Striatal Neuron Activity Patterns in Dystonia

The causes of dystonia are not clearly understood but abnormal signaling within the striatum, a region of the brain that controls movement, is thought to be involved. It is now possible to record the firing patterns of dozens of neurons simultaneously in the striatum of awake dystonic mice to reveal the abnormal neural code associated with dystonia. Technology known as in vivo microscopy will be used in mice with dystonia to visualize the firing patterns of neurons within the striatum. Mice will be recorded while they are dystonic and after they have been treated with drugs that alleviate the dystonia. By comparing the different firing patterns with and without dystonia, these experiments will reveal the neural code associated with dystonia for the first time. In the short term, these experiments will provide important information that could be useful to guide stimulation parameters for deep brain stimulation in dystonia patients. In the long-term, understanding the neural code of dystonia will provide important information for the development of novel therapeutics that target the abnormal neural code.

Machine Learning Guided Deep Brain Stimulation to Cure Neurological Disease

The DMRF is partnering with Jesse H. Goldberg, MD, PhD of Cornell University on a project to engineer a revolutionary new generation of deep brain stimulation (DBS) devices to treat dystonia and other neurological diseases.

Dystonia results from abnormal brain activity that can be corrected by direct electrical stimulation of dysfunctional brain pathways. In current DBS systems, an implanted medical device delivers continuous stimulation to the brain and adjustments to the stimulation must be made using a remote control device in the hands of a highly trained clinician. A major obstacle to providing patients with maximum benefit from this therapy is knowing where in the brain to stimulate and tailoring stimulation parameters to the unique needs of each patient.

Dr. Goldberg proposes a radically new approach to DBS. He is using artificial intelligence to develop a system in which a computer, interconnected with the brain, figures out exactly how and where to stimulate to restore normal movement.

In this three-year project, Dr. Goldberg will establish the feasibility of this concept in mice. He is collaborating with Mert Sabuncu, PhD in the School of Electrical and Computer Engineering and School of Biomedical Engineering at Cornell University.

Three-Dimensional Network Architecture of Dystonia

Brain imaging techniques have advanced the understanding of metabolic network abnormalities in inherited and sporadic dystonia. It remains elusive, however, whether dystonia-related brain networks can be identified with resting state functional MRI (magnetic resonance imaging) utilizing time-series information. It is also unclear whether such networks relate to underlying anatomical connections. Dr. Vo hypothesizes that dystonia is characterized by distinct functional and structural network topographies in the resting state. To test this hypothesis, she and her team will examine resting state functional MRI and diffusion MRI data in patients with inherited and sporadic dystonia. The proposed work will advance the understanding of brain network architecture in dystonia. The new information will help identify areas within the network space for optimal therapeutic targeting and individually customized treatment.