Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of medically refractory Parkinson's disease (PD). However, numerous questions remain on the therapeutic mechanisms of the technology. The fundamental goal of this Bioengineering Research Grant (PA-07-279) Competitive Renewal (NIH R01 NS047388) is to use detailed computer modeling techniques to augment neurophysiological investigation on the mechanisms of DBS. We hypothesize that therapeutic STN DBS induces basal ganglia (BG) network activity consistent with activation of axonal processes near the stimulating electrode, resulting in a regularization of BG input to the thalamus. In turn, thalamocortical processing can occur with reduced pathological interference from the BG during STN DBS. We propose to address this hypothesis with the integration of complementary data from three research modalities (experimental electrophysiology, computational modeling, and functional magnetic resonance imaging (fMRI)). First, we will parameterize a large scale neural network model to coincide with simultaneous multi-unit microelectrode recordings from the BG and thalamus of parkinsonian non-human primates. This experimental data will be acquired in collaboration with ongoing studies in the Vitek laboratory (NIH R01 NS037019;NIH R01 NS058945). Next, we will develop detailed patient-specific DBS models of the spread of stimulation in a cohort of 30 individuals using methodology developed in the PI's laboratory (NIH R01 NS059736). These patient-specific predictions of the neural tissue directly activated by their DBS will then be applied to the network model, allowing for evaluation of the network activity patterns generated by the stimulation. Finally, the integrated synaptic activity of the network model will be compared to fMRI data acquired in these same patients during DBS. This fMRI data will be acquired in collaboration with ongoing studies in the Phillips laboratory (NIH R01 NS052566). Our multi-disciplinary approach combines the strengths of each research modality and will provide a systems-level model of DBS. We will use our model to decipher the underlying neurophysiological changes responsible therapeutic benefit and define novel stimulation strategies that could improve clinical outcomes.

Public Health Relevance

The basic goal of this project is to use computer modeling to better understand the clinical therapy of deep brain stimulation (DBS) for the treatment of Parkinson's disease. We propose that detailed models, based on experimental data, will provide a unified description of the mechanisms of the therapy. We will then use that knowledge to design more efficacious stimulation strategies for patients implanted with DBS systems.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS047388-08
Application #
8243575
Study Section
Neurotechnology Study Section (NT)
Program Officer
Ludwig, Kip A
Project Start
2003-12-01
Project End
2013-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
8
Fiscal Year
2012
Total Cost
$334,614
Indirect Cost
$120,239
Name
Cleveland Clinic Lerner
Department
Other Basic Sciences
Type
Schools of Medicine
DUNS #
135781701
City
Cleveland
State
OH
Country
United States
Zip Code
44195
McIntyre, Cameron C; Chaturvedi, Ashutosh; Shamir, Reuben R et al. (2015) Engineering the next generation of clinical deep brain stimulation technology. Brain Stimul 8:21-6
Shamir, Reuben R; Dolber, Trygve; Noecker, Angela M et al. (2015) Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease. Brain Stimul 8:1025-32
Shamir, Reuben R; Dolbert, Trygve; Noecker, Angela M et al. (2014) A method for predicting the outcomes of combined pharmacologic and deep brain stimulation therapy for Parkinson's disease. Med Image Comput Comput Assist Interv 17:188-95
Shamir, Reuben R; Dolbert, Trygve; Noecker, Angela M et al. (2014) A method for predicting the outcomes of combined pharmacologic and deep brain stimulation therapy for Parkinson's disease. Med Image Comput Comput Assist Interv 17:188-95
Arlow, R L; Foutz, T J; McIntyre, C C (2013) Theoretical principles underlying optical stimulation of myelinated axons expressing channelrhodopsin-2. Neuroscience 248:541-51
McIntyre, Cameron C; Foutz, Thomas J (2013) Computational modeling of deep brain stimulation. Handb Clin Neurol 116:55-61
Lempka, Scott F; McIntyre, Cameron C (2013) Theoretical analysis of the local field potential in deep brain stimulation applications. PLoS One 8:e59839
Foutz, Thomas J; Arlow, Richard L; McIntyre, Cameron C (2012) Theoretical principles underlying optical stimulation of a channelrhodopsin-2 positive pyramidal neuron. J Neurophysiol 107:3235-45
Rubin, Jonathan E; McIntyre, Cameron C; Turner, Robert S et al. (2012) Basal ganglia activity patterns in parkinsonism and computational modeling of their downstream effects. Eur J Neurosci 36:2213-28
Foutz, Thomas J; Ackermann Jr, D Michael; Kilgore, Kevin L et al. (2012) Energy efficient neural stimulation: coupling circuit design and membrane biophysics. PLoS One 7:e51901

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