Evaluating lesion-specific preprocessing pipelines for rs-fMRI in stroke patients: Impact on functional connectivity and behavioral prediction.

Functional magnetic resonance imaging (fMRI) is essential for studying brain function and connectivity. Resting-state fMRI, which captures spontaneous brain activity without task requirements, is particularly suited for individuals with post-stroke impairments. However, the inherent noise and artifacts in fMRI signals can compromise analysis accuracy, especially in stroke patients with complex neurological conditions. Currently, there is no consensus on the best preprocessing approach for stroke fMRI data. In this study, we design and evaluate three preprocessing pipelines: a standard pipeline, an enhanced pipeline that accounts for lesions when computing tissue masks, and a stroke-specific pipeline that incorporates independent component analysis to address lesion-driven artifacts. These pipelines are assessed for their effectiveness in reducing spurious connectivity and improving the prediction of behavioral outcomes on a large stroke dataset. Using metrics such as connectivity mean strength and functional connectivity contrast, our results indicate that the stroke-specific pipeline significantly reduces spurious connectivity without impacting behavioral predictions. These findings underscore the need for tailored preprocessing strategies in stroke fMRI research to enhance the reliability and accuracy of connectivity measures. In addition, we make the stroke-specific pipeline accessible by designing an open-source tool (fMRIStroke), in order to ensure replicability of our results and to contribute to best practices.