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Related Experiment Video

Updated: Jul 13, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

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Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network

Minwoo Lee1, Yuseong Hong2, Sungsik An3

  • 1Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea.

Frontiers in Aging Neuroscience
|October 16, 2023
PubMed
Summary

Electroencephalography (EEG) brain network properties can predict cognitive impairment after acute ischemic stroke (AIS). Machine learning models using acute EEG data accurately forecast cognitive outcomes three months post-stroke.

Keywords:
cognitionelectroencephalographyfunctional networkischemic strokemachine learning

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Post-stroke cognitive impairment (PSCI) affects over 50% of acute ischemic stroke (AIS) patients, hindering neurological recovery.
  • Predicting cognitive trajectories after AIS is essential for effective patient management and rehabilitation strategies.

Purpose of the Study:

  • To investigate if electroencephalography (EEG)-derived brain network properties can predict post-stroke cognitive function.
  • To utilize a machine learning approach for predicting cognitive outcomes in AIS patients.

Main Methods:

  • Eighty-seven AIS patients underwent acute EEG and 3-month cognitive assessments (MoCA).
  • EEG data were preprocessed, and network characteristics were quantified using iSyncBrain®.
  • Machine learning models were developed to predict cognitive status based on lesion lateralization and EEG network properties.

Main Results:

  • EEG attributes accurately predicted lesion laterality (97.0%).
  • In the left hemispheric lesion group, theta band network attributes correlated with MoCA scores (R-squared=0.76).
  • Higher global efficiency and clustering coefficient, and lower characteristic path length were linked to better cognitive function.

Conclusions:

  • Acute-phase EEG-based brain network properties, analyzed via machine learning, show potential for predicting cognitive outcomes after ischemic stroke.
  • This approach offers a promising tool for early identification of patients at risk for PSCI.