Symposia speakers

(updated on April 5th, 2017)

Symposium #1: Bioelectronic Medicine (Metin Akay, Dominique Durand)

Cynthia Chestek

Short biography

Cynthia A. Chestek received the B.S. and M.S. degrees in electrical engineering from Case Western Reserve University in 2005 and the Ph.D. degree in electrical engineering from Stanford University in 2010. From 2010 to 2012, she was a Research Associate at the Stanford Department of Neurosurgery with the Braingate 2 clinical trial. In 2012 she became an assistant professor of Biomedical Engineering at the University of Michigan, Ann Arbor, MI, where she runs the Cortical Neural Prosthetics Lab. She is the author of 30 full-length scientific articles. Her research interests include high-density interfaces to the nervous system for the control of multiple degree of freedom hand and finger movements.

Presentation title and abstract

Neural Interfaces for Controlling Finger Movements

 

Dominique M. Durand

Short biography

Dominique M. Durand is E.L. Linsedth Professor of Biomedical Engineering and Neurosciences and Director of the Neural Engineering Center at Case Western Reserve University in Cleveland, Ohio. He received an engineering degree from Ecole Nationale Superieure d’Electronique, Hydrolique, Informatique et Automatique de Toulouse, France in 1973. In 1974, he received a M.S. degree in Biomedical Engineering from Case Reserve University in Cleveland OH., worked several years at the Addiction Research Foundation of Toronto, Canada and in 1982 received a Ph.D. in Electrical Engineering from the University of Toronto in the Institute of Biomedical Engineering. He received an NSF Young Investigator Presidential Award as well as the Diekhoff and Wittke awards for graduate and undergraduate teaching and the Mortar board top-prof awards at Case Western Reserve University. He is a Fellow of the American Institute for Medical and Biomedical Engineering. His research interests are in neural engineering and include computational neuroscience, neurophysiology and control of epilepsy, non-linear dynamics of neural systems, neural prostheses and applied magnetic and electrical field interactions with neural tissue. He is the founder and editor-in-chief of the Journal of Neural Engineering. He has obtained funding for his research from the National Science Foundation, the National Institutes of Health and private foundations. He has published over 100 articles and he has consulted for many biotechnology companies and foundations.

Presentation title and abstract

Low noise recording: electrode fabrication and electronics

 

Mario Romero-Ortega

Short biography

Dr. Romero is an Associate Professor of Bioengineering at the University of Texas at Dallas (UTD) and adjunct faculty in the Surgery department at the University of Texas Southwestern Medical Center (UTSW), the UTA Research Institute (UTARI) and Partner Researcher at the University of Wollongong, Australia. He received his doctorate in Neuroscience from Tulane University and postdoctoral training from UTSW as Associate Member of the Christopher Reeve Paralysis Foundation Research Consortium on Spinal Cord Injury. He has served as Director of the Regenerative Neurobiology Research Division at Texas Scottish Rite Hospital and Assistant Professor of Neurology and Plastic Surgery at UTSW. He serves as Associate Editor of Frontiers in Neuroengineering and as Founder and Chief Scientific Officer for Nerve Solutions Inc, a company that commercializes the Biosynthetic Nerve Implant and the NeuroBlock devices developed in his laboratory. He is the recipient of the 2014 UTA College of Engineering Excellence in Research Award, the 2013 TechFortWorth Impact Award and “Ten Most Promising Life Science Company Award”, and the 2013 Tech Titans Award in Technology Innovation.

Presentation title and abstract

Bioelectronic Applications of Wireless Shape Memory Cuff Electrodes

 

Mikhail Lebedev

Short biography

Mikhail Lebedev received a Master’s degree in Physics from Moscow Institute of Physics and Technology (1986), and a PhD in Neurobiology from the University of Tennessee, Memphis (1995). He conducted research on human sensorimotor integration with Victor Gurfinkel (1986-1991). Since 1991, Lebedev is involved in neurophysiological studies. He investigated sensorimotor neurophysiology in primates with Randal Nelson (1991-1995) and in rodents with Mathew Diamond (1995-1997). In 1997-2002, Lebedev worked with Steven Wise. These studies elucidated cortical mechanisms of motor control, spatial attention, working memory and perceptions. Since 2003, Lebedev works with Miguel Nicolelis at Duke University; he supervises the primate laboratory. The major focus of this research is on brain-machine interfaces (BMIs). These are BMIs for reaching and grasping, BMIs that reproduce bipedal locomotion patterns, as well as sensorized BMIs that both extract motor command from the brain and deliver sensory information back to the brain. Lebedev is on the editorial board of Journal of Neural Engineering, Frontiers in Neuroscience, PLOS ONE and several other journals. He also an editor of several books and special issues.

Presentation title and abstract

Augmentation of brain functions: lessons from a Frontiers research topic

 

Symposium #2: Cognitive Neural Engineering (Paul Sajda)

Jean Vettel

Short biography

Jean M Vettel earned her PhD in Cognitive Neuroscience from Brown University, funded by an NSF Graduate Fellowship (2004-2007) and a DoD SMART Fellowship (2007-2009), following a lab position at Washington University in St. Louis and BA from Carnegie Mellon. Since Sept 2009, she has been a civilian neuroscientist at Army Research Laboratory and leads the Brain Structure-Function Couplings research area in the Army’s Translational Neuroscience mission program in the Future Soldier Technologies Division. In support of ARL’s Open Campus, Dr. Vettel was appointed as adjunct faculty at University of California, Santa Barbara in 2014 and a visiting scholar at University of Pennsylvania in 2015. Her collaborative research investigates methods to quantify brain connectivity that accounts for task performance variability both within- and between-individuals and then uses these brain metrics in novel approaches to revolutionize the way that human brains and technology interact. Dr. Vettel has over 40 publications and reports and 50 conference presentations. She has conducted over 10 media interviews and given numerous invited briefs and talks to communicate the value of neuroscience and human sciences research for the Army across a wide range of audiences.

Presentation title and abstract

Using Structural and Functional Connectivity Methods to Push the Boundaries of Brain-behavior Relationships

 

Paul Sajda

Short biography

Paul Sajda is Professor of Biomedical Engineering, Electrical Engineering and Radiology at Columbia University. He is Director of the Laboratory for Intelligent Imaging and Neural Computing (LIINC) and Co‐Director of Columbia’s Center for Neural Engineering and Computation (CNEC). His research focuses on neural engineering, neuroimaging, computational neural modeling and machine learning applied to the study of rapid decision making in the human brain. Prior to Columbia he was Head of The Adaptive Image and Signal Processing Group at the David Sarnoff Research Center in Princeton, NJ. He received his B.S. in Electrical Engineering from MIT and his M.S. and Ph.D. in Bioengineering from the University of Pennsylvania. He is a recipient of the NSF CAREER Award, the Sarnoff Technical Achievement Award, and is a Fellow of the IEEE and the American Institute of Medical and Biological Engineering (AIMBE). He is the current the Editor‐in‐Chief for the IEEE Transactions in Neural Systems and Rehabilitation Engineering and Chair of the IEEE Brain Initiative. He is a founder/co-founder of several neurotechnology start-up companies.

Presentation title and abstract

BCI-based Neurofeedback for Improved Human-Machine Interaction

 

Tzyy-Ping Jung

Short biography

Tzyy-Ping Jung is currently the Co-Director of Center for Advanced Neurological Engineering, Associate Director of the Swartz Center for Computational Neuroscience and an Adjunct Professor of Department of Bioengineering at University of California San Diego, CA, USA. He is also an Adjunct Professor of Department of Electrical Engineering and Department of Computer Science at National Chiao Tung University, Hsinchu, Taiwan. In addition, he is an Adjunct Professor at School of Precision Instrument and Opto-electronic Engineering, Tianjin University, Tianjin, China. Dr. Jung established transformative techniques for applying blind source separation to decompose multichannel EEG/MEG/ERP and fMRI data and was elevated to an IEEE Fellow for his contributions to blind source separation for biomedical applications in 2015. Dr. Jung’s research emphasis has been placed on integration of the cognitive sciences, basic and clinical neuroscience, and bioengineering.  Dr. Jung’s work is truly interdisciplinary. He has published many well-cited articles in the prestigious scientific journal such as Science, PNAS,  PLoS Biology, and J. Neurosciences, engineering journals such as Proceedings of the IEEE, IEEE Trans Biomed. Eng., and clinical journal such as European J. Nuclear Medicine and Molecular Imaging and Investigative Ophthalmology & Visual Science.

Presentation title and abstract

Recent Progress in Real-world Neuroimaging and Brain-Computer Interface Technologies

 

Symposium #3: Neurorehabilitation and Prosthetics (Dario Farina, Ning Jiang)

Cipriani Christian

Short biography

Christian Cipriani is a Professor of Bioengineering and Head of the Artificial Hands Area at The BioRobotics Institute, Scuola Superiore Sant’Anna (SSSA), Pisa, Italy. He received the M.Sc. degree in electronic engineering from the University of Pisa, Italy, in 2004 and the Ph.D. in BioRobotics from the IMT Institute for advanced studies, Lucca, Italy in 2008. He has been working at the SSSA since 2005 and –as a Visiting Scientist– at the University of Colorado Denver | Anschutz Medical Campus, in 2012, within many national and international research projects on prosthetics and robotics. His current field of research is (bio)mechatronics applied to the area of upper limb prosthetics. He is interested in mechatronic, controllability and sensory feedback issues of dexterous robotic hands to be used as thought-controlled prostheses. On these topics he has authored 100+ peer reviewed scientific papers, 50+ of which on international journals in the field of prosthetics and rehabilitation robotics. He also filed eight patents on related fields and founded a spin-off company of the Scuola Sant’Anna – Prensilia. Dr. Cipriani is the recipient of several awards including a Starting Grant from the European Research Council in 2015, an early career grant by the Italian Ministry of Research in 2011 (FIRB 2010 program), a Fulbright Research Scholar fellowship in 2011 and the d’Auria Award from the Italian Robotics and Automation Association, in 2009.

Presentation title and abstract

Non-invasive, temporally discrete feedback improves grasp control of closed-loop myoelectric transradial prostheses

 

Dario Farina

Short biography

Dario Farina received Ph.D. degrees in automatic control and computer science and in electronics and communications engineering from the Ecole Centrale de Nantes, Nantes, France, and Politecnico di Torino, Italy, in 2001 and 2002, respectively. He is currently Full Professor and Chair in Neurorehabilitation Engineering at the Department of Bioengineering of the Imperial College London, UK. He has previously been Full Professor at Aalborg University, Aalborg, Denmark, (until 2010) and at the University Medical Center Göttingen, Georg-August University, Germany, where he founded and directed the Department of Neurorehabilitation Systems (2010-2016). Among other awards, he has been the recipient of the 2010 IEEE Engineering in Medicine and Biology Society Early Career Achievement Award, in 2012 he has been elected Fellow of the American Institute for Medical and Biological Engineering (AIMBE), and in 2014-2015 he has been Distinguished Lecturer IEEE. He has also been awarded an Advanced Grant by the European Research Council (2011-2016). His research focuses on biomedical signal processing, neurorehabilitation technology, and neural control of movement. Within these areas, he has (co)-authored approximately 400 papers in peer-reviewed Journals, which have currently received cumulatively >17,000 citations, and over 500 among conference papers/abstracts, book chapters, and encyclopedia contributions. Professor Farina has been the President of the International Society of Electrophysiology and Kinesiology (ISEK) (2012-2014) and is currently the Editor-in-Chief of the official Journal of this Society, the Journal of Electromyography and Kinesiology. He is also currently an Editor for IEEE Transactions on Biomedical Engineering and the Journal of Physiology, and previously covered editorial roles in several other Journals.

 Presentation title and abstract

Control of Active Prostheses by Decoding Spinal Motor Neuron Behavior

Abstract: Alpha motor neurons are the final common pathway of the neuromuscular system since they receive synaptic input from the whole nervous system and convert it into the ultimate neural code of movement. Recently, the interfacing (bioelectrodes) and processing methods for identifying the output of motor neuron pools from interference electromyogram (EMG) signals have been advanced substantially. The identification of motor neuron population behaviours has opened the possibility of using this information for man-machine interfacing for the control of active prostheses. In this view, the muscles act as biological amplifiers of the activities of the output circuitries of the spinal cord, either through natural innervation or surgical targeted reinnervation. This combination of surgical procedures, advanced decoding, and mapping into effective commands constitutes a direct neural interface (since it decodes the efferent nerve activity). The talk will overview the technology for motor neuron interfacing and the potential of motor neuron recording technology for prosthesis control.

 

Marco Santello

School of Biological and Health Systems Engineering, Arizona State University, USA. http://faculty.engineering.asu.edu/santello

Short biography

Marco Santello received a Bachelor in Kinesiology from the University of L’Aquila, Italy, in 1990 and a Doctoral degree in Sport and Exercise Science from the University of Birmingham (U.K.) in 1995. After a post-doctoral fellowship at the Department of Physiology (now Neuroscience) at the University of Minnesota, he joined the Department of Kinesiology at Arizona State University (ASU) (1999-2010). He is currently Professor of Biomedical Engineering, Director, and Harrington Endowed Chair at the School of Biological and Health Systems Engineering. His main research interests are motor control, learning, and biomechanics of object grasping and manipulation, neural control of hand muscles, haptics, and multisensory integration. His laboratory uses complementary research approaches, ranging from intramuscular electromyography and transcranial magnetic stimulation to motion tracking, kinetic analysis, and virtual reality environments. Dr. Santello’s research has applications to rehabilitation of sensorimotor hand function, prosthetics, and biologically-inspired robotics. Dr. Santello has published his work (100+ publications) in neuroscience and engineering journals. His research has been supported by research awards from the National Institutes of Health, the National Science Foundation, DARPA, the Whitaker Foundation, The Mayo Clinic, and Google. He has served as regular member of the Motor Function, Speech, and Rehabilitation Study Section at the National Institutes of Health, and currently serves as Associate Editor for Neuroscience and Biomedical Engineering, and member of the Editorial Board of the Journal of Assistive, Rehabilitative and Therapeutic Technologies. He is a member of the Society for Neuroscience, the Society of Neural Control of Movement, and IEEE.

Presentation title and abstract

Dexterous manipulation as a model to study sensorimotor learning and function

Abstract: Sensorimotor control and adaptation rely on complex interactions between reflexes and anticipatory control mechanisms. Through repeated exposure to mechanical interactions with the environment, the sensorimotor system learns to expect sensory consequences arising from motor actions, whereas reflexes would intervene when a discrepancy occurs between expected and actual sensory feedback. We have tested this theoretical framework in the context of grasping and dexterous manipulation using tasks that allow subjects to explore and choose relations between digit forces and positions. This experimental approach has revealed the phenomenon of digit force-to-position modulation, through which trial-to-trial variability in digit placement is compensated for by digit force modulation. However, this phenomenon has also opened up questions about what the neural representations underlying learned manipulation are, and in particular the relation between high-level (task) and low-level (effectors) representations. To address this question, we have designed experiments that combines behavioral and electrophysiological approaches (non-invasive brain stimulation and electroencephalography) to identify cortical mechanisms underlying learning and execution of dexterous manipulation. I will review experimental evidence supporting the following notions: (1) Interactions with objects grasped at unconstrained versus constrained contacts are mediated by different sensorimotor mechanisms, (2) high-level representations of learned manipulations acquired through sensorimotor adaptation allow the central nervous system to compensate for motor variability, but (3) high-level representations can also limit the extent to which a given learned manipulation can transfer to different task contexts. These findings extend our understanding of the functional role of cortical areas within the grasp network and have implications for neuroprosthetics, robotics, and rehabilitation of sensorimotor function.

 

Ning Jiang

Short biography

Ning Jiang (S’02–M’09-SM’14) received the B.S. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 1998, and the M.Sc. and Ph.D. degrees in engineering from the University of New Brunswick, Fredericton, NB, Canada, in 2004 and 2009, respectively. He was Research Assistant Professor at Aalborg University, Denmark from April 2009 to August 2010, a Marie Curie Fellow at the Strategic Technology Management, Otto Bock Healthcare GmbH, Germany from September 2010 to October 2012, and a research scientist with Department of Neurorehabilitation Engineering, University Medical Center Göttingen, Georg-August University, Göttingen, Germany from November 2012 to April 2015. Since May 2015, he is an Assistant Professor at the Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada. He is currently the Director of Engineering Bionics Lab at the University of Waterloo. His research interests include signal processing of electromyography, advanced prosthetic control, neuromuscular modeling, electroencephalogram processing and brain–computer interfaces for neurorehabilitation. He is currently Associated Editor for IEEE Journal of Biomedical and Health Informatics, the Brain Computer Interface, and a guest editor for Frontiers in Neuroscience.

Presentation title and abstract

Taking Advanced Myoelectric Control Out of the Lab: Towards Robust Myoelectric Signal Processing for Prosthetic Control

Abstract: Advance myoelectric signal processing has been investigated for prosthetic control, particularly for multi-function upper limb prosthesis with great success in research laboratories. Unfortunately, the efforts of translating these success in practical, clinical and commercially viable products has been largely disappointing. There are large number of factors to be considered, and many of them are related to the way EMG signals are processed. In particular, many popular and successful EMG processing algorithms are not robust against factors in everyday life, such as socket/electrode shift, varying limb/trunk positions, changing muscular contraction levels, last but not the least, user adaptation. In this talk, I am going to present several studies conducted by me and my collaborators around the world, aiming at addressing such robustness issues in EMG processing and myoelectric control. I will conclude the talk with a discussion on future research directions in advance prosthetic control in general.

 

Symposium #4: Neuroimaging (Bin He)

Zhi-Pei Liang

Short biography

Zhi-Pei Liang received his Ph.D. degree in Biomedical Engineering from Case Western Reserve University in 1989.  He subsequently joined the University of Illinois at Urbana-Champaign (UIUC) first as a postdoctoral fellow (working with the late Nobel Laureate Paul Lauterbur) and then as a faculty member in the Department of Electrical and Computer Engineering. Dr. Liang is currently the Franklin W. Woeltge Professor of Electrical and Computer Engineering; he also co-chairs the Integrative Imaging Theme in the Beckman Institute for Advanced Science and Technology.   Dr. Liang’s research is in the general area of magnetic resonance imaging and spectroscopy, ranging from spin physics, signal processing, machine learning, to biomedical applications. Research from his group has received a number of recognitions, including the Sylvia Sorkin Greenfield Award (Medical Physics, 1990), Whitaker Biomedical Engineering Research Award (1991), NSF CAREER Award (1995), Henry Magnuski Scholar Award (UIUC, 1999), University Scholar Award (UIUC, 2001), Isidor I. Rabi Award (International Society of Magnetic Resonance in Medicine, 2009), IEEE-EMBC Best Paper Awards (2010, 2011), IEEE-ISBI Best Paper Award (2010, 2015), Otto Schmitt Award (International Federation for Medical and Biological Engineering, 2012), and Technical Achievement Award (IEEE Engineering in Medicine and Biology Society, 2014). Dr. Liang is a Fellow of the IEEE, the International Society for Magnetic Resonance in Medicine, and the American Institute for Medical and Biological Engineering. He was elected to the International Academy of Medical and Biological Engineering in 2012. Dr. Liang served as President of the IEEE Engineering in Medicine and Biology Society from 2011-2012 and received its Distinguished Service Award in 2015.

Presentation title and abstract

Visualizing Brain Metabolism: A Marriage of Spin Physics and Machine Learning for Brain Mapping

 

Qingming Luo

Short biography

Dr. Qingming Luo is the Vice-President of Huazhong University of Science and Technology and Executive Deputy Director of Wuhan National Laboratory for Optoelectronics. He is an elected Fellow of The American Institute for Medical and Biological Engineering (AIMBE), The International Society for Optics and Photonics (SPIE), The Institution of Engineering and Technology (IET) and The Optical Society (OSA). His research interests focus primarily on multi-scale optical bioimaging and cross-level information integration. He is currently leading the project Visible Brain-wide Networks at single-neuron resolution and the Chief Scientist of National Major Scientific Instruments & Equipment’s Development Project “Instrument Development and Application Demonstration of the Micro-Optical Sectioning Tomography System”. He created “the most detailed three-dimensional map of all the connections between the neurons in a complete mouse brain” and “demonstrated the first long-range tracing of individual axons in the mouse brain”. Dr. Luo holds 60 patents and has co-authored more than 200 papers in peer-reviewed journal, including Science, Nature Cell Biology, Nature Methods, Nature Communications, PNAS, Optics Letters, Optics Express and Journal of Biomedical Optics. He won the Cheung Kong Professorship of Ministry of Education of China in 1999, the National Science Fund for Distinguished Young Scholars in 2000, the second-place prize in State Natural Sciences Award in 2010, China’s Top Ten Major Scientific Progress, and the second-place prize in State Technological Invention Award in 2014.

Presentation title and abstract

Brainsmatics: Deciphering Brain Function with Brain-wide Networks

Abstract: Deciphering the fine morphology and precise location of neurons and neural circuits are crucial to enhance our understanding of brain function and diseases. Traditionally, we have to map brain images to coarse axial-sampling planar reference atlases to orient neural structures. However, this means might fail to orient neural projections at single-cell resolution due to position errors resulting from individual differences at the cellular level. In this talk, we propose a new approach: BRAINSMATICS, which refers to the integrated, systematic approach of measuring, analyzing, managing and displaying brain spatial data. Taking the Micro-Optical Sectioning Tomography (MOST) serial techniques as the core, we have developed a multidisciplinary complete technical system of Visible Brain Network (VBN), including whole-brain samples preparing, whole-brain optical imaging as well as massive brain-image processing and analyzing. So far, we have acquired the world’s first 3D structure atlas of whole mouse brain at single-neuron resolution; have achieved tracing axonal pathways in the mouse brain without interruption for the first time; have firstly dissected neural structures with anatomical annotation at single-neuron resolution; have revealed the mechanism of fluorescent signal change in resin-embedded sample; have realized the automatic tracing and reconstruction of neuronal populations with dense dendrites. These achievements cover optics, biology, chemistry, mathematics, computer science and biomedical engineering and constitute the complete VBN technical system, which enables us to visualize and analyze the fine structures and connectivities of any area at different spatial scales in rodent brain, with unprecedented single-neuron resolution. The techniques have the advantages of high resolution, high throughput and long-time stability. With the brain-spatial information of neuron types, neural circuits, vascular network and 3D fine brain atlas, we believe that BRAINSMATICS makes it possible to better decipher the brain function and disease.

 

Bin He

Short biography

Bin He is a Distinguished McKnight University Professor of Biomedical Engineering, Medtronic-Bakken Endowed Chair for Engineering in Medicine, director of the Institute for Engineering in Medicine, director of the Center for Neuroengineering, director of NSF IGERT Neuroengineering Training Program, and director of NIH Neuroimaging Training Program at the University of Minnesota. His research interest includes brain mapping, brain-computer interface, neuromodulation, cardiac electric imaging, and impedance imaging. Dr. He is the sole editor of the textbook entitled “Neural Engineering” (2005, 2013), and is the Chair of NSF Workshop on mapping and engineering the brain, IEEE EMBS BRAIN Grand Challenges Conference, and 2013 EMBS Neural Engineering Conference held in San Diego which attracted 800+ participants. He is the recipient of the 2017 IEEE Biomedical Engineering Award, the Academic Career Achievement Award and Distinguished Service Award from the IEEE EMBS, the Established Investigator Award from the American Heart Association, and Outstanding Research Award from the International Federation of Clinical Neurophysiology. Dr. He served as President of IEEE EMBS (2009-2010) and is Chair-Elect of the International Academy of Medical and Biological Engineering. He serves as the Editor-in-Chief of IEEE Transactions on Biomedical Engineering and is a Member of the NIH BRAIN Multi-Council Working Group.

Presentation title and abstract

Dynamic Neuroimaging of Brain Activity and Functional Connectivity

 

Symposium #5: Neurocomputation (Jose Principe)

Il Memming Park

Short biography

Assistant Professor, Department of Neurobiology and Behavior, Stony Brook University, http://www.memming.com/

Presentation title and abstract

Inference, Interpretation, and Control of Low-dimensional Nonlinear Dynamic model

Abstract: A central challenge in neural engineering is to understand the dynamics of neural system and its relation to sensory, motor, and cognitive information processing. For low-complexity tasks that require information processing in the order of a few seconds, we hypothesize that the implementation of computation can be represented as a slow continuous nonlinear dynamical system. Inferring this underlying dynamical law from multichannel neural recordings can help us understanding how the brain processes information. We will show novel statistical methods to recover and interpret low-dimensional collective neural dynamics. We will demonstrate how variational Bayesian inference can be applied to modeling mixed point process and continuous observations. The locally linear nature of the inferred model allows intuitive interpretation of the system, as well as a feedback control of the neural state.

 

Joe Francis

Short biography

University of Houston http://joefrancislab.com/

Presentation title and abstract

Making use of reward related signals in the primary sensorimotor cortex toward autonomously updating Brain Machine Interfaces

Abstract: We recently discovered reward related modulation of the primary motor cortex as well as the primary somatosensory cortex. Both of these regions are currently being implanted for human BMI systems. Thus, we can use this reward related information from these sites in combination with reinforcement learning principles to update our decoding algorithms that translate the user’s intentions into actions as necessary based on performance. We will report on our progress toward determining what information is actually being represented in the sensorimotor cortex, such as state value, or reward prediction error etc. We will also describe methods to use such information toward autonomously updating BMIs in real world situations.

 

John A. White

Short biography

Boston University

Presentation title and abstract

The Unbalanced State of Cortical Networks

Abstract: High statistical variability is a hallmark of neuronal firing patterns in neocortex. This variable activity is hypothesized to be driven by input that is balanced, with membrane potential held near spike threshold by equally weighted, and fluctuating, excitation and inhibition. Models with balanced activity express voltage-dependent statistical and spectral properties that can be used to test for the presence of balanced activity. Using visually-guided intracellular recordings of layer 2/3 somatosensory neurons in awake mice, we compared the properties of spontaneous voltage fluctuations with those generated in balanced models. In both pyramidal cells and interneurons, we found that excitatory and inhibitory inputs are not balanced. Inputs appear electrotonically distant from the soma, but evoke voltage-dependent changes in apparent postsynaptic input resistance that amplify depolarizations nonlinearly. We speculate that such unbalanced inputs give rise to more flexible network responses under awake but unstimulated conditions.

Symposium #6: Brain Computer Interface (Lei Ding)

Bo Hong

Short biography

Dr. Bo Hong received his Ph.D. of biomedical engineering from Tsinghua University in 2001. From 2004 to 2005, he was a visiting scientist in the department of biomedical engineering and the center for neural engineering at Johns Hopkins University. He is now a professor with department of biomedical engineering, School of Medicine, and investigator of McGovern Institute for Brain Research at Tsinghua University. He is currently the Associate Editor of IEEE Transactions on Neural Systems and Rehabilitation Engineering. His research interests are brain computer interface and neural information decoding of auditory and speech system. He has published more than 60 papers on leading journals in the field, including in Nature Neuroscience, NeuroImage, Journal of Neural Engineering, etc. He has also been invited to give talks at Kavil Symposium on Frontiers of Science, Workshop on Advances in ECoG at Society for Neuroscience Meeting, NIH Neural Interface Conference, Cold Spring Harbor Francis Crick Symposium, Annual Meeting of the Organization for Human Brain Mapping, etc.

Presentation title and abstract

Mimimally invasive brain computer interface: from bench to bed

Abstract: Intracranial EEG (iEEG) is a promising technology for high performance brain-computer interfaces (BCIs). To implement practical iEEG based BCIs, minizing the invasiveness of the electrode implantation is critical. Here we proposed a new BCI paradigm that utilizes the attentional modulation of visual motion response over middle temporal (MT) area. Structural and functional MRI were employed to localize the visual motion processing regions. Subdural or epidural electrodes within these regions showed both motion-onset VEP and increased high gamma power, when the visual motion stimuli were attended. Based on this finding, an online BCI speller were implemented and tested on epilepsy patients using only one MT electrode for each patient. With the data from macaque monkey implanted with customized epidural electrodes, the long-term safety and signal stability were further proved. The progress of clinical trial on human subject will be reported and discussed, along with the potential extension of this miminally invasive BCI protocol to speech BCI.

 

Donatella Mattia

Short biography

Donatella Mattia received her MD degree at University of Rome “Sapienza”, Italy in 1987. In 1991, she became neurologist at the same University. She received the PhD degree in “Physiopathology of movement disorders” in 1996, at the Department of Neuroscience, “Sapienza” University of Rome. During her PhD program, she was a research fellow at the Montreal Neurological Institute and Department of Neurology and Neurosurgery, McGill University, Montréal Québec, Canada. She is currently the director of the Laboratory of Neuroelectrical Imaging and Brain Computer Interface, at the Fondazione Santa Lucia, IRCCS, Rome, Italy (www.hsantalucia.it). Research interests are focused on advanced biosignal (EEG) signal processing method application to investigate the basis of human motor and cognitive function and the design and validation of EEG-based BCI technology in the field of AT and neurorehabilitation (after stroke).

Presentation title and abstract

BCI in neurological rehabilitation: where are we?

 

Galit Pelled

Short biography

Galit Pelled is an Associate Professor at The Russell H. Morgan Department of Radiology and Radiological Science in Johns Hopkins School of Medicine and Kennedy Krieger Institute. Galit Pelled completed her doctoral thesis at 2004 in the Department of Neurobiology in The Hebrew University (Jerusalem, Israel) under the mentoring of Drs. Hagai Bergman and Gadi Goelman. Galit Pelled accomplished her postdoctoral training at Dr. Alan Koretsky’s lab at the NINDS, NIH (Bethesda, MD). At 2008 she was recruited to Johns Hopkins and KKI as an Assistant Professor of Radiology and neuroscience, and she has been promotes to the Associate Professor rank at 2014. Galit Pelled’s research focuses on understanding how injury changes neuronal connections, how neuromodulation impacts these changes, and how these changes affect recovery. They gain a comprehensive appreciation of neuroplasticity and how it translates into behavior by probing the system at the single neuron level all the way up to the whole organism level. Her research has been published in high impact-scientific journals, and she has been NIH funded continuously since 2005. Her funding includes an F32 fellowship as a postdoc, 2 R01s (including an R01 renewal (2015-2020)) and the prestigious R01 NIH EUREKA award. Her research has also been supported by private foundation and donations.

Presentation title and abstract

From fish navigation to neuromodulation

Abstract: Major advances in molecular and synthetic biology have revolutionized the capability to control cell excitability in living organisms. Yet, the majority of the technologies available today that manipulate cellular function in a cell- and spatio-temporal-specific manner demand the use of optics, drugs, radio-wave heating or ultrasound. The quest to identify genes responsible for controlling cellular function by electromagnetic fields (EMF) that penetrate deep tissue non-invasively is only in its infancy. To embark upon this challenge, we investigated the potential of an alternative and novel method to remotely control cellular function through the transmission of non-invasive, electromagnetic fields (EMF). While it is known that various aquatic species use electromagnetic fields for orientation and navigation, the cellular mechanism by which this is accomplished remains unknown. One of these species is the Kryptopterus bicirrhis (glass catfish). Using expression cloning in Xenopus laevis oocytes, we have identified a single gene from the K. bicirrhis that, once expressed in the oocytes, produces changes in the oocyte’s membrane current when wirelessly activated. Using bioinformatics approaches, we found that this gene is a putative membrane associated protein, and has never been characterized before in any other organism. We term this gene the electromagnetic perceptive gene (EPG). We have expressed the EPG in mammalian cells, neuronal cultures and in the rat brain. Our data demonstrates that wireless activation of the EPG in neuronal culture results in significant changes in neuronal excitability. Moreover, the data shows that EPG can be expressed in a specific cellular population and in a specific location in the rodent brain. We anticipate that this discovery has the potential to transform the neuroscience field, by providing a tool that offers non-invasive, cellular-specific, temporal-specific and region-specific stimulation.