System identification and modeling approach to characterizing rigidity in parkinson's disease

Neural and non-neural contributions

Ruiping Xia, Matija Radovic, Joseph Threlkeld, Zhi Hong Mao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Rigidity (muscle stiffness) is one of the most disabling symptoms in Parkinson's disease (PD). It is clinically defined as an increased resistance to passive movement of a joint. There is a fundamental gap between mechanistic and applied approaches to understanding this symptom. The objective of the current study was to apply a system identification and modeling approach to differentiating the contributions of neural (enhanced muscle reflex) and non-neural (altered mechanical properties of muscle fibers) factors to rigidity. Six patients participated in the study. The wrist joint torque and muscle activities of the wrist muscles were measured during externally induced movements. Each subject was tested in the Off- and Onmedication states. System identification and modeling approach was applied to separate the neural from the nonneural component with respect to the overall stiffness. Results show that both factors are responsible for rigidity in PD. Neural-related reflex component is the predominant factor in overall rigidity. Medication therapy decreased the level of reflex component to overall rigidity.

Original languageEnglish
Title of host publication2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
DOIs
StatePublished - 2010
Event4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 - Chengdu, China
Duration: Jun 18 2010Jun 20 2010

Other

Other4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
CountryChina
CityChengdu
Period6/18/106/20/10

Fingerprint

Rigidity
Parkinson Disease
Muscle
Identification (control systems)
Muscles
Reflex
Muscle Rigidity
Wrist Joint
Abnormal Reflexes
Stiffness
Torque
Wrist
Joints
Mechanical properties
Fibers
Therapeutics

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

Cite this

Xia, R., Radovic, M., Threlkeld, J., & Mao, Z. H. (2010). System identification and modeling approach to characterizing rigidity in parkinson's disease: Neural and non-neural contributions. In 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 [5514861] https://doi.org/10.1109/ICBBE.2010.5514861

System identification and modeling approach to characterizing rigidity in parkinson's disease : Neural and non-neural contributions. / Xia, Ruiping; Radovic, Matija; Threlkeld, Joseph; Mao, Zhi Hong.

2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010. 2010. 5514861.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Xia, R, Radovic, M, Threlkeld, J & Mao, ZH 2010, System identification and modeling approach to characterizing rigidity in parkinson's disease: Neural and non-neural contributions. in 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010., 5514861, 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010, Chengdu, China, 6/18/10. https://doi.org/10.1109/ICBBE.2010.5514861
Xia R, Radovic M, Threlkeld J, Mao ZH. System identification and modeling approach to characterizing rigidity in parkinson's disease: Neural and non-neural contributions. In 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010. 2010. 5514861 https://doi.org/10.1109/ICBBE.2010.5514861
Xia, Ruiping ; Radovic, Matija ; Threlkeld, Joseph ; Mao, Zhi Hong. / System identification and modeling approach to characterizing rigidity in parkinson's disease : Neural and non-neural contributions. 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010. 2010.
@inproceedings{69a6383eaa4f472c9c5f9570bd17bb66,
title = "System identification and modeling approach to characterizing rigidity in parkinson's disease: Neural and non-neural contributions",
abstract = "Rigidity (muscle stiffness) is one of the most disabling symptoms in Parkinson's disease (PD). It is clinically defined as an increased resistance to passive movement of a joint. There is a fundamental gap between mechanistic and applied approaches to understanding this symptom. The objective of the current study was to apply a system identification and modeling approach to differentiating the contributions of neural (enhanced muscle reflex) and non-neural (altered mechanical properties of muscle fibers) factors to rigidity. Six patients participated in the study. The wrist joint torque and muscle activities of the wrist muscles were measured during externally induced movements. Each subject was tested in the Off- and Onmedication states. System identification and modeling approach was applied to separate the neural from the nonneural component with respect to the overall stiffness. Results show that both factors are responsible for rigidity in PD. Neural-related reflex component is the predominant factor in overall rigidity. Medication therapy decreased the level of reflex component to overall rigidity.",
author = "Ruiping Xia and Matija Radovic and Joseph Threlkeld and Mao, {Zhi Hong}",
year = "2010",
doi = "10.1109/ICBBE.2010.5514861",
language = "English",
isbn = "9781424447138",
booktitle = "2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010",

}

TY - GEN

T1 - System identification and modeling approach to characterizing rigidity in parkinson's disease

T2 - Neural and non-neural contributions

AU - Xia, Ruiping

AU - Radovic, Matija

AU - Threlkeld, Joseph

AU - Mao, Zhi Hong

PY - 2010

Y1 - 2010

N2 - Rigidity (muscle stiffness) is one of the most disabling symptoms in Parkinson's disease (PD). It is clinically defined as an increased resistance to passive movement of a joint. There is a fundamental gap between mechanistic and applied approaches to understanding this symptom. The objective of the current study was to apply a system identification and modeling approach to differentiating the contributions of neural (enhanced muscle reflex) and non-neural (altered mechanical properties of muscle fibers) factors to rigidity. Six patients participated in the study. The wrist joint torque and muscle activities of the wrist muscles were measured during externally induced movements. Each subject was tested in the Off- and Onmedication states. System identification and modeling approach was applied to separate the neural from the nonneural component with respect to the overall stiffness. Results show that both factors are responsible for rigidity in PD. Neural-related reflex component is the predominant factor in overall rigidity. Medication therapy decreased the level of reflex component to overall rigidity.

AB - Rigidity (muscle stiffness) is one of the most disabling symptoms in Parkinson's disease (PD). It is clinically defined as an increased resistance to passive movement of a joint. There is a fundamental gap between mechanistic and applied approaches to understanding this symptom. The objective of the current study was to apply a system identification and modeling approach to differentiating the contributions of neural (enhanced muscle reflex) and non-neural (altered mechanical properties of muscle fibers) factors to rigidity. Six patients participated in the study. The wrist joint torque and muscle activities of the wrist muscles were measured during externally induced movements. Each subject was tested in the Off- and Onmedication states. System identification and modeling approach was applied to separate the neural from the nonneural component with respect to the overall stiffness. Results show that both factors are responsible for rigidity in PD. Neural-related reflex component is the predominant factor in overall rigidity. Medication therapy decreased the level of reflex component to overall rigidity.

UR - http://www.scopus.com/inward/record.url?scp=77956167834&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956167834&partnerID=8YFLogxK

U2 - 10.1109/ICBBE.2010.5514861

DO - 10.1109/ICBBE.2010.5514861

M3 - Conference contribution

SN - 9781424447138

BT - 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010

ER -