Cargando…

DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology

Tracking and predicting motor outcomes is important in determining effective stroke rehabilitation strategies. Diffusion tensor imaging (DTI) allows for evaluation of the underlying structural integrity of brain white matter tracts and may serve as a potential biomarker for tracking and predicting m...

Descripción completa

Detalles Bibliográficos
Autores principales: Song, Jie, Nair, Veena A., Young, Brittany M., Walton, Leo M., Nigogosyan, Zack, Remsik, Alexander, Tyler, Mitchell E., Farrar-Edwards, Dorothy, Caldera, Kristin E., Sattin, Justin A., Williams, Justin C., Prabhakaran, Vivek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410488/
https://www.ncbi.nlm.nih.gov/pubmed/25964753
http://dx.doi.org/10.3389/fnhum.2015.00195
_version_ 1782368334880178176
author Song, Jie
Nair, Veena A.
Young, Brittany M.
Walton, Leo M.
Nigogosyan, Zack
Remsik, Alexander
Tyler, Mitchell E.
Farrar-Edwards, Dorothy
Caldera, Kristin E.
Sattin, Justin A.
Williams, Justin C.
Prabhakaran, Vivek
author_facet Song, Jie
Nair, Veena A.
Young, Brittany M.
Walton, Leo M.
Nigogosyan, Zack
Remsik, Alexander
Tyler, Mitchell E.
Farrar-Edwards, Dorothy
Caldera, Kristin E.
Sattin, Justin A.
Williams, Justin C.
Prabhakaran, Vivek
author_sort Song, Jie
collection PubMed
description Tracking and predicting motor outcomes is important in determining effective stroke rehabilitation strategies. Diffusion tensor imaging (DTI) allows for evaluation of the underlying structural integrity of brain white matter tracts and may serve as a potential biomarker for tracking and predicting motor recovery. In this study, we examined the longitudinal relationship between DTI measures of the posterior limb of the internal capsule (PLIC) and upper-limb motor outcomes in 13 stroke patients (median 20-month post-stroke) who completed up to 15 sessions of intervention using brain–computer interface (BCI) technology. Patients’ upper-limb motor outcomes and PLIC DTI measures including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were assessed longitudinally at four time points: pre-, mid-, immediately post- and 1-month-post intervention. DTI measures and ratios of each DTI measure comparing the ipsilesional and contralesional PLIC were correlated with patients’ motor outcomes to examine the relationship between structural integrity of the PLIC and patients’ motor recovery. We found that lower diffusivity and higher FA values of the ipsilesional PLIC were significantly correlated with better upper-limb motor function. Baseline DTI ratios were significantly correlated with motor outcomes measured immediately post and 1-month-post BCI interventions. A few patients achieved improvements in motor recovery meeting the minimum clinically important difference (MCID). These findings suggest that upper-limb motor recovery in stroke patients receiving BCI interventions relates to the microstructural status of the PLIC. Lower diffusivity and higher FA measures of the ipsilesional PLIC contribute toward better motor recovery in the stroke-affected upper-limb. DTI-derived measures may be a clinically useful biomarker in tracking and predicting motor recovery in stroke patients receiving BCI interventions.
format Online
Article
Text
id pubmed-4410488
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-44104882015-05-11 DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology Song, Jie Nair, Veena A. Young, Brittany M. Walton, Leo M. Nigogosyan, Zack Remsik, Alexander Tyler, Mitchell E. Farrar-Edwards, Dorothy Caldera, Kristin E. Sattin, Justin A. Williams, Justin C. Prabhakaran, Vivek Front Hum Neurosci Neuroscience Tracking and predicting motor outcomes is important in determining effective stroke rehabilitation strategies. Diffusion tensor imaging (DTI) allows for evaluation of the underlying structural integrity of brain white matter tracts and may serve as a potential biomarker for tracking and predicting motor recovery. In this study, we examined the longitudinal relationship between DTI measures of the posterior limb of the internal capsule (PLIC) and upper-limb motor outcomes in 13 stroke patients (median 20-month post-stroke) who completed up to 15 sessions of intervention using brain–computer interface (BCI) technology. Patients’ upper-limb motor outcomes and PLIC DTI measures including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were assessed longitudinally at four time points: pre-, mid-, immediately post- and 1-month-post intervention. DTI measures and ratios of each DTI measure comparing the ipsilesional and contralesional PLIC were correlated with patients’ motor outcomes to examine the relationship between structural integrity of the PLIC and patients’ motor recovery. We found that lower diffusivity and higher FA values of the ipsilesional PLIC were significantly correlated with better upper-limb motor function. Baseline DTI ratios were significantly correlated with motor outcomes measured immediately post and 1-month-post BCI interventions. A few patients achieved improvements in motor recovery meeting the minimum clinically important difference (MCID). These findings suggest that upper-limb motor recovery in stroke patients receiving BCI interventions relates to the microstructural status of the PLIC. Lower diffusivity and higher FA measures of the ipsilesional PLIC contribute toward better motor recovery in the stroke-affected upper-limb. DTI-derived measures may be a clinically useful biomarker in tracking and predicting motor recovery in stroke patients receiving BCI interventions. Frontiers Media S.A. 2015-04-27 /pmc/articles/PMC4410488/ /pubmed/25964753 http://dx.doi.org/10.3389/fnhum.2015.00195 Text en Copyright © 2015 Song, Nair, Young, Walton, Nigogosyan, Remsik, Tyler, Farrar-Edwards, Caldera, Sattin, Williams and Prabhakaran. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Song, Jie
Nair, Veena A.
Young, Brittany M.
Walton, Leo M.
Nigogosyan, Zack
Remsik, Alexander
Tyler, Mitchell E.
Farrar-Edwards, Dorothy
Caldera, Kristin E.
Sattin, Justin A.
Williams, Justin C.
Prabhakaran, Vivek
DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology
title DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology
title_full DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology
title_fullStr DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology
title_full_unstemmed DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology
title_short DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology
title_sort dti measures track and predict motor function outcomes in stroke rehabilitation utilizing bci technology
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410488/
https://www.ncbi.nlm.nih.gov/pubmed/25964753
http://dx.doi.org/10.3389/fnhum.2015.00195
work_keys_str_mv AT songjie dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT nairveenaa dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT youngbrittanym dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT waltonleom dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT nigogosyanzack dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT remsikalexander dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT tylermitchelle dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT farraredwardsdorothy dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT calderakristine dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT sattinjustina dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT williamsjustinc dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology
AT prabhakaranvivek dtimeasurestrackandpredictmotorfunctionoutcomesinstrokerehabilitationutilizingbcitechnology