David Escobar, PhD
Assistant Staff
Director, Neural Dynamics and Modulation Lab
Email: escobad2@ccf.org
Location:
Cleveland Clinic Main Campus
Research
The goal of our research team is to develop effective brain stimulation therapies for brain conditions that are tailored to the needs of each patient. This research leverages the fields of neurophysiology, data science, feedback (closed-loop) control, and dynamical systems to in order identify circuit-wide neural dynamics causally linked to the manifestation of brain conditions, and to develop closed-loop brain stimulation techniques that control these dynamics in real-time. Our group also develops technologies and methods to enable and advance research and innovation in the neuromodulation field. These technologies include portable electronic systems to deliver devices and multi-objective closed-loop stimulation therapies to objectively quantify motor performance in patients with movement disorders.
Biography
David Escobar, PhD is Assistant Staff in both the Department of Biomedical Engineering, Lerner Research Institute, and with the Neurological Institute at Cleveland Clinic, where he directs the Neural Dynamics and Modulation Laboratory. Dr. Escobar received both his MS and PhD in aerospace engineering from the University of Minnesota in 2012 and 2015, respectively. His graduate research surrounded data-driven modeling of dynamical systems and robust feedback (closed-loop) control. In 2015, he joined the Department of Neurology at the University of Minnesota as a postdoctoral fellow, where he conducted preclinical and clinical research on neurophysiology, the pathophysiology of Parkinson’s disease, neural signal processing, and closed-loop deep brain stimulation (DBS) therapies.
Education & Professional Highlights
Education & Fellowships
- Postdoctoral Training
University of Minnesota Medical School, Department of Neurology, Minneapolis, MN
Focus areas: neurophysiology, movement disorders, deep brain stimulation (DBS), neural signal processing, data-driven mathematical modeling of brain circuits, and neural control engineering.
2015-2018 - PhD, Aerospace Engineering and Mechanics
University of Minnesota, Minneapolis, MN
Focus areas: data-driven mathematical modeling of nonlinear dynamical systems and robust feedback control.
2012-2015 - MS, Aerospace Engineering and Mechanics
University of Minnesota, Minneapolis, MN
Focus areas: design and validation of robust feedback control systems for autonomous aerial and underwater vehicles.
2010-2012 - Engineering Degree, Mechatronics Engineering
National University of Colombia, Bogota, Colombia
Focus areas: control systems, signal processing, robotics, computer science.
2003-2008
Research
Overview
Our laboratory is dedicated to advancing neuromodulation therapies for brain conditions, translating research into medical technology, training students and mentees in neuroengineering, and partnering with clinicians, scientists, and engineers to bring neuromodulation advancements to patients.
Research: Our team conducts clinical and preclinical research to increase the effectiveness of brain stimulation therapies. This research aims to address the variability in outcomes of current therapies within and among patients. We integrate neurophysiology, feedback control engineering, signal processing, data science, and data-driven mathematical modeling to characterize neural circuit dynamics underlying brain dysfunction, and to develop personalized brain stimulation approaches that control these neural dynamics in real-time.
Technology development and innovation: Part of our investigative efforts surround developing new technologies to advance our research, including the portable electronic systems to deliver multi-objective closed-loop brain stimulation and the devices themselves, to objectively quantify motor performance in patients with Parkinson's disease. Our technology innovation work is also dedicated to translating our research into neuromodulation devices and practical therapies.
Education and training: We are committed to education to expand the network of scientists, engineers, and clinicians working to improve the lifestyle of people suffering from brain conditions. We provide multidisciplinary training and career mentorship to undergraduate and graduate students, postdoctoral fellows, visiting researchers, medical trainees, and volunteers interested in a career in engineering, science, and medical technology innovation.
Collaboration and outreach: We work closely and collaborate with a multidisciplinary team of clinicians, scientists, and engineers across the Cleveland Clinic Neurological Institute and Lerner Research Institute to develop and test comprehensive neuromodulation treatments for distinct brain conditions, including Parkinson's disease, essential tremor, and stroke.
Projects
Identifying Circuit Dynamics Underlying Motor Dysfunction in Parkinson's Disease Using Data-Driven Modeling and Real-Time Neural ControlThe 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 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.
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.
Our Team
Selected Publications
Careers
We are always looking for talented students, postdocs, engineers, clinicians, and researchers to join our team. If you are interested in working with us and have expertise in any of the topics listed below, please contact Dr. David Escobar at escobad2@ccf.org.
- Signal processing
- Dynamical systems and feedback control engineering
- Data-driven mathematical modeling of dynamical systems
- Computer programming using Python, C++, MATLAB/Simulink
- Data science, machine learning, and deep learning
- Real-time computing
- Finite element modeling and electromagnetism
- Robotics
- Neuromodulation, deep brain stimulation
- Movement disorders
- Electrophysiology (EEG, iEEG, ECoG, MEG, and EMG)
- Computational neuroscience