Research Seminars

The Center for Neural Engineering hosts a variety of seminars throughout the academic year to expose students to relevant work in the field. Upcoming seminars and those from the recent past are listed below.

April 26, 2022

Developing therapeutic strategies for neurological disorders, particularly those of the cerebellum 

Dr. Collin Anderson, postdoctoral research associate, Department of Neurology, University of Utah

3:00-4:00 p.m. ET

Abstract: Several forms of movement disorders arise through neurodegeneration affecting the basal ganglia and cerebellum. Parkinsonism arises through the loss of dopaminergic substantia nigra neurons and affects more than 1% of the population above 60 years of age. High frequency, 100+ Hz, deep brain stimulation (DBS) of the subthalamic nucleus has become a common late-stage therapy for parkinsonism, but its mechanisms are unclear. To better elucidate the mechanisms of DBS, we evaluated the effects of deep brain stimulation in a rodent 6-hydroxydopamine lesion model of hemiparkinsonism. We made simultaneous electrophysiological recordings within the basal ganglia and downstream thalamic neurons prior to lesion and in a hemiparkinsonian state, both on and off DBS. Applying information-theoretic metrics to simultaneously recorded neuronal spike trains, we demonstrated that the parkinsonian network is characterized by an over-coupling of neuronal signals across regions compared to the healthy state, and this is reversed by successful DBS. Thus, high-frequency DBS may function as an informational lesion. 

Progressive cerebellar ataxias can arise through the degeneration of Purkinje cells, affecting 1 in every 5000 individuals, with few receiving any treatment beyond palliative care. While dozens of genetic causes have been identified, most cases are sporadic. Unlike in parkinsonism, we hypothesized that ataxic symptoms arise through a loss of motor coordination-relevant information caused by Purkinje cell degeneration. Further we hypothesized that low, rather than high-frequency, deep brain stimulation targeting the deep cerebellar nuclei may reduce ataxic symptoms by enhancing the throughput of remaining signals. We tested this hypothesis in the shaker rat, a spontaneous model of Purkinje cell degeneration and ataxia. We found that while standard 100+ Hz DBS worsened ataxia, low frequency (~30 Hz) stimulation improved both ataxia and cerebellar tremor. Thus, low-frequency cerebellar DBS may function as a catch-all therapy for sporadic ataxias. However, in a small subset of genetic cases, gene therapy may provide a more attractive treatment option. We recently found evidence suggesting that the shaker phenotype may be caused by a loss of function mutation in the Slc9a6 gene. Slc9a6 mutations cause Christianson syndrome in humans, characterized by cerebellar degeneration, progressive ataxia, and several other severe symptoms. Therefore, we generated an adeno-associated virus targeting expression of the Slc9a6 gene to Purkinje cells as a form of gene replacement therapy. Administration of this virus prior to Purkinje cell death generated substantial motor protection, with a subset of rats developing virtually no tremor or gait ataxia. Current and future work in these lines of work will focus on further optimization of neuromodulatory strategies and the full preclinical testing of a human treatment-specific viral construct for Christianson syndrome. 

Bio: Dr. Collin Anderson completed his undergraduate in Biomedical Engineering at Johns Hopkins University. He then earned his PhD in the Neural Interfaces track of Bioengineering at the University of Utah under Dr. Chuck Dorval, performing in vivo studies to characterize the mechanisms of deep brain stimulation. Dr. Anderson is currently a postdoc with Dr. Stefan Pulst in the University of Utah department of Neurology, where he works on therapeutic optimization and several novel therapeutics for movement disorders. Dr. Anderson’s primary research goals in recent years have revolved around degenerative cerebellar ataxic disorders, and his work in this area spans both neuromodulatory and gene therapeutic strategies. Dr. Anderson’s work has been funded by numerous granting organizations, with most recent awards from the National Ataxia Foundation and the RTW Charitable Foundation. 

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April 13, 2022

Mathematical Models of Anomoalus Diffusion Processes in Brain

Dr. Corina Drapaca, associate professor, Department of Engineering Science and Mechanics, Penn State

Noon – 1:00 p.m. ET


Abstract: The complexity of brain structure and processes suggests that anomalous diffusion of ions, water and other particles is involved in brain’s functions and pathology. Anomalous diffusion through various materials has been successfully modeled using fractional calculus, and, therefore, in this talk, two mathematical models that use fractional order integro-differential operators will be presented: 1) a spatio-temporal fractional cable equation for action potentials propagation in myelinated neurons, and 2) a space-fractional reaction-diffusion equation for cerebral nitric oxide (NO) biotransport. While ionic anomalous diffusion near the nodes of Ranvier could be caused by the crowdedness of the very narrow ion channels and the diffusion barriers of the extracellular space, the anomalous diffusion of NO is due to its entrapment by endothelial microparticles whose production is enhanced in the presence of pathology. In addition, the model of NO biotransport incorporates the shear-induced NO production at endothelium and the pulsatile blood flow. The predictive abilities of the proposed models are investigated through numerical simulations.

Bio: Dr. Corina Drapaca is an applied mathematician who earned her bachelor’s and master’s degrees from University of Bucharest, Romania and her doctorate from University of Waterloo, Canada. She has held postdoctoral positions at University of California in San Francisco and Mayo Clinic. Since 2007, Drapaca has been a faculty member in the Department of Engineering Science and Mechanics at Pennsylvania State University. Drapaca’s expertise is in mathematical modeling of brain multiphysics and multiscale entangled processes, continuum and fluid mechanics, medical image processing, and computational analysis. The focus of her research is understanding mechanisms of onset and evolution of brain diseases through mathematical models and numerical simulations.

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March 23, 2022

Minimally Invasive and Chronically Stable Neural Interfaces

Dr. Tao Zhou, Postdoctoral Associate, Mechanical Engineering, Massachusetts Institute of Technology

Noon – 1:00 p.m. ET


Abstract: Stable chronic mapping of brain activities at the action potential level with high temporal resolution is essential for both fundamental neuroscience research and biomedical applications, including cognitive studies, memory encoding and retrieval, and neural prostheses. Conventional neural probes can provide high spatiotemporal-resolution brain signal recordings independent of probing depth, although they generally trigger foreign body response and tissue damage in the brain. As a result, they are usually unable to stably interface with the brain in a chronic manner, which substantially hinders their applications in neuroscience. In this seminar, I will present a new paradigm, mesh-like electronics, for minimally invasive and chronically stable brain-machine interface. The mesh-like electronics can seamlessly interface with mammal brains with significantly reduced foreign body response and can stably record brain signals with high spatiotemporal resolution for more than 8 months. I will then present the application of mesh-like electronics for chronic recording and modulations of spinal cord sensory and motor neurons in awake mice. In the end, I will present an alternative approach to designing minimally invasive neural electronics with hydrogel-based materials and the rapid fabrication of designed neural electronics with additive manufacturing. Both the mesh-like electronics and hydrogel electronics opened up new windows to stably communicating with the nervous system with minimum perturbation and foreign body responses.

Bio: Tao Zhou is currently a Postdoctoral Associate at MIT. He received his B.S. from Tsinghua University with a major in chemistry and a minor in computer science. He then went to Harvard University to pursue his Ph.D. in chemical physics, where he worked on mesh-like electronics for neural interfaces. Then he moved to MIT for his postdoc research in the Department of Mechanical Engineering, where he works on hydrogel-based neural interfaces, addictive manufacturing, and bioelectronic medicine.

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March 21, 2022

Bi-directional Neural-Machine Interface to Enable Dexterous Control of Robotic Hands

Dr. Xiaogang Hu, Associate Professor, Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, North Carolina State University

Noon – 1:00 p.m. ET


Abstract: An intuitive neural interface is critical for effective communications between humans and assistive devices. We will discuss bi-directional noninvasive neural-machine interfaces that decode user intended movement and encode sensory information of the machine state and environment. We perform continuous decoding of intended finger movement based on population motoneuron firing activities, extracted from high-density electromyographic signals. It allows intuitive and robust control of individual fingers of a prosthetic hand. We also deliver artificial somatosensory (haptic and proprioceptive) feedback to people with an arm amputation using transcutaneous nerve stimulation and vibrotactile stimulation. The evoked sensory feedback can facilitate tactile-based object recognition and enhance closed-loop control of robotic hands. The bi-directional neural interfaces can enable dexterous control of assistive robotic devices in individuals with sensorimotor deficits.

Bio: Xiaogang Hu is an associate professor in the Joint Department of Biomedical Engineering at University of North Carolina at Chapel Hill and North Carolina State University. He was trained in motor control and biomechanics at Penn State during his doctoral study, and he completed his postdoc training in stroke neurophysiology at the Rehabilitation Institute of Chicago (currently Shirley Ryan AbilityLab). His research focuses on neural-machine interface and neural stimulation, targeting upper limb sensorimotor functions of individuals after stroke, traumatic brain injury, or limb loss.

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March 15, 2022

Neuron and patient-specific computational modeling for neuromodulation in neurological disorders

Dr. Daria Anderson, Postdoctoral Research Fellow, Department of Neurosurgery, Department of Pharmacology & Toxicology, University of Utah

11:00 a.m. – noon ET


Abstract: Neuromodulation is often the last line of therapy for movement disorders, psychiatric disorders, and epilepsy when medication alone cannot manage symptoms. The difference between successful and ineffective therapy often lies in stimulation parameter selection, which can be challenging to optimize. Computational modeling has been used throughout the neuromodulation field to model stimulation influence on tissue, but many aspects of successful neuromodulation, such as its influence on disease networks, are poorly understood.

In her movement disorders-focused work, Dr. Anderson has characterized multiple facets of how stimulation parameter choice affects surrounding tissue. She defined how different neuronal fiber orientations can be selectively targeted by modifying stimulation waveforms, as well as using anodic and bipolar stimulation. She advanced the classic modeling techniques of the volume of tissue activated (VTA) to incorporate anisotropic diffusion imaging. Her Hessian matrix-based VTA method can be computed orders of magnitude faster than classic VTAs, which enabled the creation of a near real-time optimization algorithm to maximize stimulation of a given neural target and avoid stimulation outside the target. This advance is particularly important in determining contact configurations for complex electrode designs, such as novel directional electrodes. She has similarly explored the role of pulse width modulation and small contact size in improving selective targeting of small diameter fibers. In the same vein, Dr. Anderson developed and fabricated a novel, multiresolution DBS electrode with 864 micro-sized, individually controllable contacts to improve targeting of smaller diameter, therapeutic fibers.

Dr. Anderson’s latest work is focused on stimulation for drug-resistant epilepsy. Understanding how stimulation affects brain tissue, how brain networks may be modulated through stimulation, and how stimulation can lead to therapeutic benefit are critical, central questions to Dr. Anderson’s research in her efforts to improve therapies for epilepsy. Epilepsy is a relatively new application for neuromodulation, and it is unclear how stimulation fundamentally leads to seizure arrest or prevention. Patients undergoing neuromodulation therapy for epilepsy represent some of the most challenging epilepsy cases: they have failed to respond to multiple anti-epileptic medications and are not candidates for resective or lesional therapies. Very few patients, approximately 15%, achieve seizure freedom through stimulation therapy, though outcomes gradually improve over time. In her current work, Dr. Anderson uses structural connectivity analyses derived from patient-specific diffusion imaging to predict patient outcomes in epilepsy. Dr. Anderson has found that non-seizure epoch stimulation (stimulation during low-risk seizure states) and increased time in low-risk states during responsive neurostimulation is predictive of improved clinical outcomes. Given that recent literature has demonstrated that patients with good outcomes undergo network reorganization, Dr. Anderson hypothesizes that stimulation during low-risk periods may be driving neuromodulation-induced plasticity and the long-term improvements that have been observed. Dr. Anderson’s future research goals are to understand the long-term, plastic effects of stimulation on epilepsy networks, with the goals of the accelerating the network reorganization effects necessary to generate therapeutic benefit and, ultimately, helping more patients achieve seizure freedom.

Bio: Dr. Daria Anderson completed her undergraduate in biomedical engineering with a minor in neuroscience at Duke University and went on to earn her PhD in the neural interfaces track in biomedical engineering at the University of Utah. Her PhD research under advisors Dr. Chuck Dorval and Dr. Christopher Butson concentrated on computational modeling to estimate neural activation, improve neural selectivity, and develop novel electrode technologies for neuromodulation therapies. Dr. Anderson is currently a postdoc with Dr. John Rolston in Neurosurgery and Dr. Karen Wilcox in Pharmacology and Toxicology at the University of Utah, and she performs translational research focused on neuromodulation therapies for epilepsy in both pre-clinical and patient-specific models. Using pre-surgical electrophysiological data and neuroimaging, her work aims to identify patient-specific neural circuits that may serve as more effective targets for neuromodulation therapy. She is also interested in uncovering functional and structural correlates to therapeutic outcomes through computational modeling approaches to enable future improvements in surgical therapies for refractory epilepsy. Dr. Anderson has been funded consistently throughout her research career. In graduate school, she was awarded a John C. Jackson Fellowship and an NSF GRFP fellowship, while in her postdoc, she earned a TL1 fellowship through the University of Utah Clinical and Translational Science Institute, as well as an F32 NRSA fellowship and an LRP award through the NIH NINDS. In her spare time, Anderson enjoys kayaking, hiking, and camping with her partner and pets, collecting tropical plants, and crafting clothing and furniture.

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March 2, 2022

Finding Beliefs within a Brain

Xaq Pitkow, Associate Professor, Department of Neuroscience, Baylor College of Medicine and Associate Professor, Department of Electrical and Computer Engineering, Rice University 

Noon - 1:00 p.m.

107 Chemical and Biomedical Engineering Building | Zoom

Abstract: Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. We interpret behavioral data by assuming an agent behaves rationally—that is, they take actions that optimize their subjective reward according to their understanding of the task and its relevant causal variables, even if they are wrong. We apply a new method, Inverse Rational Control (IRC), to learn an agent's internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. Technically, we define an animal's strategy as solving a Partially Observable Markov Decision Process (POMDP), and we invert this model to find the task and subjective costs that have maximum likelihood. This is a generalization of both Inverse Reinforcement Learning and Inverse Optimal Control. Our mathematical formulation thereby extracts rational and interpretable thoughts of the agent from its behavior. The thoughts imputed to the animal can then serve as latent targets for neural analyses. Using these targets, we provide a framework for interpreting the linked processes of encoding, recoding, and decoding of neural data in light of the rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the computational dynamics within the neural manifold that represents the task. When applied to behavioral data from monkeys catching fireflies in virtual reality, we discover the properties of their mental model. Consistent with this theory of rational control, we see signatures of mental navigation dynamics within the monkeys’ parietal cortices that predict their actions. Overall, our approach may identify explainable structure in complex neural activity patterns, and thereby lays a foundation for discovering how the brain represents and computes with dynamic beliefs. 

Bio: Xaq Pitkow is a computational neuroscientist aiming to explain brain function by constructing quantitative theories of how distributed nonlinear neural computation implements principles of statistical reasoning to guide action. Although he is a theorist, he at one point did perform neuroscience experiments and still collaborates closely with experimentalists to ground his theories, help design experiments, and analyze data. He was trained in physics as an undergraduate student at Princeton and went on to study biophysics for his Ph.D. at Harvard. He then took postdoctoral positions in the Center for Theoretical Neuroscience at Columbia and in the department of Brain and Cognitive Sciences at the University of Rochester. In 2013 he moved to Houston to become a faculty member at the Baylor College of Medicine in the Department of Neuroscience, with a joint appointment at Rice University in the Department of Electrical and Computer Engineering. He is a co-director of the Center for Neuroscience and Artificial Intelligence at Baylor and a member of the NeuroEngineering group at Rice. He has been a professional graphic artist since he was twelve and enjoys sculpting and digital art, which he often integrates into his scientific work. He also enjoys improvisation on the piano, tabla, and two dozen other musical instruments. 

February 23, 2022

Biomaterials Niche for Immuno- and Regenerative Engineering

Dr. Hai-Quan Mao, Associate Director, Institute of NanoBioTechnology; Professor, Department of Materials Science and Engineering, Whiting School of Engineering; and Department of Biomedical Engineering, Translational Tissue Engineering Center, School of Medicine, Johns Hopkins University

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February 16, 2022

Altered Cerebrospinal Fluid Hydrodynamics are Associated with Impairments to Meningeal Lymphatic Networks and the Glymphatic System in Craniosynostosis

Dr. Max A. Tischfield, Assistant Professor, Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey and Resident Scientist, Child Health Institute of New Jersey

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January 26, 2022

Tools for Analyzing and Controlling the Brain

Dr. Ed Boyden, Y. Eva Tan Professor in Neurotechnology, MIT, and Investigator, Howard Hughes Medical Institute and the MIT McGovern Institute
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January 19, 2022

Automated Neuroprosthetics: Selfhood, Trust, and Partnership

Timothy Brown, Assistant Professor of Bioethics and Humanities
University of Washington School of Medicine
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December 8, 2021

The Role of APOE in Amyloid-β and Tau-Mediated Pathogenesis of Alzheimer’s Disease

David M. Holtzman, Barbara Burton and Reuben M. Morriss III Distinguished Professor
Washington University School of Medicine
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December 1, 2021

Tackling Brain Diseases with Mechanics and Advanced Neuroimaging

Mehmet Kurt, Assistant Professor of Mechanical Engineering
Stevens Institute of Technology
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November 3, 2021

Shaping and Optimizing Learning in Brain-Machine Interfaces

Amy Orsborn, Clare Boothe Luce Assistant Professor in Departments of Electrical & Computer Engineering and Bioengineering
University of Washington
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October 27, 2021

Systems Analysis of Neural Immune Signaling in Neurodegenerative Diseases

Levi Wood, Assistant Professor of Mechanical and Biomedical Engineering
Georgia Institute of Technology
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October 6, 2021

Deep Brain Stimulation for Mood and Anxiety Disorders: Progress, Challenges, and Solutions

Alik Widge, Assistant Professor
Department of Psychiatry
University of Minnesota

Abstract: Deep brain stimulation (DBS) has been highly effective in the treatment of movement disorders and has undergone multiple clinical trials in psychiatric disorders. There have been promising early results in major depression and obsessive-compulsive disorder, but blinded and randomized trials have not reliably shown a signal. Even in successful trials, a third or more of patients do not respond at all. Part of the problem is that DBS is applied at anatomically defined targets, without a clear understanding of how it affects brain function or how that might map to response or adverse effects. Dr. Alik Widge will overview the state of knowledge, then present a new approach to mechanistic studies, based on a cross-diagnostic approach. He will preview the next generation of DBS trials, which will likely be based on “closed loop” tracking of those mechanistic biomarkers using advanced stimulating and recording implants.

September 29, 2021

Nano- and Micro-Scale Technologies for Mapping Sensory-Driven Activity from the Brain Surface

Daniel L. Gonzales, Postdoctoral Fellow
Weldon School of Biomedical Engineering
Purdue University

Abstract: From nano-scale synapses up to centimeter-sized brain regions, complex computations occur at every spatial scale in the mammalian brain. In networks of thousands of neurons, cellular and subcellular computations govern emergent properties such as behavior, perception, and learning. Therefore, a mechanistic understanding of cognition requires monitoring neural activity across many spatial scales. Here, Dr. Daniel L. Gonzales will discuss efforts to develop nano- and micro-scale technologies that enable multi-scale neurophysiology from the cortical surface in behaving animals. These flexible grids conform to the brain surface and record sensory-driven neural activity across a high-density array of recording pads. Simultaneously, he and his colleagues use silicon shanks or two-photon imaging to capture deep-layer cortical activity. The preliminary results suggest that local field potentials at the cortical surface have a substantial subcellular component, potentially dendritic in origin, that can be mapped on a scalable platform across cortical regions. The work enables a platform for neurophysiology that links activity across spatial scales and informs how subcellular dynamics guide population level outputs during behavior and perception.

September 22, 2021

Defining the Circuit-Based Mechanisms of Psychiatric Disease Vulnerability in Females

Erin. S. Calipari, Assistant Professor
Department of Pharmacology
Vanderbilt Center for Addiction Research
Vanderbilt University

Abstract: The mesolimbic dopamine system is involved in the expression of sex-specific behaviors and is a critical mediator of many psychiatric disease states. While work has focused on sex differences in the anatomy of dopamine neurons and relative dopamine levels, an important characteristic of dopamine release from axon terminals in the nucleus accumbens (NAc) is that it is rapidly modulated by local regulatory mechanisms independent of somatic activity. One of the most potent regulators of dopamine terminal function is through α4β2*-containing nicotinic acetylcholine receptors (nAChRs). While α4β2* regulation of dopamine release is robust in males, this regulatory mechanism is not present in intact female mice. However, ovariectomy restores this regulation in females—indicating that ovarian hormones play a role in this process. Critically, Dr. Calipari and her lab define the molecular mechanism underling these unique sex differences in dopamine regulation. Through a series of experiments with optical and pharmacological approaches, Dr. Calipari finds that estradiol increases dopamine release acutely through direct potentiation of α4β2*-nAChRs on dopamine terminals and following long-term exposure, alters the regulatory properties of these receptors. Finally, using optical and chemogenetic approaches in awake and behaving animals, Dr. Calipari links these sex differences to sex differences in motivated behaviors. Overall, Dr. Calipari shows that circulating ovarian hormones alter the ability of α4β2*-nAChRs on dopamine terminals to modulate dopamine release in the NAc and show that sex differences in the regulation of dopamine neurotransmission underlies sex-dependent behavior. These data have implications for understanding sex differences in basic neurobiology as well as for understanding sex differences in addiction vulnerability for stimulant drugs of abuse.

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The Penn State Center for Neural Engineering is a large, interdisciplinary research group that brings together neural engineering-focused researchers from the Penn State College of Engineering, the College of Medicine, the Materials Research Institute, and the Eberly College of Science. Chartered in June 2007, the center occupies 22,000 square feet of space in the Millennium Science Complex.

Center for Neural Engineering

Millennium Science Complex

The Pennsylvania State University

University Park, PA 16802