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Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model

BACKGROUND: Impaired postural control in Parkinson’s disease (PD) seriously compromises life quality. Although balance training improves mobility and postural stability, lack of quantitative studies on the neurophysiological mechanisms of balance training in PD impedes the development of patient-spe...

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Autores principales: Rahmati, Zahra, Schouten, Alfred C., Behzadipour, Saeed, Taghizadeh, Ghorban, Firoozbakhsh, Keikhosrow
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694532/
https://www.ncbi.nlm.nih.gov/pubmed/31412926
http://dx.doi.org/10.1186/s12984-019-0574-0
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author Rahmati, Zahra
Schouten, Alfred C.
Behzadipour, Saeed
Taghizadeh, Ghorban
Firoozbakhsh, Keikhosrow
author_facet Rahmati, Zahra
Schouten, Alfred C.
Behzadipour, Saeed
Taghizadeh, Ghorban
Firoozbakhsh, Keikhosrow
author_sort Rahmati, Zahra
collection PubMed
description BACKGROUND: Impaired postural control in Parkinson’s disease (PD) seriously compromises life quality. Although balance training improves mobility and postural stability, lack of quantitative studies on the neurophysiological mechanisms of balance training in PD impedes the development of patient-specific therapies. We evaluated the effects of a balance-training program using functional balance and mobility tests, posturography, and a postural control model. METHODS: Center-of-pressure (COP) data of 40 PD patients before and after a 12-session balance-training program, and 20 healthy control subjects were recorded in four conditions with two tasks on a rigid surface (R-tasks) and two on foam. A postural control model was fitted to describe the posturography data. The model comprises a neuromuscular controller, a time delay, and a gain scaling the internal disturbance torque. RESULTS: Patients’ axial rigidity before training resulted in slower COP velocity in R-tasks; which was reflected as lower internal torque gain. Furthermore, patients exhibited poor stability on foam, remarked by abnormal higher sway amplitude. Lower control parameters as well as higher time delay were responsible for patients’ abnormal high sway amplitude. Balance training improved all clinical scores on functional balance and mobility. Consistently, improved ‘flexibility’ appeared as enhanced sway velocity (increased internal torque gain). Balance training also helped patients to develop the ‘stability degree’ (increase control parameters), and to respond more quickly in unstable condition of stance on foam. CONCLUSIONS: Projection of the common posturography measures on a postural control model provided a quantitative framework for unraveling the neurophysiological factors and different recovery mechanisms in impaired postural control in PD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12984-019-0574-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-66945322019-08-19 Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model Rahmati, Zahra Schouten, Alfred C. Behzadipour, Saeed Taghizadeh, Ghorban Firoozbakhsh, Keikhosrow J Neuroeng Rehabil Research BACKGROUND: Impaired postural control in Parkinson’s disease (PD) seriously compromises life quality. Although balance training improves mobility and postural stability, lack of quantitative studies on the neurophysiological mechanisms of balance training in PD impedes the development of patient-specific therapies. We evaluated the effects of a balance-training program using functional balance and mobility tests, posturography, and a postural control model. METHODS: Center-of-pressure (COP) data of 40 PD patients before and after a 12-session balance-training program, and 20 healthy control subjects were recorded in four conditions with two tasks on a rigid surface (R-tasks) and two on foam. A postural control model was fitted to describe the posturography data. The model comprises a neuromuscular controller, a time delay, and a gain scaling the internal disturbance torque. RESULTS: Patients’ axial rigidity before training resulted in slower COP velocity in R-tasks; which was reflected as lower internal torque gain. Furthermore, patients exhibited poor stability on foam, remarked by abnormal higher sway amplitude. Lower control parameters as well as higher time delay were responsible for patients’ abnormal high sway amplitude. Balance training improved all clinical scores on functional balance and mobility. Consistently, improved ‘flexibility’ appeared as enhanced sway velocity (increased internal torque gain). Balance training also helped patients to develop the ‘stability degree’ (increase control parameters), and to respond more quickly in unstable condition of stance on foam. CONCLUSIONS: Projection of the common posturography measures on a postural control model provided a quantitative framework for unraveling the neurophysiological factors and different recovery mechanisms in impaired postural control in PD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12984-019-0574-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-14 /pmc/articles/PMC6694532/ /pubmed/31412926 http://dx.doi.org/10.1186/s12984-019-0574-0 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Rahmati, Zahra
Schouten, Alfred C.
Behzadipour, Saeed
Taghizadeh, Ghorban
Firoozbakhsh, Keikhosrow
Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model
title Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model
title_full Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model
title_fullStr Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model
title_full_unstemmed Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model
title_short Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model
title_sort disentangling stability and flexibility degrees in parkinson’s disease using a computational postural control model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694532/
https://www.ncbi.nlm.nih.gov/pubmed/31412926
http://dx.doi.org/10.1186/s12984-019-0574-0
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