Data-Driven Identification of Functional Network Changes in Neurofeedback Stroke Rehabilitation: a Clinical Validation of Network-Based Statistics

Functional connectivity (FC) analysis is crucial for understanding neuroplasticity in stroke rehabilitation. Neuro-feedback (NF) training has shown promise in facilitating recovery, but its whole-brain effects remain poorly understood due to limitations in traditional FC analysis methods. Many studies rely on region-of-interest (ROI)-based approaches, which restrict analysis to predefined regions, or whole-brain mass univariate tests, which suffer from the multiple comparisons problem. In this study, we apply Network-Based Statistics (NBS), a graphtheoretic signal processing approach, to identify data-driven FC changes following NF-based stroke rehabilitation. Using fMRI data, we detected two significant network components: one within the somatomotor network, reflecting expected motor recovery processes, and another within the default mode network (DMN), highlighting broader neuroplasticity effects. Our findings validate NBS as a robust tool for unbiased, whole-brain connectivity analysis, offering new insights into the distributed impact of NF training in stroke rehabilitation.