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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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Normal response of the TC cell to periodic sensorimotor stimulation. (a) Voltage in reduced TC-cell model (thin black line), with substantial added noise to serve as a noisy observable (blue markers), and (b) sensorimotor input (red pulses). Each time a sensorimotor pulse is reliably transmitted, a marker (green) is placed above the successfully transmitted spike. This cell is 100% reliable.
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RSTA20100050F10: Normal response of the TC cell to periodic sensorimotor stimulation. (a) Voltage in reduced TC-cell model (thin black line), with substantial added noise to serve as a noisy observable (blue markers), and (b) sensorimotor input (red pulses). Each time a sensorimotor pulse is reliably transmitted, a marker (green) is placed above the successfully transmitted spike. This cell is 100% reliable.

Mentions: Now let us look deeper into these reduced dynamics in normal, DBS and Parkinsonian states. In figure 10, we see a normal state. The input to the reduced TC cell from the GPi is a modest constant, which represents a state where there is asynchrony among the GPi cells, and there results a modest steady level of inhibition to the TC cells. There is periodic sensorimotor stimulation represented by brief square-wave excitatory inputs (figure 10b). A substantial amount of random measurement noise is added to the actual TC voltage. These noisy measurements are what we record from in these models as our observable variable. Note that for each sensorimotor stimulus, a ‘spike’ is transmitted from the TC cell. This is reliable transmission (100% reliable in this case).


Towards model-based control of Parkinson's disease.

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

Normal response of the TC cell to periodic sensorimotor stimulation. (a) Voltage in reduced TC-cell model (thin black line), with substantial added noise to serve as a noisy observable (blue markers), and (b) sensorimotor input (red pulses). Each time a sensorimotor pulse is reliably transmitted, a marker (green) is placed above the successfully transmitted spike. This cell is 100% reliable.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F10: Normal response of the TC cell to periodic sensorimotor stimulation. (a) Voltage in reduced TC-cell model (thin black line), with substantial added noise to serve as a noisy observable (blue markers), and (b) sensorimotor input (red pulses). Each time a sensorimotor pulse is reliably transmitted, a marker (green) is placed above the successfully transmitted spike. This cell is 100% reliable.
Mentions: Now let us look deeper into these reduced dynamics in normal, DBS and Parkinsonian states. In figure 10, we see a normal state. The input to the reduced TC cell from the GPi is a modest constant, which represents a state where there is asynchrony among the GPi cells, and there results a modest steady level of inhibition to the TC cells. There is periodic sensorimotor stimulation represented by brief square-wave excitatory inputs (figure 10b). A substantial amount of random measurement noise is added to the actual TC voltage. These noisy measurements are what we record from in these models as our observable variable. Note that for each sensorimotor stimulus, a ‘spike’ is transmitted from the TC cell. This is reliable transmission (100% reliable in this case).

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