Prediction of Long-term Cognitive Functions after Minor Stroke, Using Functional Connectivity.

To determine whether functional MRI connectivity can predict the long-term cognitive functions 36 months after minor stroke. Seventy-two participants with first-ever stroke were included at baseline and followed up for 36 months. A ridge regression machine learning algorithm was developed and used to predict cognitive scores 36 months post-stroke on the basis of the functional networks measured using MRI at 6 months (referred to here as the post-stroke cognitive impairment (PSCI) network). The prediction accuracy was evaluated in four domains (memory, attention/executive, language and visuospatial functions) and compared with clinical data and other functional networks. The models' statistical significance was probed with permutation tests. The potential involvement of cortical atrophy was assessed 6 months post-stroke. A second, independent dataset (n=40) was used to validate the results and assess their generalizability. Based on the PSCI network, a machine learning model was able to predict memory, attention, visuospatial functions and language functions 36 months post-stroke (r A machine learning model based on the PSCI network can predict the long-term cognitive outcome after stroke.

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© 2021 American Academy of Neurology.