent strategy where we construct multilayer network representations of hd-EEG recordings that is able to maximize their information content. We tested this method on sleep data recorded in individuals with mental health issues.    

[1] Font-Clos F, Spelta B, D’Agostino A, Donati F, Sarasso S, Canevini MP, Zapperi S, La Porta CA. Information optimized multilayer network representation of high density electroencephalogram recordings. Frontiers in Network Physiology. 2021;1:746118.