Mapping brain networks and cognitive functioning after stroke: A systematic review

Abstract

Stroke, the second leading cause of death, exhibits no significant sex differences and primarily affects the elderly, with sociodemographic and income factors playing a role. Lifestyle patterns, including elevated blood pressure, weight, glucose levels, air pollution exposure, smoking, and nutrition, contribute to stroke risk. Stroke's impact on the brain's functional and structural integrity leads to cognitive deficits and challenges in daily activities. Rehabilitation is crucial for functional recovery. This review explores the association between brain networks and behavioral deficits post-stroke, aiming to establish a cartographic approach for predicting rehabilitation outcomes. Methodologically, a systematic review following PRISMA-ScR guidelines was conducted, searching PUBMED and SCOPUS for relevant studies from 2003 to 2023. The synthesis of 29 studies reveals insights into language, comprehension, general cognition, praxis, and complex cognitive abilities after stroke. Language recovery involves networks like the presupplementary motor area, Default Mode Network, and sensorimotor integration. Comprehension deficits result from focal lesions and left hemisphere stroke, with connectivity training showing potential. General cognition studies emphasize the role of working memory, connectivity patterns predicting ischemic attacks, and cognitive impairment post-subtentorial strokes. Praxis studies highlight the importance of spared left hemisphere regions, interhemispheric connectivity, and cognitive mechanisms in complex figure copying tasks. The intricate relationship between complex cognitive abilities and brain networks is explored, revealing the impact of damage on verbal creativity, mental state judgments, affordance-based processing, and beta-band phase synchronization in memory retrieval. Strengths include a systematic search strategy and inclusion of original English studies. Limitations include the lack of statistical analysis due to heterogeneity and varying methodologies. The synthesis underscores the shift toward understanding brain function through network perspectives, combining neuroimaging with neuropsychological assessments. The integration of artificial intelligence offers promise in processing complex datasets. Future implications involve standardizing methodologies, interdisciplinary collaboration, and leveraging AI for personalized interventions, with broad applications in clinical, research, and policy domains.