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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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(a,b) Stimulation of TC-cell model at 10 Hz and (c,d) 40 Hz, (a,c) at low and (b,d) high levels of tonic inhibition. (Adapted from Rubin & Terman (2004).)
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RSTA20100050F7: (a,b) Stimulation of TC-cell model at 10 Hz and (c,d) 40 Hz, (a,c) at low and (b,d) high levels of tonic inhibition. (Adapted from Rubin & Terman (2004).)

Mentions: We can provide an analogue to sensorimotor stimulation to this TC cell by periodically stimulating it. This is a signal that we hope the cell can relay. In figure 7a, we start with slow stimulation. The signal is reliably relayed. Now, simultaneously provide the cell with excessive inhibition, such as in Parkinson’s disease, from an overactive GPi. In figure 7b, the baseline membrane potential is now more hyperpolarized. With each sensorimotor pulse, the cell rebound spikes because the T-current is deinactivated. This is because hyperpolarization removes IT inactivation, just as in the sodium inactivation in the Hodgkin–Huxley gating variable h (Hodgkin & Huxley 1952). But removing inactivation is also relatively slow. In figure 7b, there is sufficient time for this variable to be fully deinactivated. Contrast this with higher stimulation frequencies. In figure 7c, the TC cell is reliable, and it remains so in the setting of additional inhibition in figure 7d because there is insufficient time for the inactivation to deinactivate.


Towards model-based control of Parkinson's disease.

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

(a,b) Stimulation of TC-cell model at 10 Hz and (c,d) 40 Hz, (a,c) at low and (b,d) high levels of tonic inhibition. (Adapted from Rubin & Terman (2004).)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F7: (a,b) Stimulation of TC-cell model at 10 Hz and (c,d) 40 Hz, (a,c) at low and (b,d) high levels of tonic inhibition. (Adapted from Rubin & Terman (2004).)
Mentions: We can provide an analogue to sensorimotor stimulation to this TC cell by periodically stimulating it. This is a signal that we hope the cell can relay. In figure 7a, we start with slow stimulation. The signal is reliably relayed. Now, simultaneously provide the cell with excessive inhibition, such as in Parkinson’s disease, from an overactive GPi. In figure 7b, the baseline membrane potential is now more hyperpolarized. With each sensorimotor pulse, the cell rebound spikes because the T-current is deinactivated. This is because hyperpolarization removes IT inactivation, just as in the sodium inactivation in the Hodgkin–Huxley gating variable h (Hodgkin & Huxley 1952). But removing inactivation is also relatively slow. In figure 7b, there is sufficient time for this variable to be fully deinactivated. Contrast this with higher stimulation frequencies. In figure 7c, the TC cell is reliable, and it remains so in the setting of additional inhibition in figure 7d because there is insufficient time for the inactivation to deinactivate.

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