Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions.

ABSTRACT: The severity of post-stroke aphasia and the potential for recovery are highly variable and difficult to predict. Evidence suggests that optimal estimation of aphasia severity requires the integration of multiple neuroimaging modalities and the adoption of new methods that can detect multivariate brain-behavior relationships. We created and tested a multimodal framework that relies on three information sources (lesion maps, structural connectivity, and functional connectivity) to create an array of unimodal predictions which are then fed into a final model that creates "stacked multimodal predictions" (STAMP). Crossvalidated predictions of four aphasia scores (picture naming, sentence repetition, sentence comprehension, and overall aphasia severity) were obtained from 53 left hemispheric ch

SUBMITTER: Pustina D 

PROVIDER: S-EPMC5765865 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature