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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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Related in: MedlinePlus

Schematic of Terman et al. (2002) suggesting how an increase in striatal input and decrease in GPe internal connections would generate the oscillations of a Parkinsonian state. (Adapted from Terman et al. (2002).)
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RSTA20100050F5: Schematic of Terman et al. (2002) suggesting how an increase in striatal input and decrease in GPe internal connections would generate the oscillations of a Parkinsonian state. (Adapted from Terman et al. (2002).)

Mentions: Strong oscillations emerge in the GPe–STN network in Parkinson’s disease and in dopamine depletion in experimental animals. Nevertheless, there is a body of experimental evidence, in both human patients and MPTP primates (see Terman et al. 2002), that fails to find the sort of highly correlated and synchronized firing that would support the coherent waves predicted in the most structured networks of figure 4. The picture emerging from this work is that, among the more sparse networks, the conversion from normal to Parkinsonian dynamics fits well with the schematic in figure 5. This schematic illustrates that, following a loss of dopamine input to the striatum, a strengthening of striatal input to the GPe, perhaps with a concurrent weakening of recurring inhibitory connections within the GPe, could create a Parkinsonian state.


Towards model-based control of Parkinson's disease.

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

Schematic of Terman et al. (2002) suggesting how an increase in striatal input and decrease in GPe internal connections would generate the oscillations of a Parkinsonian state. (Adapted from Terman et al. (2002).)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F5: Schematic of Terman et al. (2002) suggesting how an increase in striatal input and decrease in GPe internal connections would generate the oscillations of a Parkinsonian state. (Adapted from Terman et al. (2002).)
Mentions: Strong oscillations emerge in the GPe–STN network in Parkinson’s disease and in dopamine depletion in experimental animals. Nevertheless, there is a body of experimental evidence, in both human patients and MPTP primates (see Terman et al. 2002), that fails to find the sort of highly correlated and synchronized firing that would support the coherent waves predicted in the most structured networks of figure 4. The picture emerging from this work is that, among the more sparse networks, the conversion from normal to Parkinsonian dynamics fits well with the schematic in figure 5. This schematic illustrates that, following a loss of dopamine input to the striatum, a strengthening of striatal input to the GPe, perhaps with a concurrent weakening of recurring inhibitory connections within the GPe, could create a Parkinsonian state.

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