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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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Nullclines for w (thick line) and v (dotted line). (a) With increased GPi inhibition onto the reduced TC cell, the v cline is elevated with respect to the w cline. (b) Reconstruction estimates of the clines from noisy measurements.
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RSTA20100050F14: Nullclines for w (thick line) and v (dotted line). (a) With increased GPi inhibition onto the reduced TC cell, the v cline is elevated with respect to the w cline. (b) Reconstruction estimates of the clines from noisy measurements.

Mentions: It is tempting to examine and consider control from the clines. There is an extensive literature in control theory on what is generally termed variable structure control. First described by Utkin in the 1970s (Utkin 1977), this control law strategy is now more commonly referred to as sliding-mode control (DeCarlo et al. 1988; Young et al. 1999). We could use such clines to generate control functions that we seek to target the system towards. In figure 14a, we see the actual cline intersection of our simulation of the reduced TC cell as the GPi current switches from off to on (refer to figure 9). The w cline does not change here as the GPi input fluctuates. In figure 14b, I show an estimation of these clines from a reconstruction of these curves using a UKF. There is a great deal of uncertainty in the v cline. So, estimating a control surface in this phase space is not a trivial problem that we know how to solve at present.


Towards model-based control of Parkinson's disease.

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

Nullclines for w (thick line) and v (dotted line). (a) With increased GPi inhibition onto the reduced TC cell, the v cline is elevated with respect to the w cline. (b) Reconstruction estimates of the clines from noisy measurements.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F14: Nullclines for w (thick line) and v (dotted line). (a) With increased GPi inhibition onto the reduced TC cell, the v cline is elevated with respect to the w cline. (b) Reconstruction estimates of the clines from noisy measurements.
Mentions: It is tempting to examine and consider control from the clines. There is an extensive literature in control theory on what is generally termed variable structure control. First described by Utkin in the 1970s (Utkin 1977), this control law strategy is now more commonly referred to as sliding-mode control (DeCarlo et al. 1988; Young et al. 1999). We could use such clines to generate control functions that we seek to target the system towards. In figure 14a, we see the actual cline intersection of our simulation of the reduced TC cell as the GPi current switches from off to on (refer to figure 9). The w cline does not change here as the GPi input fluctuates. In figure 14b, I show an estimation of these clines from a reconstruction of these curves using a UKF. There is a great deal of uncertainty in the v cline. So, estimating a control surface in this phase space is not a trivial problem that we know how to solve at present.

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