Identifying Circuit Dynamics Underlying Motor Dysfunction in Parkinson's Disease Using Data-Driven Modeling and Real-Time Neural Control
The goals of this project are to identify circuit-wide neural dynamics linked to the manifestation of Parkinson’s disease (PD) motor signs and to develop new deep brain stimulation (DBS) technology to control these neural dynamics in real-time.
Current theories propose that elevated 8-35 Hz oscillations, synchronized throughout the basal ganglia-thalamocortical (BGTC) circuit, are associated with rigidity and bradykinesia in Parkinson's disease. The circuit-wide dynamics underlying the generation of these oscillations and their causal relationship with specific motor signs, however, remains unclear. Clarifying this relationship is critical to advancing our understanding of PD pathophysiology and developing deep brain stimulation (DBS) therapies optimized to control neural processes underlying motor dysfunction in PD.
We are leveraging a new neural control approach we developed to suppress or amplify frequency-specific neural activity in real-time to characterize how controlled changes in 8-35 Hz oscillations in the internal segment of the globus pallidus (GPi) or the subthalamic nucleus (STN) influence the severity of motor signs in PD patients. This technique, referred to as evoked-interference closed-loop DBS (eiDBS), delivers stimulation with precise magnitude and timing to evoke neural responses that modulate spontaneous activity in a targeted neuronal population. The papers in  and  provide a more detailed description of eiDBS.
The data from this project will help clarify the causal role of GPi and STN 8-35 Hz oscillations in the manifestation of PD motor signs, and advance the development of closed-loop DBS technology that precisely controls oscillatory dynamics linked to PD.
Localizing, Modeling, and Modulating Neural Circuits to Control Seizure Onset in Epilepsy
The objective of this project is to develop tools that help improve clinical outcomes for resection or ablation surgery and brain stimulation therapies in mesial temporal lobe epilepsy (MTLE).
The first aim of this project is to develop a methodology to isolate and localize epileptiform activity sources in epilepsy patients by combining MR images, intracranial electrophysiological data, spatiotemporal data decomposition methods, and finite element modeling. Our objective in developing this methodology is to facilitate the localization of the seizure onset zone for resection or ablation surgery.
Our second aim is to construct models that describe the spatiotemporal neural dynamics associated with interictal epileptiform activity and seizure onset in MTLE. Using intracranial electrophysiological data, we will also develop mathematical models that characterize neural responses evoked by electrical stimulation pulses in circuits involved in seizure onset and propagation. By combining the aforementioned models of spatiotemporal neural dynamics and stimulation-evoked responses, we aim to conceive and test feedback control strategies to minimize seizure onset.
Electrical Stimulation Technologies and Feedback Control Strategies to Modulate Brain Circuitry in Real-Time
The goal of this project is to conceive and test feedback control technologies to precisely modulate brain activity in real-time. We are developing hardware and software tools to validate and rapidly prototype signal processing and feedback control algorithms for neural modulation. These technologies will enable us to characterize the role of neural activity in the manifestation of brain conditions and to advance the development of personalized neuromodulation therapies.