Connectome guided approaches to cognitive rehabilitation after stroke

Abstract

Stroke is among the major causes of disability around the world, and cognitive deficits remain one of the most disabling and consistently overlooked consequences. Standard rehabilitative techniques often struggle to promote the neural reorganization needed to foster long-lasting improvement in patients’ cognitive skills because they do not account for patients’ personal network-level pathology. Connectome refers to the complete set of structural and functional connections within the brain and offers a groundbreaking tool for understanding cognitive impairment following strokes and guiding treatment. This narrative review highlights how current research demonstrates the role stroke plays in disrupting large-scale cognitive networks, namely the default mode network, the frontoparietal network, and the salience network. We then discuss how connectome analysis may help tailor personalized therapy by aiding decision-making, allowing clinicians to target specific brain areas when applying treatments such as transcranial magnetic stimulation, tDCS, neurofeedback, brain-computer interface technologies, virtual reality, and behavior-based interventions (language training, attention training). We further clarify which observations are well-established and where the field still relies on emerging data to inform predictions regarding the efficacy of different interventions. Limitations in predictive modeling of stroke-related connectomic alterations will be highlighted (reproducibility issues, limited sample sizes, and lack of consensus on image-acquisition protocols). It outlines the process for translating knowledge into practice. Potential future avenues for research include serial neuroimaging, machine learning, omics biomarkers, and behavioral targets. Connectomics is not an intervention but represents the scientific basis needed for making interventions more efficient. Not applicable.