Predicting language recovery in post-stroke aphasia using behavior and functional MRI

Abstract Language outcomes after speech and language therapy in post-stroke aphasia are challenging to predict. This study examines behavioral language measures and resting state fMRI (rsfMRI) as predictors of treatment outcome. Fifty-seven patients with chronic aphasia were recruited and treated fo...

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Autores principales:	Michael Iorga, James Higgins, David Caplan, Richard Zinbarg, Swathi Kiran, Cynthia K. Thompson, Brenda Rapp, Todd B. Parrish
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d95d3d16dffb4fdcb0c47894a9bcf118
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spelling	oai:doaj.org-article:d95d3d16dffb4fdcb0c47894a9bcf1182021-12-02T18:27:48ZPredicting language recovery in post-stroke aphasia using behavior and functional MRI10.1038/s41598-021-88022-z2045-2322https://doaj.org/article/d95d3d16dffb4fdcb0c47894a9bcf1182021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88022-zhttps://doaj.org/toc/2045-2322Abstract Language outcomes after speech and language therapy in post-stroke aphasia are challenging to predict. This study examines behavioral language measures and resting state fMRI (rsfMRI) as predictors of treatment outcome. Fifty-seven patients with chronic aphasia were recruited and treated for one of three aphasia impairments: anomia, agrammatism, or dysgraphia. Treatment effect was measured by performance on a treatment-specific language measure, assessed before and after three months of language therapy. Each patient also underwent an additional 27 language assessments and a rsfMRI scan at baseline. Patient scans were decomposed into 20 components by group independent component analysis, and the fractional amplitude of low-frequency fluctuations (fALFF) was calculated for each component time series. Post-treatment performance was modelled with elastic net regression, using pre-treatment performance and either behavioral language measures or fALFF imaging predictors. Analysis showed strong performance for behavioral measures in anomia (R2 = 0.948, n = 28) and for fALFF predictors in agrammatism (R2 = 0.876, n = 11) and dysgraphia (R2 = 0.822, n = 18). Models of language outcomes after treatment trained using rsfMRI features may outperform models trained using behavioral language measures in some patient populations. This suggests that rsfMRI may have prognostic value for aphasia therapy outcomes.Michael IorgaJames HigginsDavid CaplanRichard ZinbargSwathi KiranCynthia K. ThompsonBrenda RappTodd B. ParrishNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael Iorga James Higgins David Caplan Richard Zinbarg Swathi Kiran Cynthia K. Thompson Brenda Rapp Todd B. Parrish Predicting language recovery in post-stroke aphasia using behavior and functional MRI
description	Abstract Language outcomes after speech and language therapy in post-stroke aphasia are challenging to predict. This study examines behavioral language measures and resting state fMRI (rsfMRI) as predictors of treatment outcome. Fifty-seven patients with chronic aphasia were recruited and treated for one of three aphasia impairments: anomia, agrammatism, or dysgraphia. Treatment effect was measured by performance on a treatment-specific language measure, assessed before and after three months of language therapy. Each patient also underwent an additional 27 language assessments and a rsfMRI scan at baseline. Patient scans were decomposed into 20 components by group independent component analysis, and the fractional amplitude of low-frequency fluctuations (fALFF) was calculated for each component time series. Post-treatment performance was modelled with elastic net regression, using pre-treatment performance and either behavioral language measures or fALFF imaging predictors. Analysis showed strong performance for behavioral measures in anomia (R2 = 0.948, n = 28) and for fALFF predictors in agrammatism (R2 = 0.876, n = 11) and dysgraphia (R2 = 0.822, n = 18). Models of language outcomes after treatment trained using rsfMRI features may outperform models trained using behavioral language measures in some patient populations. This suggests that rsfMRI may have prognostic value for aphasia therapy outcomes.
format	article
author	Michael Iorga James Higgins David Caplan Richard Zinbarg Swathi Kiran Cynthia K. Thompson Brenda Rapp Todd B. Parrish
author_facet	Michael Iorga James Higgins David Caplan Richard Zinbarg Swathi Kiran Cynthia K. Thompson Brenda Rapp Todd B. Parrish
author_sort	Michael Iorga
title	Predicting language recovery in post-stroke aphasia using behavior and functional MRI
title_short	Predicting language recovery in post-stroke aphasia using behavior and functional MRI
title_full	Predicting language recovery in post-stroke aphasia using behavior and functional MRI
title_fullStr	Predicting language recovery in post-stroke aphasia using behavior and functional MRI
title_full_unstemmed	Predicting language recovery in post-stroke aphasia using behavior and functional MRI
title_sort	predicting language recovery in post-stroke aphasia using behavior and functional mri
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d95d3d16dffb4fdcb0c47894a9bcf118
work_keys_str_mv	AT michaeliorga predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri AT jameshiggins predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri AT davidcaplan predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri AT richardzinbarg predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri AT swathikiran predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri AT cynthiakthompson predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri AT brendarapp predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri AT toddbparrish predictinglanguagerecoveryinpoststrokeaphasiausingbehaviorandfunctionalmri
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Predicting language recovery in post-stroke aphasia using behavior and functional MRI

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