@ARTICLE{10.3389/fneur.2020.00120, AUTHOR={Osa García, Alberto and Brambati, Simona Maria and Brisebois, Amélie and Désilets-Barnabé, Marianne and Houzé, Bérengère and Bedetti, Christophe and Rochon, Elizabeth and Leonard, Carol and Desautels, Alex and Marcotte, Karine}, TITLE={Predicting Early Post-stroke Aphasia Outcome From Initial Aphasia Severity}, JOURNAL={Frontiers in Neurology}, VOLUME={11}, YEAR={2020}, URL={https://www.frontiersin.org/articles/10.3389/fneur.2020.00120}, DOI={10.3389/fneur.2020.00120}, ISSN={1664-2295}, ABSTRACT={Background: The greatest degree of language recovery in post-stroke aphasia takes place within the first weeks. Aphasia severity and lesion measures have been shown to be good predictors of long-term outcomes. However, little is known about their implications in early spontaneous recovery. The present study sought to determine which factors better predict early language outcomes in individuals with post-stroke aphasia.Methods: Twenty individuals with post-stroke aphasia were assessed <72 h (acute) and 10–14 days (subacute) after stroke onset. We developed a composite score (CS) consisting of several linguistic sub-tests: repetition, oral comprehension and naming. Lesion volume, lesion load and diffusion measures [fractional anisotropy (FA) and axial diffusivity (AD)] from both arcuate fasciculi (AF) were also extracted using MRI scans performed at the same time points. A series of regression analyses were performed to predict the CS at the second assessment.Results: Among the diffusion measures, only FA from right AF was found to be a significant predictor of early subacute aphasia outcome. However, when combined in two hierarchical models with FA, age and either lesion load or lesion size, the initial aphasia severity was found to account for most of the variance (R2 = 0.678), similarly to the complete models (R2 = 0.703 and R2 = 0.73, respectively).Conclusions: Initial aphasia severity was the best predictor of early post-stroke aphasia outcome, whereas lesion measures, though highly correlated, show less influence on the prediction model. We suggest that factors predicting early recovery may differ from those involved in long-term recovery.} }