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Towards model-based control of Parkinson's disease.

Schiff SJ - Philos Trans A Math Phys Eng Sci (2010)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural Engineering, Department of Neurosurgery, Pennsylvania State University, University Park, PA 16802, USA. sschiff@psu.edu

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.

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Exploration of the parameter space of period and amplitude focusing on reliability. (a,b) Normal; (c,d) Parkinson’s disease (PD); and (e,f) DBS in PD. (g) Optimization of reliability (Rel) as a function of period and amplitude. (h) Slower DBS frequency at 80 ms reliability peak. (Adapted from Feng et al. (2007a).)
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RSTA20100050F11: Exploration of the parameter space of period and amplitude focusing on reliability. (a,b) Normal; (c,d) Parkinson’s disease (PD); and (e,f) DBS in PD. (g) Optimization of reliability (Rel) as a function of period and amplitude. (h) Slower DBS frequency at 80 ms reliability peak. (Adapted from Feng et al. (2007a).)

Mentions: So, starting from a sparse structured network, with two TC cells, they replicate the fundamental findings of Rubin & Terman (2004) in figure 11a–f.


Towards model-based control of Parkinson's disease.

Schiff SJ - Philos Trans A Math Phys Eng Sci (2010)

Exploration of the parameter space of period and amplitude focusing on reliability. (a,b) Normal; (c,d) Parkinson’s disease (PD); and (e,f) DBS in PD. (g) Optimization of reliability (Rel) as a function of period and amplitude. (h) Slower DBS frequency at 80 ms reliability peak. (Adapted from Feng et al. (2007a).)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2944387&req=5

RSTA20100050F11: Exploration of the parameter space of period and amplitude focusing on reliability. (a,b) Normal; (c,d) Parkinson’s disease (PD); and (e,f) DBS in PD. (g) Optimization of reliability (Rel) as a function of period and amplitude. (h) Slower DBS frequency at 80 ms reliability peak. (Adapted from Feng et al. (2007a).)
Mentions: So, starting from a sparse structured network, with two TC cells, they replicate the fundamental findings of Rubin & Terman (2004) in figure 11a–f.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural Engineering, Department of Neurosurgery, Pennsylvania State University, University Park, PA 16802, USA. sschiff@psu.edu

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.

Show MeSH
Related in: MedlinePlus