Disentangling stability and flexibility degrees in Parkinson's disease using a computational postural control model.
Identifieur interne : 000278 ( Main/Exploration ); précédent : 000277; suivant : 000279Disentangling stability and flexibility degrees in Parkinson's disease using a computational postural control model.
Auteurs : Zahra Rahmati [Iran] ; Alfred C. Schouten [Pays-Bas] ; Saeed Behzadipour [Iran] ; Ghorban Taghizadeh [Iran] ; Keikhosrow Firoozbakhsh [Iran]Source :
- Journal of neuroengineering and rehabilitation [ 1743-0003 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- physiologie : Équilibre postural.
- physiopathologie : Maladie de Parkinson.
- rééducation et réadaptation : Maladie de Parkinson.
- complications : Adulte d'âge moyen, Femelle, Humains, Maladie de Parkinson, Mâle, Simulation numérique, Sujet âgé.
English descriptors
- KwdEn :
- MESH :
- complications : Parkinson Disease.
- physiology : Postural Balance.
- physiopathology : Parkinson Disease.
- rehabilitation : Parkinson Disease.
- Aged, Computer Simulation, Female, Humans, Male, Middle Aged.
Abstract
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.
DOI: 10.1186/s12984-019-0574-0
PubMed: 31412926
PubMed Central: PMC6694532
Affiliations:
Links toward previous steps (curation, corpus...)
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Schouten</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.</nlm:affiliation><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Department of Biomechanical Engineering, Delft University of Technology, Delft</wicri:regionArea><wicri:noRegion>Delft</wicri:noRegion></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands.</nlm:affiliation><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Department of Biomechanical Engineering, University of Twente, Enschede</wicri:regionArea><orgName type="university">Université de Twente</orgName><placeName><settlement type="city">Enschede</settlement><region nuts="2">Overijssel</region></placeName></affiliation></author><author><name sortKey="Behzadipour, Saeed" sort="Behzadipour, Saeed" uniqKey="Behzadipour S" first="Saeed" last="Behzadipour">Saeed Behzadipour</name><affiliation wicri:level="1"><nlm:affiliation>Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran. behzadipour@sharif.edu.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Mechanical Engineering Department, Sharif University of Technology, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran, Iran. behzadipour@sharif.edu.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Taghizadeh, Ghorban" sort="Taghizadeh, Ghorban" uniqKey="Taghizadeh G" first="Ghorban" last="Taghizadeh">Ghorban Taghizadeh</name><affiliation wicri:level="1"><nlm:affiliation>School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Firoozbakhsh, Keikhosrow" sort="Firoozbakhsh, Keikhosrow" uniqKey="Firoozbakhsh K" first="Keikhosrow" last="Firoozbakhsh">Keikhosrow Firoozbakhsh</name><affiliation wicri:level="1"><nlm:affiliation>Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Mechanical Engineering Department, Sharif University of Technology, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of neuroengineering and rehabilitation</title><idno type="eISSN">1743-0003</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged (MeSH)</term><term>Computer Simulation (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Parkinson Disease (complications)</term><term>Parkinson Disease (physiopathology)</term><term>Parkinson Disease (rehabilitation)</term><term>Postural Balance (physiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte d'âge moyen (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Maladie de Parkinson (complications)</term><term>Maladie de Parkinson (physiopathologie)</term><term>Maladie de Parkinson (rééducation et réadaptation)</term><term>Mâle (MeSH)</term><term>Simulation numérique (MeSH)</term><term>Sujet âgé (MeSH)</term><term>Équilibre postural (physiologie)</term></keywords><keywords scheme="MESH" qualifier="complications" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Équilibre postural</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Postural Balance</term></keywords><keywords scheme="MESH" qualifier="physiopathologie" xml:lang="fr"><term>Maladie de Parkinson</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="rééducation et réadaptation" xml:lang="fr"><term>Maladie de Parkinson</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Computer Simulation</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term></keywords><keywords scheme="MESH" qualifier="complications" xml:lang="fr"><term>Adulte d'âge moyen</term><term>Femelle</term><term>Humains</term><term>Maladie de Parkinson</term><term>Mâle</term><term>Simulation numérique</term><term>Sujet âgé</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>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.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>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.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>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.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>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.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">31412926</PMID><DateCompleted><Year>2020</Year><Month>06</Month><Day>30</Day></DateCompleted><DateRevised><Year>2020</Year><Month>06</Month><Day>30</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1743-0003</ISSN><JournalIssue CitedMedium="Internet"><Volume>16</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>08</Month><Day>14</Day></PubDate></JournalIssue><Title>Journal of neuroengineering and rehabilitation</Title><ISOAbbreviation>J Neuroeng Rehabil</ISOAbbreviation></Journal><ArticleTitle>Disentangling stability and flexibility degrees in Parkinson's disease using a computational postural control model.</ArticleTitle><Pagination><MedlinePgn>104</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12984-019-0574-0</ELocationID><Abstract><AbstractText Label="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.</AbstractText><AbstractText Label="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.</AbstractText><AbstractText Label="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.</AbstractText><AbstractText Label="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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rahmati</LastName><ForeName>Zahra</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schouten</LastName><ForeName>Alfred C</ForeName><Initials>AC</Initials><AffiliationInfo><Affiliation>Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Behzadipour</LastName><ForeName>Saeed</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0001-9313-315X</Identifier><AffiliationInfo><Affiliation>Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran. behzadipour@sharif.edu.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran, Iran. behzadipour@sharif.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Taghizadeh</LastName><ForeName>Ghorban</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Firoozbakhsh</LastName><ForeName>Keikhosrow</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>08</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Neuroeng Rehabil</MedlineTA><NlmUniqueID>101232233</NlmUniqueID><ISSNLinking>1743-0003</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="Y">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010300" MajorTopicYN="N">Parkinson Disease</DescriptorName><QualifierName UI="Q000150" MajorTopicYN="N">complications</QualifierName><QualifierName UI="Q000503" MajorTopicYN="Y">physiopathology</QualifierName><QualifierName UI="Q000534" MajorTopicYN="Y">rehabilitation</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004856" MajorTopicYN="N">Postural Balance</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Balance training</Keyword><Keyword MajorTopicYN="Y">Parkinson’s disease</Keyword><Keyword MajorTopicYN="Y">Postural control model</Keyword><Keyword MajorTopicYN="Y">Posturography</Keyword><Keyword MajorTopicYN="Y">Power spectral density</Keyword><Keyword MajorTopicYN="Y">Stability and flexibility degrees</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>02</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>08</Month><Day>07</Day></PubMedPubDate><PubMedPubDate 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