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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

Nullclines for  and . (a) The heavy line is a trajectory initiated by depolarizing the rest state at the intersection of the clines from −65 to −25 mV, and the subsequent trajectory in the phase plane closely tracks the faster equilibrating v cline and slowly works its way up along the direction of increasing w as the T-current deinactivates (i.e. the availability of T-current, w, increases). (b) The effect of DBS, elevating the v cline. (c) The effect of sensorimotor stimulation, SM, which lowers the v cline. (Adapted from Rubin & Terman (2004).)
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RSTA20100050F9: Nullclines for and . (a) The heavy line is a trajectory initiated by depolarizing the rest state at the intersection of the clines from −65 to −25 mV, and the subsequent trajectory in the phase plane closely tracks the faster equilibrating v cline and slowly works its way up along the direction of increasing w as the T-current deinactivates (i.e. the availability of T-current, w, increases). (b) The effect of DBS, elevating the v cline. (c) The effect of sensorimotor stimulation, SM, which lowers the v cline. (Adapted from Rubin & Terman (2004).)

Mentions: The clines for v and w are shown in figure 9a. In excitatory cells and their models, there is almost always a cubic or N-shaped cline for the fast excitatory variable, the voltage v in this case. Keep in mind that, in this reduced model, there are no true action potential spikes (no INa or IK currents). These phase-space plots show us the slow dance between voltage changes, v and IT inactivation, w. In figure 9b, we see the effect of DBS. Increasing the synaptic current from the GPi, sGi, as the DBS parameter in equation (8.7) literally adds the IGi→Th current term in equation (8.7) to the solution of the v cline.7 This has a qualitatively opposite effect as increasing excitatory sensorimotor stimulation ISM in equation (8.6), which decreases the height of the v cline. The point where these clines intersect is the resting steady state for v and for w. The T-current inactivation, w, is a key factor in whether this system will respond with a rebound burst, respond reliably to a sensorimotor input or be inactive and unreliable by not responding at all.


Towards model-based control of Parkinson's disease.

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

Nullclines for  and . (a) The heavy line is a trajectory initiated by depolarizing the rest state at the intersection of the clines from −65 to −25 mV, and the subsequent trajectory in the phase plane closely tracks the faster equilibrating v cline and slowly works its way up along the direction of increasing w as the T-current deinactivates (i.e. the availability of T-current, w, increases). (b) The effect of DBS, elevating the v cline. (c) The effect of sensorimotor stimulation, SM, which lowers the v cline. (Adapted from Rubin & Terman (2004).)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F9: Nullclines for and . (a) The heavy line is a trajectory initiated by depolarizing the rest state at the intersection of the clines from −65 to −25 mV, and the subsequent trajectory in the phase plane closely tracks the faster equilibrating v cline and slowly works its way up along the direction of increasing w as the T-current deinactivates (i.e. the availability of T-current, w, increases). (b) The effect of DBS, elevating the v cline. (c) The effect of sensorimotor stimulation, SM, which lowers the v cline. (Adapted from Rubin & Terman (2004).)
Mentions: The clines for v and w are shown in figure 9a. In excitatory cells and their models, there is almost always a cubic or N-shaped cline for the fast excitatory variable, the voltage v in this case. Keep in mind that, in this reduced model, there are no true action potential spikes (no INa or IK currents). These phase-space plots show us the slow dance between voltage changes, v and IT inactivation, w. In figure 9b, we see the effect of DBS. Increasing the synaptic current from the GPi, sGi, as the DBS parameter in equation (8.7) literally adds the IGi→Th current term in equation (8.7) to the solution of the v cline.7 This has a qualitatively opposite effect as increasing excitatory sensorimotor stimulation ISM in equation (8.6), which decreases the height of the v cline. The point where these clines intersect is the resting steady state for v and for w. The T-current inactivation, w, is a key factor in whether this system will respond with a rebound burst, respond reliably to a sensorimotor input or be inactive and unreliable by not responding at all.

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