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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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Control of TC-reduced-cell model using reliability as a control parameter. (a) A threshold of turning on GPi stimulation when reliability less than 0.9 is shown. (b) A different strategy, using an inverse approach is shown. In (b) control is turned on when reliability is greater than 0.5. Note that the relevant reliability in both examples is the controlled (red) piecewise continuous line in (i) (the blue reliability line is the uncontrolled state shown for comparison). The inherent delays in employing the moving average of reliability can be exploited so that inverse reliability control can be more reliable than using a more intuitive strategy based on turning on stimulation when reliability falls.
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RSTA20100050F18: Control of TC-reduced-cell model using reliability as a control parameter. (a) A threshold of turning on GPi stimulation when reliability less than 0.9 is shown. (b) A different strategy, using an inverse approach is shown. In (b) control is turned on when reliability is greater than 0.5. Note that the relevant reliability in both examples is the controlled (red) piecewise continuous line in (i) (the blue reliability line is the uncontrolled state shown for comparison). The inherent delays in employing the moving average of reliability can be exploited so that inverse reliability control can be more reliable than using a more intuitive strategy based on turning on stimulation when reliability falls.

Mentions: As reliability is our goal, and as we are estimating it, why not use it as the control parameter? Recognize that the way I have been calculating TC-cell reliability in figures 14–17 employed a moving average of relatively infrequent events (the incoming spikes are on a time scale significantly slower than membrane dynamics such as w). So, this formulation of reliability is substantially delayed with respect to the dynamics of the system. This delay creates the type of results seen in figure 18a for control based upon turning the stimulator on when estimated reliability is too low.


Towards model-based control of Parkinson's disease.

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

Control of TC-reduced-cell model using reliability as a control parameter. (a) A threshold of turning on GPi stimulation when reliability less than 0.9 is shown. (b) A different strategy, using an inverse approach is shown. In (b) control is turned on when reliability is greater than 0.5. Note that the relevant reliability in both examples is the controlled (red) piecewise continuous line in (i) (the blue reliability line is the uncontrolled state shown for comparison). The inherent delays in employing the moving average of reliability can be exploited so that inverse reliability control can be more reliable than using a more intuitive strategy based on turning on stimulation when reliability falls.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F18: Control of TC-reduced-cell model using reliability as a control parameter. (a) A threshold of turning on GPi stimulation when reliability less than 0.9 is shown. (b) A different strategy, using an inverse approach is shown. In (b) control is turned on when reliability is greater than 0.5. Note that the relevant reliability in both examples is the controlled (red) piecewise continuous line in (i) (the blue reliability line is the uncontrolled state shown for comparison). The inherent delays in employing the moving average of reliability can be exploited so that inverse reliability control can be more reliable than using a more intuitive strategy based on turning on stimulation when reliability falls.
Mentions: As reliability is our goal, and as we are estimating it, why not use it as the control parameter? Recognize that the way I have been calculating TC-cell reliability in figures 14–17 employed a moving average of relatively infrequent events (the incoming spikes are on a time scale significantly slower than membrane dynamics such as w). So, this formulation of reliability is substantially delayed with respect to the dynamics of the system. This delay creates the type of results seen in figure 18a for control based upon turning the stimulator on when estimated reliability is too low.

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