Data-Driven Identification of Functional Network Changes in Neurofeedback Stroke Rehabilitation: a Clinical Validation of Network-Based Statistics
Year: 2025
Type: conference-paper
Abstract: 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.
Institutions: Centre National de la Recherche Scientifique, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, IMT Atlantique, Institut national de recherche en sciences et technologies du numérique, Inserm, Institut de Recherche en Informatique et Systèmes Aléatoires, Empenn, Université de Rennes, Centre Hospitalier Universitaire de Rennes
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4416219334
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