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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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Tracking and estimating parameters as a function of process noises Q. Estimates of synaptic current from GPi (i, sGi) and sensorimotor input (ii, SM input). Process noise parameters are (a) sGi : Q=30, SM : Q= 0.01, (b) sGi : Q=0.01, SM : Q=30 and (c) sGi : Q=10.0, SM : Q=0.01. The algorithmic incorporation of such process noise can be explored in the code archive with Schiff & Sauer (2008).
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RSTA20100050F12: Tracking and estimating parameters as a function of process noises Q. Estimates of synaptic current from GPi (i, sGi) and sensorimotor input (ii, SM input). Process noise parameters are (a) sGi : Q=30, SM : Q= 0.01, (b) sGi : Q=0.01, SM : Q=30 and (c) sGi : Q=10.0, SM : Q=0.01. The algorithmic incorporation of such process noise can be explored in the code archive with Schiff & Sauer (2008).

Mentions: When faced with such symmetries, it is best to get rid of them (Sauer & Schiff 2009). If that is not feasible, then an empirical rule of thumb seems to be to set process noise in rough proportions to the average magnitudes of the corresponding variables. One could adaptively tune these process noises over time by tracking innovation error.11 Process noise, Q, is uncertainty commonly added to the model of the process (the plant in control jargon) in analogous applications. In this particular instance of ensemble Kalman filtering, we will use several Q values as the assumed variance in the respective parameters to be tracked. In addition to apportioning variance to the respective parameters, this also has the benefit of preventing a Kalman filter from driving the parameter covariance to zero.12 In figure 12a, the Q for (−IGi→Th) is 30, while the Q for ISM is 0.01. Note that the rhythmicity of (−IGi→Th) is resolved, while none of the features of ISM are picked up (figure 12a). Now reverse the situation, setting Q for (−IGi→Th) to 0.01 and the Q for ISM to 30; the (−IGi→Th) will be poorly tracked, but the sensorimotor inputs ISM are better tracked (figure 12b). A more balanced set of process noises, where Q for (−IGi→Th) is 10, and Q for ISM is 0.01, yields a more optimal tracking of (−IGi→Th).


Towards model-based control of Parkinson's disease.

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

Tracking and estimating parameters as a function of process noises Q. Estimates of synaptic current from GPi (i, sGi) and sensorimotor input (ii, SM input). Process noise parameters are (a) sGi : Q=30, SM : Q= 0.01, (b) sGi : Q=0.01, SM : Q=30 and (c) sGi : Q=10.0, SM : Q=0.01. The algorithmic incorporation of such process noise can be explored in the code archive with Schiff & Sauer (2008).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F12: Tracking and estimating parameters as a function of process noises Q. Estimates of synaptic current from GPi (i, sGi) and sensorimotor input (ii, SM input). Process noise parameters are (a) sGi : Q=30, SM : Q= 0.01, (b) sGi : Q=0.01, SM : Q=30 and (c) sGi : Q=10.0, SM : Q=0.01. The algorithmic incorporation of such process noise can be explored in the code archive with Schiff & Sauer (2008).
Mentions: When faced with such symmetries, it is best to get rid of them (Sauer & Schiff 2009). If that is not feasible, then an empirical rule of thumb seems to be to set process noise in rough proportions to the average magnitudes of the corresponding variables. One could adaptively tune these process noises over time by tracking innovation error.11 Process noise, Q, is uncertainty commonly added to the model of the process (the plant in control jargon) in analogous applications. In this particular instance of ensemble Kalman filtering, we will use several Q values as the assumed variance in the respective parameters to be tracked. In addition to apportioning variance to the respective parameters, this also has the benefit of preventing a Kalman filter from driving the parameter covariance to zero.12 In figure 12a, the Q for (−IGi→Th) is 30, while the Q for ISM is 0.01. Note that the rhythmicity of (−IGi→Th) is resolved, while none of the features of ISM are picked up (figure 12a). Now reverse the situation, setting Q for (−IGi→Th) to 0.01 and the Q for ISM to 30; the (−IGi→Th) will be poorly tracked, but the sensorimotor inputs ISM are better tracked (figure 12b). A more balanced set of process noises, where Q for (−IGi→Th) is 10, and Q for ISM is 0.01, yields a more optimal tracking of (−IGi→Th).

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