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Related Experiment Video

Updated: Jun 5, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Sub-acute stroke demonstrates altered beta oscillation and connectivity pattern in working memory.

Lin Mao1, Xianwei Che2, Juehan Wang1

  • 1Departments of Physical Medicine and Rehabilitation, The First Affiliated Hospital, Zhejiang University School of Medicine, Building 6, 58 Chengzhan Road, Hangzhou, 310003, China.

Journal of Neuroengineering and Rehabilitation
|December 5, 2024
PubMed
Summary
This summary is machine-generated.

Stroke survivors with poor recovery show impaired working memory due to reduced beta rebound and network connectivity. These brainwave patterns may predict stroke rehabilitation outcomes.

Keywords:
EEGOscillationsPrognosisStrokeWorking memory

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Area of Science:

  • Neuroscience
  • Rehabilitation Medicine
  • Cognitive Science

Background:

  • Working memory (WM) is crucial for neural restoration and relearning after stroke.
  • Investigating WM's oscillatory mechanisms in subacute stroke is vital for understanding recovery.
  • Electroencephalography (EEG) offers a non-invasive method to study brain activity.

Purpose of the Study:

  • To investigate the oscillatory mechanisms of working memory in subacute stroke patients.
  • To differentiate WM characteristics between good and poor recovery groups.
  • To identify potential biomarkers for stroke prognosis.

Main Methods:

  • Included 48 subacute stroke patients (26 good-recovery, 22 poor-recovery) and 24 healthy controls.
  • Utilized EEG to examine oscillatory characteristics and functional connectivity during a 0-back WM task.
  • Assessed the association between WM parameters and 4-week recovery prognosis.

Main Results:

  • Poor recovery patients exhibited a loss of beta rebound and impaired beta-band connectivity.
  • Working memory speed and performance were significantly reduced in the poor recovery group.
  • Beta rebound was strongly correlated with working memory speed and performance.

Conclusions:

  • Beta rebound and network connectivity are mechanistic indicators of impaired working memory post-stroke.
  • These oscillatory features may serve as biomarkers for stroke recovery.
  • Findings support the role of WM in stroke rehabilitation and neural restoration.