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Background <p>Severe upper limb impairment (ULI) presents a significant challenge in the rehabilitation of chronic stroke survivors and affects their quality of life. Identifying biomarkers and understanding the neural mechanisms associated with severe ULI are essential for evaluating recovery potential and enhancing rehabilitation effectiveness. This study aimed to identify resting-state electroencephalography (EEG) functional connectivity features associated with severe ULI in chronic stroke survivors using machine learning (ML) methods.</p> Methods <p>EEG data were collected from 34 chronic stroke survivors. Participants were categorized into two groups based on their Fugl–Meyer Assessment for Upper Extremity (FMA-UE) scores: a mild/moderate ULI (FMA-UE ≥ 30; <i>n</i> = 19) and severe ULI (FMA-UE &lt; 30; <i>n</i> = 15). We employed ML algorithms to classify severe ULI, including logistic regression with L1, elastic net regularization, stochastic gradient descent, and support vector machines, along with several feature selection methods. Coherence was evaluated across six frequency bands in both the ipsilesional (affected by the lesion) and contralesional (opposite side of the lesion) hemispheres.</p> Results <p>The logistic regression model with L1 and ReliefF feature selection methods was the most effective, achieving a balanced accuracy of 0.91 (sensitivity = 0.93; specificity = 0.90). This approach identified 14 significant features for distinguishing severe ULI from mild to moderate ULI, including delta interhemispheric and intrahemispheric connectivity in the frontal, parietal, and temporal regions. Additionally, interhemispheric and intrahemispheric theta connectivity was observed in the prefrontal, frontal, temporal, and parietal regions. Low-beta intrahemispheric connectivity was also observed in the contralesional parietal regions.</p> Conclusions <p>Our research highlights the association between alterations in connectivity within low-frequency bands and severe ULI across widespread brain regions, including areas outside the sensorimotor cortex and bilateral intrahemispheric and interhemispheric regions. Further research utilizing larger longitudinal datasets from early stroke survivors employing ML approaches could contribute to the development of more accurate predictive models for motor recovery and rehabilitation responses.</p>

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Functional connectivity associated with severe upper limb impairment in resting-state electroencephalography among chronic stroke survivors: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Ji-Yoon Lee,
  • Miseon Shim,
  • Won Kee Chang,
  • Hee-Mun Cho,
  • Ji-Soo Choi,
  • Hyunji Kim,
  • Bongwon Suh,
  • Nam-Jong Paik,
  • Han-Jeong Hwang,
  • Won-Seok Kim

摘要

Background

Severe upper limb impairment (ULI) presents a significant challenge in the rehabilitation of chronic stroke survivors and affects their quality of life. Identifying biomarkers and understanding the neural mechanisms associated with severe ULI are essential for evaluating recovery potential and enhancing rehabilitation effectiveness. This study aimed to identify resting-state electroencephalography (EEG) functional connectivity features associated with severe ULI in chronic stroke survivors using machine learning (ML) methods.

Methods

EEG data were collected from 34 chronic stroke survivors. Participants were categorized into two groups based on their Fugl–Meyer Assessment for Upper Extremity (FMA-UE) scores: a mild/moderate ULI (FMA-UE ≥ 30; n = 19) and severe ULI (FMA-UE < 30; n = 15). We employed ML algorithms to classify severe ULI, including logistic regression with L1, elastic net regularization, stochastic gradient descent, and support vector machines, along with several feature selection methods. Coherence was evaluated across six frequency bands in both the ipsilesional (affected by the lesion) and contralesional (opposite side of the lesion) hemispheres.

Results

The logistic regression model with L1 and ReliefF feature selection methods was the most effective, achieving a balanced accuracy of 0.91 (sensitivity = 0.93; specificity = 0.90). This approach identified 14 significant features for distinguishing severe ULI from mild to moderate ULI, including delta interhemispheric and intrahemispheric connectivity in the frontal, parietal, and temporal regions. Additionally, interhemispheric and intrahemispheric theta connectivity was observed in the prefrontal, frontal, temporal, and parietal regions. Low-beta intrahemispheric connectivity was also observed in the contralesional parietal regions.

Conclusions

Our research highlights the association between alterations in connectivity within low-frequency bands and severe ULI across widespread brain regions, including areas outside the sensorimotor cortex and bilateral intrahemispheric and interhemispheric regions. Further research utilizing larger longitudinal datasets from early stroke survivors employing ML approaches could contribute to the development of more accurate predictive models for motor recovery and rehabilitation responses.