Individual cortical connectivity changes after stroke: a resampling approach to enable statistical assessment at...

Area of Science:

• Neuroscience
• Biostatistics
• Medical Imaging

Background:

• Assessing brain connectivity changes at the individual level is challenging due to limited distribution information of connectivity estimators.
• Group analyses, while common, may not capture patient heterogeneity, especially in conditions like stroke.
• Quantitative measurement of rehabilitation efficacy in stroke patients requires methods sensitive to individual network configurations and lesion effects.

Purpose of the Study:

• To develop and validate a resampling-based approach for statistically assessing significant changes in cortical connectivity networks at the single-subject level.
• To address the limitations of group analyses in capturing individual patient variability and treatment response.
• To provide a tool for evaluating the impact of brain lesions and rehabilitation interventions on neural networks.

Main Methods:

• A resampling-based statistical approach was developed for single-subject brain network analysis.
• A simulation study was conducted to evaluate the performance of the proposed method under various conditions.
• The method was applied to electroencephalographic (EEG) data from post-stroke patients with differing recovery outcomes.

Main Results:

• The simulation study demonstrated the performance of the resampling approach under different conditions.
• Application to EEG data from two post-stroke patients illustrated the method's sensitivity to individual recovery patterns.
• The approach allows for the assessment of statistically significant changes in brain connectivity at the individual level.

Conclusions:

• The proposed resampling-based method offers a viable solution for statistically assessing individual brain network changes.
• This approach can aid in understanding the relationship between brain network alterations, lesion effects, and functional recovery after stroke.
• The method holds promise for personalized assessment of rehabilitation interventions and patient outcomes.