Dynamic connectivity predicts acute motor impairment and recovery post-stroke

Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from...

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Detalles Bibliográficos
Autores principales: Bonkhoff, Anna K, Rehme, Anne K, Hensel, Lukas, Tscherpel, Caroline, Volz, Lukas J, Espinoza, Flor A, Gazula, Harshvardhan, Vergara, Victor M, Fink, Gereon R, Calhoun, Vince D, Rost, Natalia S, Grefkes, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578497/
https://www.ncbi.nlm.nih.gov/pubmed/34778761
http://dx.doi.org/10.1093/braincomms/fcab227

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578497/
https://www.ncbi.nlm.nih.gov/pubmed/34778761
http://dx.doi.org/10.1093/braincomms/fcab227

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