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Related Experiment Video

Updated: Apr 7, 2026

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke
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Mapping Brain Function After Stroke: From Early Disorganization to Late Compensation.

Célia Delcamp1,2, Zhibin Zhou3,4, Ramesh Srinivasan3,5

  • 1Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.

Neurorehabilitation and Neural Repair
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

Electroencephalography (EEG) reveals brain reorganization after stroke. Findings highlight how EEG biomarkers depend on time post-stroke, motor deficit severity, and lesion location for personalized rehabilitation.

Keywords:
coherenceelectroencephalographyfunctional connectivitymotor statusneuroplasticityresting-state

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Area of Science:

  • Neuroscience
  • Clinical Neurology
  • Biomarkers

Background:

  • Electroencephalography (EEG) is valuable for assessing brain function post-stroke.
  • The relationship between EEG changes, time since stroke, and functional deficits requires further clarification.

Purpose of the Study:

  • To investigate how cortical power and functional connectivity change over time after stroke.
  • To determine the association between EEG measures, stroke duration, and motor deficit severity.

Main Methods:

  • A cross-sectional study involving 100 stroke patients and 45 healthy controls.
  • Dense-array (256-lead) resting-state EEG was used to record cortical power and coherence.
  • Analyses included subgroups based on time post-stroke and motor status (Fugl-Meyer score), employing permutation tests and partial Spearman's correlations.

Main Results:

  • Early post-stroke, EEG showed supranormal low-frequency power and subnormal high-frequency power, with widespread subnormal connectivity.
  • Over time, beta and gamma connectivity increased, and overall brain connectivity became supranormal in the late phase.
  • Motor status correlated with cortical power in specific patient groups and with connectivity in a manner dependent on deficit severity and lesion location.

Conclusions:

  • EEG measures show promise as biomarkers for brain reorganization post-stroke.
  • Understanding EEG requires consideration of time post-stroke, deficit severity, and lesion location for personalized rehabilitation strategies.
  • Network connectivity is crucial for understanding motor deficits, emphasizing context-specific interpretation of EEG biomarkers.