Decoding post-stroke motor function from structural brain imaging.

ABSTRACT: Clinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions and to account for the interaction among units of information in the brain. Application of machine learning in structural imaging to investigate diseases that involve brain injury presents an additional challenge, especially in conditions like stroke, due to the high variability across patients regarding characteristics of the lesions. Extracting data from anatomical images in a way that translates brain damage information into features to be used as input to learning algorithms is still an open question. One of the most common approaches to capture regional information from brain injury is to obtain the lesion load per region (i.e. the prop

SUBMITTER: Rondina JM 

PROVIDER: S-EPMC4995603 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature