Research Program

Deep Brain Stimulation

Deep brain stimulation (DBS) of the thalamus or basal ganglia represents an effective clinical treatment of several medically refractory neurological disorders. DBS is an established therapy for essential tremor, Parkinson’s disease, and dystonia. DBS also shows promise in the treatment of epilepsy, obsessive-compulsive disorder, Tourette's syndrome, and major depression. However, the clinical successes of DBS are tempered by our limited understanding of the effects of DBS on the nervous system, and scientific definition of the therapeutic mechanisms of action of DBS remains elusive. In addition, it is presently unclear what electrode designs and stimulation parameters are optimal for maximum therapeutic benefit and minimal side effects. The goal of this laboratory is to couple results from functional imaging, neurophysiology, and neuroanatomy to create a theoretical framework that enhances our understanding of the effects of DBS and provides a virtual testing ground for new stimulation paradigms.

Uncovering the Mechanisms of DBS

We are working to develop a quantitative understanding of the effects of DBS using the techniques of computational neuroscience and electromagnetic field modeling. Our goal is to augment experimental investigation in DBS of the parkinsonian non-human primate as well as improve the electrode targeting and postoperative parameter selection processes in the human. Our modeling process consists of three basic steps. First we develop models of the electric field generated by DBS electrodes. We use diffusion tensor MRI to characterize the 3D tissue electrical properties of the brain. We then create a 3D rendering of the DBS electrode and surrounding tissue medium and solve for the electric field generated by the stimulation using the finite element method. Our results show that minor alterations in either the electrode position in the brain or geometry of the stimulating contact can strongly affect the shape of the field and subsequent neural response to stimulation. The second step consists of coupling the electric field to models of individual neurons surrounding the electrode. The neuron models have geometries based on 3D anatomical reconstructions, and ion channel biophysics derived from experimental recordings. The neuron models are positioned in the field and their response is measured as a function of the stimulation parameters. Using these techniques, we have developed stimulus waveforms that enable selective activation of targeted neuronal populations. The final step in our modeling process consists of applying the stimulation effects predicted at the single cell level to large scale neural network models. The therapeutic effects of DBS probably lie in its ability to disrupt pathological network oscillations within different regions of the brain. We are working to understand the origin of these oscillatory patterns in network models that consist of hundreds of interacting neurons. We apply the effects of DBS to our network models and address how the stimulation changes interactions between nuclei. Our results show that DBS can dramatically enhance the firing of nuclei upstream and downstream from the site of stimulation and we are working to couple our network modeling results to neurophysiological recordings and fMRI experiments performed in both experimental animals and humans.

Patient-Specific Models of DBS

The Cleveland Clinic represents one of the largest DBS implant centers in the world. In turn, our research group has a substantial amount of interaction with the patients and clinicians that use DBS technology. The major limitations in the current clinical practice of DBS are the time consuming surgical procedure and the difficulties in the post-operative programming of the device. Programming DBS devices for maximal clinical benefit typically requires a highly trained and experienced individual to achieve acceptable results.These procedures are typically done with no visual reference of the electrode location in the anatomy or current spread as it depends on the stimulation parameters. The fundamental purpose of DBS is to modulate neural activity with electric fields, but the technology necessary to accurately predict and visualize the neural response to DBS has not been previously available. To address this problem we use detailed computer models of STN DBS, combining diffusion tensor based finite element models of the electric field and 3D anatomical models of nuclei surrounding the electrode, to predict the effects of electrode location and stimulation parameter adjustments on the volume of tissue activated (VTA) by the stimulation on a patient-specific basis. We developed a Windows-based, clinician-friendly, software package (StimExplorer) that allows for integration of MRI data sets and positioning of the DBS electrode with respect to the anatomy.An interactive 3D display shows the anatomy, electrode, and VTA for any given stimulation parameter setting (contact, voltage, pulse width, frequency). We are currently using our patient-specific software system, in combination with clinical stimulation testing, to better define the target VTA for maximal therapeutic benefit and minimal side effects.

Next Generation DBS Technology

DBS technology is in its infancy. The current clinical DBS systems were adapted from cardiac pacing technology ~20 years ago without knowledge of several fundamental neurostimulation principals that have only recently been elucidated. In addition, current DBS technology uses a simplistic open-loop continuous stimulation strategy, which cannot be adjusted by the patient, and can be associated with a variety of stimulation induced side effects. Recent advances in computational capabilities and neural engineering design techniques provide the ability to quantitatively evaluate the neural response to DBS in a controlled environment. In turn, a unique opportunity exists to design theoretically optimal DBS electrodes, customized to the anatomy and physiolology of the stimulation target, and countless opportunities exist to design closed-loop DBS systems with novel stimulation patterns. We are using computer modeling coupled to experimental and clinical investigation to build the foundation for the development of the next generation of DBS devices. Our goal is to develop a computational framework that will generate experimentally testable hypotheses on the mechanisms of DBS and provide a testing ground for new electrode designs and stimulation parameters. In turn, we hope to improve DBS for the treatment of movement disorders and provide fundamental technology necessary for the application of DBS to a vast array of new clinical arenas.