Towards model-based control of Parkinson's disease
Identifieur interne : 000236 ( Main/Exploration ); précédent : 000235; suivant : 000237Towards model-based control of Parkinson's disease
Auteurs : Steven J. SchiffSource :
- Philosophical transactions. Series A, Mathematical, physical, and engineering sciences [ 1364-503X ] ; 2010.
Abstract
Modern model-based control theory has led to transformative improvements in our ability to track the nonlinear dynamics of systems that we observe, and to engineer control systems of unprecedented efficacy. In parallel with these developments, our ability to build computational models to embody our expanding knowledge of the biophysics of neurons and their networks is maturing at a rapid rate. In the treatment of human dynamical disease, our employment of deep brain stimulators for the treatment of Parkinson’s disease is gaining increasing acceptance. Thus, the confluence of these three developments—control theory, computational neuroscience and deep brain stimulation—offers a unique opportunity to create novel approaches to the treatment of this disease. This paper explores the relevant state of the art of science, medicine and engineering, and proposes a strategy for model-based control of Parkinson’s disease. We present a set of preliminary calculations employing basal ganglia computational models, structured within an unscented Kalman filter for tracking observations and prescribing control. Based upon these findings, we will offer suggestions for future research and development.
Url:
DOI: 10.1098/rsta.2010.0050
PubMed: 20368246
PubMed Central: 2944387
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Towards model-based control of Parkinson's disease</title><author><name sortKey="Schiff, Steven J" sort="Schiff, Steven J" uniqKey="Schiff S" first="Steven J." last="Schiff">Steven J. Schiff</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">20368246</idno><idno type="pmc">2944387</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944387</idno><idno type="RBID">PMC:2944387</idno><idno type="doi">10.1098/rsta.2010.0050</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">000309</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000309</idno><idno type="wicri:Area/Pmc/Curation">000197</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000197</idno><idno type="wicri:Area/Pmc/Checkpoint">000143</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000143</idno><idno type="wicri:Area/Ncbi/Merge">000055</idno><idno type="wicri:Area/Ncbi/Curation">000055</idno><idno type="wicri:Area/Ncbi/Checkpoint">000055</idno><idno type="wicri:doubleKey">1364-503X:2010:Schiff S:towards:model:based</idno><idno type="wicri:Area/Main/Merge">000236</idno><idno type="wicri:Area/Main/Curation">000236</idno><idno type="wicri:Area/Main/Exploration">000236</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Towards model-based control of Parkinson's disease</title><author><name sortKey="Schiff, Steven J" sort="Schiff, Steven J" uniqKey="Schiff S" first="Steven J." last="Schiff">Steven J. Schiff</name></author></analytic><series><title level="j">Philosophical transactions. Series A, Mathematical, physical, and engineering sciences</title><idno type="ISSN">1364-503X</idno><idno type="eISSN">1471-2962</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Modern model-based control theory has led to transformative improvements in our ability to track the nonlinear dynamics of systems that we observe, and to engineer control systems of unprecedented efficacy. In parallel with these developments, our ability to build computational models to embody our expanding knowledge of the biophysics of neurons and their networks is maturing at a rapid rate. In the treatment of human dynamical disease, our employment of deep brain stimulators for the treatment of Parkinson’s disease is gaining increasing acceptance. Thus, the confluence of these three developments—control theory, computational neuroscience and deep brain stimulation—offers a unique opportunity to create novel approaches to the treatment of this disease. This paper explores the relevant state of the art of science, medicine and engineering, and proposes a strategy for model-based control of Parkinson’s disease. We present a set of preliminary calculations employing basal ganglia computational models, structured within an unscented Kalman filter for tracking observations and prescribing control. Based upon these findings, we will offer suggestions for future research and development.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Alberts, J L" uniqKey="Alberts J">J. L. Alberts</name></author><author><name sortKey="Voelcker Rehage, C" uniqKey="Voelcker Rehage C">C. Voelcker-Rehage</name></author><author><name sortKey="Hallahan, K" uniqKey="Hallahan K">K. 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