Aim. Neural plastic changes are experience and learning dependent, yet exploiting this knowledge to enhance clinical outcomes after stroke is in its infancy. Our aim was to search the available evidence for the core concepts of neuroplasticity, stroke recovery, and learning; identify links between these concepts; and identify and review the themes that best characterise the intersection of these three concepts. Methods. We developed a novel approach to identify the common research topics among the three areas: neuroplasticity, stroke recovery, and learning. A concept map was created a priori, and separate searches were conducted for each concept. The methodology involved three main phases: data collection and filtering, development of a clinical vocabulary, and the development of an automatic clinical text processing engine to aid the process and identify the unique and common topics. The common themes from the intersection of the three concepts were identified. These were then reviewed, with particular reference to the top 30 articles identified as intersecting these concepts. Results. The search of the three concepts separately yielded 405,636 publications. Publications were filtered to include only human studies, generating 263,751 publications related to the concepts of neuroplasticity (n=6,498), stroke recovery (n=79,060), and learning (n=178,193). A cluster concept map (network graph) was generated from the results; indicating the concept nodes, strength of link between nodes, and the intersection between all three concepts. We identified 23 common themes (topics) and the top 30 articles that best represent the intersecting themes. A time-linked pattern emerged. Discussion and Conclusions. Our novel approach developed for this review allowed the identification of the common themes/topics that intersect the concepts of neuroplasticity, stroke recovery, and learning. These may be synthesised to advance a neuroscience-informed approach to stroke rehabilitation. We also identified gaps in available literature using this approach. These may help guide future targeted research.

Stroke recovery is multifaceted and complex. Machine learning approaches have potential to identify patterns of brain activity associated with clinical outcomes, providing new insights into recovery. We aim to use machine learning to characterise the contribution of and potential interaction between resting state functional connectivity networks in predicting touch discrimination outcomes in a well-phenotyped, but small, stroke cohort. We interrogated and compared a suite of automated machine learning approaches to identify patterns of brain activity associated with clinical outcomes. Using feature reduction, the identification of combined ‘golden features’, and five-fold cross-validation, two golden features patterns emerged. These golden features identified patterns of resting state connectivity involving interactive relationships: 1. The difference between right insula and right superior temporal lobe correlation and left cerebellum and vermis correlation; 2. The ratio between right inferior temporal lobe and left cerebellum correlation and left frontal inferior operculum and left supplementary motor area correlation. Our findings demonstrate evidence of the potential for automated machine learning to provide new insights into brain network patterns and their interactions associated with the prediction of quantitative touch discrimination outcomes, through the automated identification of robust associations and golden feature brain patterns, even in a small cohort of stroke survivors.