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Clinical and Neural Predictors of Treatment Response to Music Listening Intervention after Stroke

Patients with post-stroke impairments present often significant variation in response to therapeutic interventions. Recent studies have shown that daily music listening can aid post-stroke recovery of language and memory, but reliable predictors of treatment response are unknown. Utilizing data from...

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Detalles Bibliográficos
Autores principales: Sihvonen, Aleksi J., Särkämö, Teppo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699822/
https://www.ncbi.nlm.nih.gov/pubmed/34942878
http://dx.doi.org/10.3390/brainsci11121576
Descripción
Sumario:Patients with post-stroke impairments present often significant variation in response to therapeutic interventions. Recent studies have shown that daily music listening can aid post-stroke recovery of language and memory, but reliable predictors of treatment response are unknown. Utilizing data from the music intervention arms of a single-blind randomized controlled trial (RCT) on stroke patients (N = 31), we built regression models to predict the treatment response of a two-month music listening intervention on language skills and verbal memory with baseline demographic, clinical and musical data as well as fMRI data from a music listening task. Clinically, greater improvement in verbal memory and language skills after the music listening intervention were predicted by the severity of the initial deficit and educational level. Neurally, greater baseline fMRI activation during vocal music listening in the left parietal cortical and medial frontal areas predicted greater treatment-induced improvement in language skills and greater baseline engagement of the auditory network during instrumental music listening predicted improvement in both verbal memory and language skills. Our results suggest that clinical, demographic, and neuroimaging data predicts music listening treatment response. This data could be used clinically to target music-based treatments.