Guest Editorial Special Issue of DARPA NEST ... - IEEE Xplore

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 20, NO. 2, MARCH 2012

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Guest Editorial Special Issue of DARPA NEST Proceedings HE IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (TNSRE) is privileged to provide to its readership this special issue devoted to applied neurotechnology efforts presented at the Defense Advanced Research Agency’s (DARPA) first annual Neural Engineering, Science, and Technology (NEST) Forum in November 2010. We acknowledge the DARPA leadership, in particular Dr. Regina Dugan, the DARPA Director, whose vision and support have greatly facilitated the directionality and momentum of the research presented. DARPA’s portfolio of funded efforts in the field of neurotechnology ranges from basic to applied interdisciplinary research and development, with a focus on technological innovations that protect our nation’s warfighters. Many of these efforts involve the use of neural interfaces, which have far reaching operational and clinical applications, spanning the modeling, use, and restoration of cognitive, sensory, motor, and autonomic functions. This TNSRE special issue focuses primarily on the multiscale modeling and restoration of dynamic sensorimotor and memory functions, as well as the development of novel neural interfaces. The efforts presented here establish “proof of principle” concepts that will provide a foundation for future key technological developments in the field of neuroscience. The DARPA Reorganization and Plasticity to Accelerate Injury Recovery (REPAIR) program seeks to develop novel methodologies to model the dynamic brain networks underlying sensorimotor functions, including how brain activity and connectivity adapts over time during learning and following injury. The program ultimately aims to use neural stimulation to bypass or accelerate the recovery of injured brain regions, thus restoring lost sensorimotor function. This TNSRE special issue includes efforts from two of the three research teams funded by the REPAIR program, with each team consisting of research groups from multiple universities and other research organizations who collaboratively work toward achieving the program’s goals. One of these teams, led by a research group at the California Institute of Technology (Caltech), includes efforts by the University of Southern California (USC) to develop accurate biomechanical models of complex limb movements. The degrees-of-freedom of complex limb movements suggests that such models are essential for guiding the development of artificial prosthetic limbs, as well as for modeling and simulating the use of cortical signals to control prosthetic limbs. The paper by Tsianos et al. describes a computational model of muscle and muscle contraction–relaxation. The authors implement a lumped modeling of motor units comprising each fiber type and suggest applications for

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Digital Object Identifier 10.1109/TNSRE.2012.2189499

investigation of optimal motor control and development of rehabilitation strategies. The model system is tested on three different human experimental paradigms, and the predicted and observed model performance demonstrates reproducibility, robustness, and consistency. Additional information on USC’s modeling efforts under the REPAIR program is described by Davoodi et al., who present a suite of interactive software tools known as MusculoSkeletal Modeling Software (MSMS). MSMS can be used to develop arbitrarily complex biological models of human, nonhuman primate, or prosthetic limbs and the objects in the virtual reality task environment with which they can interact. Additionally, the software enables animation of the virtual limb and object movement using motion data from a variety of offline/online sources, including command inputs decoded directly from neural activity. These versatile software utilities can be used in any application that requires precisely timed animations, and are particularly suited for research and rehabilitation of movement disorders following brain injury. Collaborating closely with USC and other members of the Caltech REPAIR team is a research group from Johns Hopkins University (JHU). Benz et al. describe JHU’s efforts in developing an electrocorticography (ECoG)-based brain–machine interface (BMI) for control of a neural prosthesis. ECoG provides wide area coverage of the brain and hence affords the opportunity to study interactions and connectivity between different cortical areas, such as the motor cortex and contiguous sensory cortex. This paper presents a novel method using time-varying dynamic Bayesian networks to quantify the connectivity between the cortical electrodes. The authors created a hand movement decoder that refined the neural output from a decoder constructed with standard spectral based ECoG features. Their results suggest that such network models can be implemented to yield early movement prediction and improve neural network decoding. Such network connectivity analysis is pertinent and useful in studying the organization of brain. How this information can be applied to enhance and refine neural prostheses control is a significant question. In the case of injury to the brain, when cortical information-flow is impaired from one neuroanatomical area to another, connectivity analysis may serve as a useful approach to understand and restore lost cortical function. Another solution to restoring cortical function may be to apply neuroprosthetic stimulation to replace information transmitted among cortical areas, as well as activation of downstream areas that would normally receive this information. The feasibility of this approach is being investigated by another REPAIR team led by a research team at the State University of New York (SUNY) Downstate Medical Center. These efforts include work by Lytton et al., who have developed a computer

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 20, NO. 2, MARCH 2012

model of the cortex to simulate the dynamic loss of information processing following perturbation of cortical network components. Lost activation dynamics in the perturbed model were then repaired using simple repetitive neuroprosthetic stimulation, which was demonstrated to successfully restore the more complex information processing functionality within the in silico brain. Further efforts in the development of neuroprosthetic stimulation by the SUNY Downstate REPAIR team are described by Choi et al. Here, the authors present an electric field model for prediction of the primary somatosensory (S1) cortical field potentials induced by microstimulation (MiSt) of the ventral posterior lateral (VPL) thalamic nucleus. The model maps the computed electric field generated by VPL MiSt to the local field potential (LFP) fluctuations evoked in S1, and is able to predict cortical activity patterns that occur in response to VPL MiSt produced by novel electrode configurations. This model is an important first step in the design of a somatosensory prosthesis, which has the potential to enhance the agility, dexterity, and functional utility of neuroprosthetic design. The SUNY Downstate REPAIR team’s efforts in multiscale modeling of neural activity are reflected in the paper by Graber et al., which describes a test-bed for multiple modalities and signals used in BMI applications. While most conventional BMIs rely on electrical measures of neural activity, the test-bed presented here uses integrated, noninvasive optical (near infrared spectroscopy, or NIRS) and bioelectric (electroencephalography, or EEG) sensing to enable analysis of neural activation and its functional connectivity. This paper presents system design details and measures of effective connectivity obtained using dynamic NIRS tomography. This new test-bed is anticipated to aid in the development of new brain–machine interface approaches and to, more generally, enable the analysis of complex, dynamic neural phenomenology. Similar to the REPAIR program, DARPA’s Restorative Encoding Memory Integration Neural Device (REMIND) program seeks models of complex, dynamic neural activity and demonstrates the use of neural stimulation to restore brain function and its behavioral outputs. The focus of REMIND is on the modeling and restoration of memory function, with collaborative investigations led by research groups at the Wake Forest University School of Medicine and USC. The paper by Hampson et al. describes progress on a nonlinear multi-input/multi-output (MIMO) model of dynamic hippocampal function, which predicts and emulates complex neural firing patterns related to memory encoding. The present paper extends the MIMO model to online delivery of electrical stimulation of the rodent hippocampus to successfully mimic hippocampal firing patterns associated with optimal memory encoding performance. Such hippocampal stimulation during spatial memory encoding was demonstrated to significantly improve behavioral performance on a memory task. In related work under the REMIND program, Song et al. further demonstrate the capability of the MIMO model to predict memories coded by the CA1 region of the hippocampus on a single-trial basis and in real-time. When CA1 function is phar-

macologically blocked, preventing the formation of new longterm memories, the MIMO model’s predictions are used to reinstate memory-related hippocampal activity by driving spatiotemporal electrical stimulation of hippocampal output. The authors outline the design for a hardware implementation of the MIMO model intended to serve as a cognitive prosthesis in behaving animals. Eventually, these technologies may be extended to humans and used to restore memory function in individuals with amnestic disorders such as Alzheimer’s disease. In addition to the DARPA-funded efforts presented at the NEST Forum, the forum also provided the opportunity for both DARPA-funded and non-DARPA-funded postdoctoral fellows to present posters on selected research. One of these non-DARPA-funded efforts is presented in the paper by Presacco et al. In general, BMI research has used invasive neural activity recordings from microelectrode arrays and electrocorticography (ECoG) grids and has focused on controlling computer cursors and upper-limb prosthetic devices. In contrast, Presacco et al. describe the development and implementation of noninvasive scalp electroencephalography (EEG) to provide neural signals for the restoration of gait function. The authors show that by using as few as 12 electrodes to acquire scalp EEG signals associated with the kinematics of human bipedal treadmill walking, gait functionality can be estimated with a high degree of accuracy. This work lays important groundwork for developing noninvasive EEG-based BMI systems for restoration and modification of gait abnormalities in patients with Parkinson’s disease or paralysis. Many of the above efforts have elucidated the need for the development of novel reliable interfaces for chronic use with the central and peripheral nervous systems, a goal addressed by the DARPA Microsystems Technology Office’s (MTO) suite of Reliable Neural-Interface Technologies (RE-NET) programs. One RE-NET effort is described in the paper by Seifert et al., who present a newly developed regenerative multi-electrode interface (REMI) for capturing residual sensory and motor activity in the peripheral nervous system of amputees. This device is able to record neural activity as early as seven days postimplantation and captures increases in neuronal firing rate from 7 to 14 days following REMI implantation. Increased electrical activity, along with robust axonal regeneration around the electrodes, suggests that REMI may provide a viable strategy for stable nerve–electrode interfaces as a means for prosthetic control. Lastly, the clinical ease of application of electrode systems together with the retention of high signal-to-noise is critical for the development of noninvasive neural interface systems. Recent developments in dry and noncontact EEG electrodes that do not require gel or even direct scalp coupling are expected to change the technological framework of building practical noninvasive BMI. DARPA’s Cognitive Technology Threat Warning System (CT2WS) program includes efforts to develop and refine such noninvasive, dry-electrode technologies. One of these efforts is presented in the paper by Chi et al., who demonstrate the feasibility of developing a steady state visual evoked potential (SSVEP) based BMI that uses neural signals recorded by novel noncontact, capacitive electrodes. These sensors utilize

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 20, NO. 2, MARCH 2012

a custom integrated, high-impedance analog front-end to maximize the signal-to-noise ratio of the recorded neural activity. Chi et al. show that performance of dry electrodes is comparable to those of conventional wet electrodes, and suggest that dry and noncontact electrodes, after further refinements, are likely to become important and viable tools for future mobile BMI applications. We commend the quality of these original peer reviewed research papers to the TNSRE readership and the authors for their significant contributions to the field. NITISH V. THAKOR, Guest Editor Department of Biomedical Engineering Johns Hopkins University Baltimore, MD 21205 USA

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DAVID F. MOORE, Guest Editor Tulane University Medical Center Tulane University New Orleans, LA 70112 USA ROBBIN A. MIRANDA, Guest Editor Technology Management Division Schafer Corporation Arlington, VA 22203 USA GEOFFREY S. F. LING, Guest Editor Defense Sciences Office Defense Advanced Research Projects Agency Arlington, VA 22203 USA

Nitish V. Thakor (F’97) received the undergraduate degree from Indian Institute of Technology, Bombay, India, in 1974. He worked as an engineer with Philips India for two years and then received the Ph.D. degree from University of Wisconsin, Madison, in 1981. He is a Professor of Biomedical Engineering. Electrical and Computer Engineering, and Neurology at Johns Hopkins University, Baltimore, MD, and directs the Laboratory for Neuroengineering. He was on the faculty of Northwestern University first (1981–1983) and since then he has been with Johns Hopkins School of Medicine, serving as the Professor of Biomedical Engineering, Electrical and Computer Engineering, and Neurology. His technical expertise is in the areas of neural diagnostic instrumentation, neural microsystem, neural signal processing, optical imaging of the nervous system, rehabilitation, neural control of prosthesis and brain–machine interface. He is the Director of a Neuroengineering Training program funded by the National Institute of Health. He has published 225 refereed journal papers and generated 11 patents and carries out research funded mainly by the NIH, NSF, and DARPA. He has been an active promoter of clinical applications of medical technologies resulting in several small business innovation awards, and co-founding three small medical device companies. Dr. Thakor was the Editor-in-Chief of IEEE TRANSACTIONS ON NEURAL AND REHABILITATION ENGINEERING.. He is a recipient of a Research Career Development Award from the National Institutes of Health and a Presidential Young Investigator Award from the National Science Foundation, and is a Fellow of the American Institute of Medical and Biological Engineering, and Founding Fellow of the Biomedical Engineering Society. He is also a recipient of the Centennial Medal from the University of Wisconsin School of Engineering, Honorary Membership from Alpha Eta Mu Beta Biomedical Engineering student Honor Society, award of Technical Excellence in Neuroengineering from IEEE Engineering in Medicine and Biology Society, and Distinguished Alumnus Award as well as Distinguished Service Award from Indian Institute of Technology, Bombay, India, and Centennial Medal from University of Wisconsin, Madison School of Engineering.

David F. Moore is a certified Neurologist and Vascular Neurologist (ABPN) with extensive expertise in neuro-imaging, fluid dynamics, bioinformatics, and mathematical biology. He has previously carried out investigations involving transcranial Doppler, positron emission tomography, arterial spin tagging, laser Doppler flow studies, magnetic resonance elastography, peripheral vessel M-mode and B-mode ultrasound scanning, analysis of neuro-imaging data, gene microarray data and UNIX system administration. He trained at Imperial College, (London, U.K.), New York Hospital and the National Institutes of Health (Bethesda, MD). He served as Deputy Director for Research at The Defense and Veterans Brain Injury, headquartered at Walter Reed Army Medical Center, Washington DC, the TBI Scientific Advisor to the Defense Centers of Excellence. Currently, he is Visiting Scientist at the Institute of Soldier Nanotechnology, MIT, Adjunct Professor of Physiology Case Western University, Vascular Neurologist and Clinician Investigator Department of Neurology, Tulane University, and Medical Director Aeromics, Inc.

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Robbin A. Miranda received the undergraduate degrees in biology (with a concentration in neuroscience) and music from Duke University, Durham, NC, in 2002. In 2007, she received the Ph.D. degree in neuroscience from Georgetown University, Washington DC, where she stayed as a Postdoctoral Research Associate until 2008. She is a Research Scientist/Engineer at Schafer Corporation’s Technology Management Division and has provided technical scientific support toward various neuroscience programs since 2008. Her technical expertise is in the field of interdisciplinary neuroscience, including laboratory research experience in cellular/molecular neuroscience and neurophysiology, as well as behavioral, electroencephalography, and functional magnetic resonance imaging studies in humans. She has particular expertise in the development, implementation, and data analysis of behavioral and event-related potential studies in humans. As a recipient of a three-year NSF Graduate Research Fellowship, she utilized these techniques as part of her graduate thesis work at Georgetown to investigate the neural correlates of declarative and procedural memory in the processing of music and language.

Geoffrey S. F. Ling is a Program Manager at the Defense Advanced Research Projects Agency (DARPA). His portfolio includes revolutionizing prosthetics, fracture putty, preventing violent explosive neuro trauma (PREVENT), restoring plasticity and acceleration of injury recovery (REPAIR), predicting health and disease (PHD), and others. He is also Professor and Interim Chairman of Neurology at the Uniformed Services University of the Health Sciences, Bethesda, MD, Director of Neuro Critical Care at Walter Reed Army Medical Center, and Attending Neuro Critical Care Physician at Johns Hopkins Hospital. He deployed to both Afghanistan (2003) and Baghdad, Iraq (2005), and additionally has done shorter missions as a member of the Joint Chiefs of Staff’s Gray Team. He has published over 150 peer-reviewed journal articles, reviews, and book chapters, including the traumatic brain injury chapter in Cecil’s Textbook of Medicine. Dr. Ling is a member of the American Neurological Association, a fellow of the American Academy of Neurology, “A” designated in his medical specialty, and is a member of the Order of Military Medical Merit. He serves on the NIH-NINDS Advisory Council, the executive board of the Neuro Critical Care Society, and other similar positions.

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