A data-driven approach to post-stroke aphasia classification and lesion-based prediction.

ABSTRACT: Aphasia is an acquired impairment in the production or comprehension of language, typically caused by left hemisphere stroke. The subtyping framework used in clinical aphasiology today is based on the Wernicke-Lichtheim model of aphasia formulated in the late 19th century, which emphasizes the distinction between language production and comprehension. The current study used a data-driven approach that combined modern statistical, machine learning, and neuroimaging tools to examine behavioural deficit profiles and their lesion correlates and predictors in a large cohort of individuals with post-stroke aphasia. First, individuals with aphasia were clustered based on their behavioural deficit profiles using community detection analysis (CDA) and these clusters were compared with the tradition

SUBMITTER: Landrigan JF 

PROVIDER: S-EPMC8219353 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature