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Related Experiment Video

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High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
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Information Optimized Multilayer Network Representation of High Density Electroencephalogram Recordings.

Francesc Font-Clos1, Benedetta Spelta1, Armando D'Agostino2,3

  • 1Center for Complexity and Biosystems, Department of Physics, University of Milan, Milano, Italy.

Frontiers in Network Physiology
|March 17, 2023
PubMed
Summary

High-density electroencephalography (hd-EEG) reveals distinct brain network patterns in individuals with mental health issues. Multilayer network analysis identified increased parieto-occipital connectivity in patients, aiding in disease stratification.

Keywords:
bipolar disorderfirst episode psychosishigh density electroencephalogrammaximum informationmultilayer networks

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Informatics

Background:

  • High-density electroencephalography (hd-EEG) offers a non-invasive method for recording brain activity, crucial for disease diagnosis and monitoring.
  • Analyzing the complex, multidimensional data from hd-EEG presents significant challenges in extracting meaningful insights.
  • Network representations provide a spatial connectivity overview of EEG data, though some information may be lost during projection.

Purpose of the Study:

  • To develop a novel method for constructing multilayer network representations of hd-EEG data that maximize information content.
  • To apply this method to sleep data from individuals with mental health issues and compare network properties with healthy controls.
  • To investigate the potential of these network measures for stratifying patients and identifying biomarkers for mental health conditions.

Main Methods:

  • Construction of multilayer network representations from hd-EEG recordings.
  • Statistical analysis of network properties including clustering coefficient, betweenness centrality, and average shortest path length.
  • Comparison of network metrics between patients with mental health issues and healthy control subjects, focusing on edge density and regional connectivity.

Main Results:

  • Significant differences in network properties were detected between patient and control groups.
  • Patients with mood disorders exhibited increased edge presence in the parieto-occipital region, indicating heightened electrical activity correlation.
  • Multilayer networks analyzed at constant edge density demonstrated improved performance, mitigating confounding factors.

Conclusions:

  • Multilayer network analysis of hd-EEG data enables effective stratification of patients with mental health conditions.
  • The findings highlight strongly correlated signals in the parieto-occipital region for individuals with mental health issues.
  • This methodology serves as a valuable tool for visualizing and analyzing hd-EEG recordings across various pathologies.