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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

The extended Rubin–Terman model as proposed by Pirini et al. (2008). (Adapted from Pirini et al. (2008).)
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RSTA20100050F19: The extended Rubin–Terman model as proposed by Pirini et al. (2008). (Adapted from Pirini et al. (2008).)

Mentions: Recent work has extended the model of Rubin & Terman (2004) to take into account more biologically relevant connections, the direct versus indirect pathways, from striatum to the structures of the basal ganglia (figure 19). The further incorporation of relevant model components may well give greater fidelity to the realistic basal ganglia dynamics in Parkinson’s disease. But with our goal being control, such fidelity through complexity will need to be balanced against accuracy of data assimilation and control metrics. One of the important issues raised by Pirini et al. (2008) is that a more complex model of the thalamic cell’s function, beyond the simple relay, is probably important. One example of this is action-selection theory, which envisions that the basal ganglia serves to select from competing neuronal efforts for access to the final common path of motor movement (Humphries et al. 2006). Furthermore, as in human patient experience, the DBS target sites are not equivalent. Indeed, maintaining the flexibility to perform model-based control the STN, GPi or VIM, depending upon a patient’s symptom complex, is a challenge for future work.


Towards model-based control of Parkinson's disease.

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

The extended Rubin–Terman model as proposed by Pirini et al. (2008). (Adapted from Pirini et al. (2008).)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100050F19: The extended Rubin–Terman model as proposed by Pirini et al. (2008). (Adapted from Pirini et al. (2008).)
Mentions: Recent work has extended the model of Rubin & Terman (2004) to take into account more biologically relevant connections, the direct versus indirect pathways, from striatum to the structures of the basal ganglia (figure 19). The further incorporation of relevant model components may well give greater fidelity to the realistic basal ganglia dynamics in Parkinson’s disease. But with our goal being control, such fidelity through complexity will need to be balanced against accuracy of data assimilation and control metrics. One of the important issues raised by Pirini et al. (2008) is that a more complex model of the thalamic cell’s function, beyond the simple relay, is probably important. One example of this is action-selection theory, which envisions that the basal ganglia serves to select from competing neuronal efforts for access to the final common path of motor movement (Humphries et al. 2006). Furthermore, as in human patient experience, the DBS target sites are not equivalent. Indeed, maintaining the flexibility to perform model-based control the STN, GPi or VIM, depending upon a patient’s symptom complex, is a challenge for future work.

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