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Published Online: 1 July 2011

A Parameter Selection for Differentiating Between Healthy and Parkinsonian Gait Through Modeling Parkinson's Disease From a Chaotic Viewpoint

Publication: The Journal of Neuropsychiatry and Clinical Neurosciences
To the Editor: Parkinson's disease (PD) is a neurodegenerative disease that is the most common disease after Alzheimer's disease among neurological diseases. Destruction of the substantia nigra pars compacta of basal ganglia (BG) is the cause of the disease. The exact cause of this destruction is not yet known.1 Gait disorder is one of the cardinal symptoms of PD. The gait disorder in PD patients includes slowed gait, shortened length of stride, decreased rhythm and cadence, increased time of double support in the stance phase, shuffling and festinating gait, decreased swing of the arms, and disturbed regulation of the stride length. Five-minute walking can exhibit the disturbances in patients as compared with normal persons.1,2 Researchers have focused from different perspectives on PD, some of which are: introducing animal models, introducing conceptual models, presenting cognitive symptoms, and finding novel treatments such as drugs and deep brain stimulation (DBS).35 As studies shown, we can understand two main points about the brain and PD:
1. 
The chaotic nature of the brain that has been verified through regional recordings that have been acquired from the brain.
2. 
The main disorders in the movements of PD patients originate in dysfunction of the basal ganglia.
Considering the two facts, we propose to model the basal ganglia, using a black-box model that is obtained from one of the well-known chaotic relations so called “sin Circle Map.” We tried to model the disease using the recurrent relation (sin Circle Map) adding some other parameters to the equation, to increase its capability to model the disease. We used the recordings available in the Physionet6 to obtain the model parameters for each of the recordings. Our primary unpublished simulations showed that the range of variability of Omega (Ω) is considerably different in the two groups (healthy versus parkinsonian). Omega (Ω) is the main parameter in the model, and the rest of the system can be based on it.7 Therefore, we think that this parameter is the main factor for the model to show the state of the system (BG), and it can be used for proposing treatments for the disease.

References

1.
Factor SA, Weiner WJ: Parkinson's Disease Diagnosis and Clinical Management, 2nd Edition, New York, Demos Medical Publishing, 2008
2.
Murray MP: Studies of normal and abnormal locomotion. Int J Rehabil Res 1979; 2:510–511
3.
Volpato C, Signorini M, Meneghello F, et al.: Cognitive and personality features in Parkinson disease: two sides of the same coin? Cogn Behav Neurol 2009; 22:258–263
4.
Henry V, Paille V, Lelan F, et al.: Kinetics of microglial activation and degeneration of dopamine-containing neurons in a rat model of Parkinson disease induced by 6-hydroxydopamine. J Neuropathol Exp Neurol 2009; 68:1092–1102
5.
Baev KV: A new conceptual understanding of brain function: basic mechanisms of brain-initiated normal and pathological behaviors. Crit Rev Neurobiol 2007; 19:119–202
7.
Hilborn RC: Chaos and Nonlinear Dynamic, 2nd Edition. New York, Oxford University Press, 2000

Information & Authors

Information

Published In

Go to The Journal of Neuropsychiatry and Clinical Neurosciences
Go to The Journal of Neuropsychiatry and Clinical Neurosciences
The Journal of Neuropsychiatry and Clinical Neurosciences
Pages: E22
PubMed: 21948912

History

Published online: 1 July 2011
Published in print: Summer 2011

Authors

Affiliations

Masood Banaie
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, IranBiomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
Mohammad Pooyan
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, IranBiomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
Yashar Sarbaz
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, IranBiomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
Shahriar Gharibzadeh
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, IranBiomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
Farzad Towhidkhah
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, IranBiomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran

Notes

correspondence: [email protected]

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