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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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(i) Model of the TC, (ii) GPi and (iii) STN cells to periodic sensorimotor stimulation (i) in the (a) normal and (b) Parkinsonian states. (Adapted from Rubin & Terman (2004).)
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RSTA20100050F8: (i) Model of the TC, (ii) GPi and (iii) STN cells to periodic sensorimotor stimulation (i) in the (a) normal and (b) Parkinsonian states. (Adapted from Rubin & Terman (2004).)

Mentions: Following the schematic in figure 5, the Parkinsonian state is recreated by increasing the striatal input to the GPe, and decreasing the amount of internal recurrent inhibition within the GPe. The result is that the normal reliability of the TC cell to transmit sensorimotor information, illustrated in figure 8, becomes impaired in the Parkinsonian state. The key quantity here is the error rate of transmitting sensorimotor input into TC spikes. An error index can be created asRubin & Terman (2004) showed that the error rate was significantly elevated in the Parkinsonian3 state in comparison with the normal state, and that the error rate could be normalized by simulating DBS using a constant level of high-frequency stimulation of the STN. The key to understand these results is to know what the TC cell is receiving. In the Parkinsonian state, the amount of inhibition is fluctuating more than normal. This induces sequential excess suppression and rebound bursting in the TC cell, which destroys reliability. By applying DBS, the fluctuations that the TC cell receives are decreased, despite an overall increase in GPi-cell firing.


Towards model-based control of Parkinson's disease.

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

(i) Model of the TC, (ii) GPi and (iii) STN cells to periodic sensorimotor stimulation (i) in the (a) normal and (b) Parkinsonian states. (Adapted from Rubin & Terman (2004).)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F8: (i) Model of the TC, (ii) GPi and (iii) STN cells to periodic sensorimotor stimulation (i) in the (a) normal and (b) Parkinsonian states. (Adapted from Rubin & Terman (2004).)
Mentions: Following the schematic in figure 5, the Parkinsonian state is recreated by increasing the striatal input to the GPe, and decreasing the amount of internal recurrent inhibition within the GPe. The result is that the normal reliability of the TC cell to transmit sensorimotor information, illustrated in figure 8, becomes impaired in the Parkinsonian state. The key quantity here is the error rate of transmitting sensorimotor input into TC spikes. An error index can be created asRubin & Terman (2004) showed that the error rate was significantly elevated in the Parkinsonian3 state in comparison with the normal state, and that the error rate could be normalized by simulating DBS using a constant level of high-frequency stimulation of the STN. The key to understand these results is to know what the TC cell is receiving. In the Parkinsonian state, the amount of inhibition is fluctuating more than normal. This induces sequential excess suppression and rebound bursting in the TC cell, which destroys reliability. By applying DBS, the fluctuations that the TC cell receives are decreased, despite an overall increase in GPi-cell firing.

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