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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

An extremely simplified schematic of network imbalance in Parkinson’s disease. Excitation in red and inhibition in blue. The contrast with normal in the Parkinson’s disease state is shown on the right, where thickened (thinned) lines indicate an increase (decrease) in excitation (red) or inhibition (blue). St, striatum; GPe, globus pallidus externa; GPi, globus pallidus interna; Th, Thalamus; STN, subthalamic nucleus; and SN, substantia nigra. I have made no distinction between indirect and direct pathways, and customized this for the purposes of the discussion within this paper. For a more complete and detailed description of this anatomy, see Obeso et al. (2008).
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RSTA20100050F2: An extremely simplified schematic of network imbalance in Parkinson’s disease. Excitation in red and inhibition in blue. The contrast with normal in the Parkinson’s disease state is shown on the right, where thickened (thinned) lines indicate an increase (decrease) in excitation (red) or inhibition (blue). St, striatum; GPe, globus pallidus externa; GPi, globus pallidus interna; Th, Thalamus; STN, subthalamic nucleus; and SN, substantia nigra. I have made no distinction between indirect and direct pathways, and customized this for the purposes of the discussion within this paper. For a more complete and detailed description of this anatomy, see Obeso et al. (2008).

Mentions: In Parkinson’s disease, there is degeneration of neurons that use dopamine as a neurotransmitter, which have their cell bodies in the substantia nigra at the upper edge of the midbrain. The decrease in neural output from the substantia nigra causes a disturbance in the network balance of excitation and inhibition, as schematized in figure 2. The result is a net increase in inhibition from the GPi to thalamus (for a much more detailed discussion of the circuitry, see Obeso et al. (2008)). But the lines and arrows in these static diagrams refer to average firing rate or activity, and do not reflect the dynamics that is critical to understand what is happening. In Parkinson’s disease, the inhibition to the thalamus becomes phasic and oscillates.


Towards model-based control of Parkinson's disease.

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

An extremely simplified schematic of network imbalance in Parkinson’s disease. Excitation in red and inhibition in blue. The contrast with normal in the Parkinson’s disease state is shown on the right, where thickened (thinned) lines indicate an increase (decrease) in excitation (red) or inhibition (blue). St, striatum; GPe, globus pallidus externa; GPi, globus pallidus interna; Th, Thalamus; STN, subthalamic nucleus; and SN, substantia nigra. I have made no distinction between indirect and direct pathways, and customized this for the purposes of the discussion within this paper. For a more complete and detailed description of this anatomy, see Obeso et al. (2008).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F2: An extremely simplified schematic of network imbalance in Parkinson’s disease. Excitation in red and inhibition in blue. The contrast with normal in the Parkinson’s disease state is shown on the right, where thickened (thinned) lines indicate an increase (decrease) in excitation (red) or inhibition (blue). St, striatum; GPe, globus pallidus externa; GPi, globus pallidus interna; Th, Thalamus; STN, subthalamic nucleus; and SN, substantia nigra. I have made no distinction between indirect and direct pathways, and customized this for the purposes of the discussion within this paper. For a more complete and detailed description of this anatomy, see Obeso et al. (2008).
Mentions: In Parkinson’s disease, there is degeneration of neurons that use dopamine as a neurotransmitter, which have their cell bodies in the substantia nigra at the upper edge of the midbrain. The decrease in neural output from the substantia nigra causes a disturbance in the network balance of excitation and inhibition, as schematized in figure 2. The result is a net increase in inhibition from the GPi to thalamus (for a much more detailed discussion of the circuitry, see Obeso et al. (2008)). But the lines and arrows in these static diagrams refer to average firing rate or activity, and do not reflect the dynamics that is critical to understand what is happening. In Parkinson’s disease, the inhibition to the thalamus becomes phasic and oscillates.

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