Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Statistical network analysis for epilepsy MEG data

  • Haeji Lee (Department of Statistics, Duksung Women's University) ;
  • Chun Kee Chung (Department of Neurosurgery, Seoul National University Hospital) ;
  • Jaehee Kim (Department of Statistics, Duksung Women's University)
  • Received : 2023.06.07
  • Accepted : 2023.09.18
  • Published : 2023.11.30
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Abstract

Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Magnetoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static/temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network differences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (BRL No. 2021R1A4A5028907) and Basic Research (No. 2021R1F1A1054968).

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