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Predicting language recovery in post-stroke aphasia using behavior and functional MRI.
Iorga, Michael; Higgins, James; Caplan, David; Zinbarg, Richard; Kiran, Swathi; Thompson, Cynthia K; Rapp, Brenda; Parrish, Todd B.
Afiliación
  • Iorga M; Center for the Neurobiology of Language Recovery, Northwestern University, Evanston, IL, USA. michael.iorga@northwestern.edu.
  • Higgins J; Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Chicago, IL, USA. michael.iorga@northwestern.edu.
  • Caplan D; Department of Radiology, Feinberg School of Medicine, Northwestern University, Suite 1600, 737 N. Michigan Ave., Chicago, IL, 60611, USA. michael.iorga@northwestern.edu.
  • Zinbarg R; Center for the Neurobiology of Language Recovery, Northwestern University, Evanston, IL, USA.
  • Kiran S; Department of Radiology, Feinberg School of Medicine, Northwestern University, Suite 1600, 737 N. Michigan Ave., Chicago, IL, 60611, USA.
  • Thompson CK; Center for the Neurobiology of Language Recovery, Northwestern University, Evanston, IL, USA.
  • Rapp B; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Parrish TB; Center for the Neurobiology of Language Recovery, Northwestern University, Evanston, IL, USA.
Sci Rep ; 11(1): 8419, 2021 04 19.
Article en En | MEDLINE | ID: mdl-33875733
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Afasia / Imagen por Resonancia Magnética / Accidente Cerebrovascular / Terapia del Lenguaje Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Afasia / Imagen por Resonancia Magnética / Accidente Cerebrovascular / Terapia del Lenguaje Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido