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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

(a) Uncontrolled reduced TC-cell dynamics in the Parkinsonian state with fluctuating current from the GPi (sGi). (b) Perfect DBS stimulation filling in the troughs in the fluctuating current from the GPi. (i) Noisy observable voltage (blue symbols), reliability as piecewise continuous plots without (blue) and with (red) control on, the green circles are the timing of SM spikes and the smaller red circles are transmitted spikes. (ii) Estimated w (red). (iii) Real (black) and estimated (magenta) synaptic current from the GPi (sGi, estimated values multiplied by 10 for discriminability from the true values). (iv) Real (red) and estimated (magenta) motor input (we are deliberately not trying to reconstruct motor input in the reconstruction through Q ratio adjustment). (v) Running control energy, the squared value of the control signal at each time point and the total sum of squares given as total control energy.
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RSTA20100050F13: (a) Uncontrolled reduced TC-cell dynamics in the Parkinsonian state with fluctuating current from the GPi (sGi). (b) Perfect DBS stimulation filling in the troughs in the fluctuating current from the GPi. (i) Noisy observable voltage (blue symbols), reliability as piecewise continuous plots without (blue) and with (red) control on, the green circles are the timing of SM spikes and the smaller red circles are transmitted spikes. (ii) Estimated w (red). (iii) Real (black) and estimated (magenta) synaptic current from the GPi (sGi, estimated values multiplied by 10 for discriminability from the true values). (iv) Real (red) and estimated (magenta) motor input (we are deliberately not trying to reconstruct motor input in the reconstruction through Q ratio adjustment). (v) Running control energy, the squared value of the control signal at each time point and the total sum of squares given as total control energy.

Mentions: In figure 13a, I employ the reduced TC-cell model from Rubin & Terman (2004) in a data assimilation framework. The observable will be noisy voltage from the TC cell in figure 13a(i),b(i). Below that is shown the reconstructed estimated T-current inactivation, w, and the reconstruction of estimated GPi and sensorimotor activity ((ii)–(iv), respectively). The techniques used to accomplish this reconstruction are detailed in Schiff & Sauer (2008), Sauer & Schiff (2009) and Ullah & Schiff (2009), and a code archive for the basics of such data assimilation can be found in the electronic supplementary material with Schiff & Sauer (2008).


Towards model-based control of Parkinson's disease.

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

(a) Uncontrolled reduced TC-cell dynamics in the Parkinsonian state with fluctuating current from the GPi (sGi). (b) Perfect DBS stimulation filling in the troughs in the fluctuating current from the GPi. (i) Noisy observable voltage (blue symbols), reliability as piecewise continuous plots without (blue) and with (red) control on, the green circles are the timing of SM spikes and the smaller red circles are transmitted spikes. (ii) Estimated w (red). (iii) Real (black) and estimated (magenta) synaptic current from the GPi (sGi, estimated values multiplied by 10 for discriminability from the true values). (iv) Real (red) and estimated (magenta) motor input (we are deliberately not trying to reconstruct motor input in the reconstruction through Q ratio adjustment). (v) Running control energy, the squared value of the control signal at each time point and the total sum of squares given as total control energy.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F13: (a) Uncontrolled reduced TC-cell dynamics in the Parkinsonian state with fluctuating current from the GPi (sGi). (b) Perfect DBS stimulation filling in the troughs in the fluctuating current from the GPi. (i) Noisy observable voltage (blue symbols), reliability as piecewise continuous plots without (blue) and with (red) control on, the green circles are the timing of SM spikes and the smaller red circles are transmitted spikes. (ii) Estimated w (red). (iii) Real (black) and estimated (magenta) synaptic current from the GPi (sGi, estimated values multiplied by 10 for discriminability from the true values). (iv) Real (red) and estimated (magenta) motor input (we are deliberately not trying to reconstruct motor input in the reconstruction through Q ratio adjustment). (v) Running control energy, the squared value of the control signal at each time point and the total sum of squares given as total control energy.
Mentions: In figure 13a, I employ the reduced TC-cell model from Rubin & Terman (2004) in a data assimilation framework. The observable will be noisy voltage from the TC cell in figure 13a(i),b(i). Below that is shown the reconstructed estimated T-current inactivation, w, and the reconstruction of estimated GPi and sensorimotor activity ((ii)–(iv), respectively). The techniques used to accomplish this reconstruction are detailed in Schiff & Sauer (2008), Sauer & Schiff (2009) and Ullah & Schiff (2009), and a code archive for the basics of such data assimilation can be found in the electronic supplementary material with Schiff & Sauer (2008).

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