| PI Name | Institution | Link | Description | Techs-used | # people | Area of Focus 1 | Area of Focus 2 | Area of Focus 3 | Area of Focus 4 | Recent Publication 1 | Recent Publication 2 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Caltech | Our behaviors are dictated by our intentions, but we have only recently begun to understand how the brain forms intentions to act. The posterior parietal cortex is situated between the sensory and the movement regions of the cerebral cortex and serves as a bridge from sensation to action. We have found that an anatomical map of intentions exists within this area, with one part devoted to planning eye movements and another part to planning arm movements (Andersen and Buneo 2002). The action plans exist in a cognitive form, specifying the goal of the intended movement. Current studies involve examining decision making, stages in motor planning, coordinate transformations for sensory guided movements and motion perception. In recent years we have also used the findings from these animal studies to develop brain-machine interfaces using intention signals recorded from the posterior parietal cortex of tetraplegic human participants (Aflalo et al. 2015). | EGS | 19 | R. A. Andersen, T. Aflalo. "Preserved cortical somatotopic and motor representations in tetraplegic humans" 2022 Current Opinion in Neurobiology 74:102547. <pdf version> | S. K. Wandelt, S. Kellis, D. A. Bjånes, K. Pejsa, B. Lee, C. Liu, R. A. Andersen. "Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human" 2022 Neuron 110, 1-11. doi: https://doi.org/10.1016/j.neuron.2022.03.009 <pdf version> Neuron Preview: "Getting a grasp on BMIs: Decoding prehension and speech signals." L. Edmondson and H. Saal. <pdf version> | ||||||
MIT | “Our lab works at the interface of materials science, electronics, and neurobiology with the goal of advancing the understanding and treatment of disorders of the nervous system. We design, synthesize, and fabricate optoelectronic and magnetic devices that manipulate and record neuronal activity and development.” | Opto-electronicsMultifunctional FibersmicrofabricationFiber-based Devices | 20 | Magnetic Nanotransducers: Minimally invasive neural stimulation |
| Fiber-based Devices: Tissue repair and replacement | Uri Vool , Assaf Hamo , Georgios Varnavides , Yaxian Wang , Tony X Zhou , Nitesh Kumar , Yuliya Dovzhenko , Ziwei Qiu , Christina A Garcia , Andrew T Pierce , Johannes Gooth , Polina Anikeeva , Claudia Felser , Prineha Narang , Amir Yacoby (2021). Imaging phonon-mediated hydrodynamic flow in WTe2 . Nat Phys | Xuezeng Tian , Xingxu Yan , Georgios Varnavides , Yakun Yuan , Dennis S. Kim , Christopher J. Ciccarino , Polina Anikeeva , Ming-Yang Li , Lain-Jong Li , Prineha Narang , Xiaoqing Pan , Jianwei Miao (2021). Capturing 3D atomic defects and phonon localization at the 2D heterostructure interface . Sci Advances | |||
University of Pittsburgh | "We study how learning, cognition, and sensory-motor coordination are achieved by populations of neurons.” | neuroimaging | 12 | Flexibility and constraints in learning and cognition | Improving brain-computer interfaces | Sensory-motor coordination | Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate.Degenhart AD*, Eles J*, Dum R, Mischel JL, Smalianchuk I, Endler B, Ashmore RC, Tyler-Kabara EC, Hatsopoulos NG, Wang W**, Batista AP**, Cui XT**.J Neural Eng. 2016 Aug;13(4):046019 | Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters.Oby ER, Perel S, Sadtler PT, Ruff DA, Mischel JL, Montez DF, Cohen MR, Batista AP*, Chase SM*.J Neural Eng. 2016 Jun;13(3):036009. | |||
University of Chicago | “The overall scientific goal of my research program to understand how nervous systems give rise to flexible, intelligent behavior. The lens through which I address this question is the study of sensory processing: how are robust and flexible neuronal representations of the environment constructed to support behavior? The advantage of sensory neuroscience is that the experimenter has complete control of (or access to) the input and can then track how neural signals relating to this input are transformed as they propagate along the neuraxis. The sensory system of interest is the somatosensory system (of primates), critical to motor behavior and particularly manual behavior. The overall biomedical goal of my research program is to leverage what we learn about natural neural coding to restore the sense of touch to patients who have lost it, namely amputees and tetraplegic patients, by sensitizing bionic hands through electrical stimulation of the nerve or brain. I am the principal architect of the biomimetic approach, which posits that encoding algorithms that mimic natural neural signals will give rise to more intuitive tactile percepts, thereby endowing bionic hands with greater dexterity. Not only are neuroengineering efforts explicitly informed by neuroscientific ones, they also provide an opportunity to test scientific hypotheses using causal manipulations, in this case electrical stimulation of neuronal populations.” | bionic handsmotor coordination | 12 | Neural Coding: Touch and proprioception | Neuroprosthetics: Biomimetic sensory feedback | Suresh, A.K., Greenspon, C.M., He, Q., Rosenow, J.M., Miller, L.E., Bensmaia, S.J. (2021). Sensory computations in the cuneate nucleus of macaques, Proceedings of the National Academy of Sciences. | Sobinov, A.R., Bensmaia, S.J. (2021). The neural mechanisms of manual dexterity, Nature Reviews Neuroscience. | ||||
Stanford University | “Today's deep learning solutions scale poorly. Emitting 10,000-fold more carbon to train a deep net only doubles its performance on benchmark tasks. The next doubling in performance will emit as much CO2 as New York City does in a month. In contrast, the brain learns extremely efficiently. A child masters language by the age of 6, having heard at most 65 million words. That’s 15,000-fold less than the trillion words used to train GPT-3. Equivalently, a child that learns language at the same rate as GPT-3 would be 90,000 years old before it could converse fluently. By reverse-engineering how the brain uses so little data to learn, we hope to invent solutions that enable a sustainable technological future.” | neuromorphic software | 5 | Sparse Spatiotemporal Codes | Parallel Computing in 3D | Retinomorphic Vision Sensors | Scaling & Programming Neuromorphic Systems | Y Shi, N A Steinmetz, T Moore, K Boahen, T A Engel, Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex , Nature Communications , vol 13, no 44, 2022. PDF | B Benjamin, N A Steinmetz, N N Oza, J J Aguayo and K Boahen, Neurogrid simulates cortical cell-types, active dendrites, and top-down attention , Neuromorphic Computing and Engineering , 2021. PDF | ||
MIT | Your brain mediates everything that you sense, feel, think, and do. The brain is incredibly complex - each cubic millimeter of your brain contains perhaps a hundred thousand cells, connected by a billion synapses, each operating with millisecond precision. We develop tools that enable the mapping of the molecules and wiring of the brain, the recording and control of its neural dynamics, and the repair of its dysfunction. We distribute our tools as freely as possible to the scientific community, and also apply them to the systematic analysis of brain computations, aiming to reveal the fundamental mechanisms of brain function, and yielding new, ground-truth oriented therapeutic strategies for neurological and psychiatric disorders. | optogenetics | 64 | Tools for mapping brain computations | Tools for recording high-speed brain dynamics | Tools for controlling brain computations | Technologies for understanding and creating complex systems | ||||
UC Berkeley | 'We leverage neuroplasticity, machine learning, and neurotechnology to ask how the brain learns and controls movement, and to develop smart prosthetics and neurotherapies.” | neural dustneural stimulation | 8 | Algorithms for neuroprosthetic control | System-based therapies for neuropsychiatric disorders | Neurotechnologies for research and clinical applications | Piech D.K., Johnson B.C., Shen K., Ghanbari M.M., Li K.Y., Neely R.M., Kay J.E., Carmena J.M.1 , Maharbiz M.M.1 , Muller R.1 (2020) A wireless millimetre-scale implantable neural stimulator with ultrasonically powered bidirectional communication . Nature Biomedical Engineering doi: 10.1038/s41551-020-0518-9. [1, co-senior authors] | Athalye V.R., Carmena J.M., Costa R.M. (2020) Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes. Current Opinion in Neurobiology doi:10.1016/j.conb.2019.11.023. | |||
Harvard University | “The focus of our group is on understanding brain function works under both normal and pathological conditions with an ultimate goal of developing techniques for diagnosing and treating some of the most devastating neuropsychiatric diseases. We focus on using approaches which extract information at multiple scales and then combine them into a more complete and meaningful understanding of brain physiology. We feel this multi-scalar, multi-modal approach is particularly powerful for understanding the human brain because of its complexity and architecture which is, by nature, multi-scalar. This information can then be used to design therapeutic interventions including brain-computer (machine) neuroprosthetics.” | eegeCoG | 18 | Brain-Computer Interface Research | Neuromodulation of the human brain | Neurophysiology of Epilepsy | Understanding Human Cognition | Rubin DB, Hosman T, Kelemen JN, Kapitonava A, Willett FR, Coughlin BF, Halgren E, Kimchi EY, Williams ZM, Simeral JD, Hochberg LR, Cash SS (2022) Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep. J Neurosci 42:5007 LP – 5020 Available at: doi: 10.1523/JNEUROSCI.2074-21.2022 . | Rendon LF, Bick SK, Cash SS , Cole AJ, Eskandar EN, Williams ZM (2022) Aura Type and Outcome After Anterior Temporal Lobectomy. World Neurosurg 161:e199–e209 Available at: https://www.sciencedirect.com/science/article/pii/S1878875022001176. | ||
UC San Francisco | “A unique and defining trait of human behavior is our ability to communicate through speech. Our laboratory is interested in determining the basic mechanisms that underlie our ability to perceive and produce speech. While much of this processing has been localized to the peri-sylvian cortex, including Broca’s and Wernicke’s areas, the fundamental organizational principles of the neural circuits within these areas are completely unknown. To address this, our laboratory applies a variety of experimental approaches including psychophysics, local field potential and microelectrode array recordings, electrocortical stimulation, and real-time signal processing. These methods allow us to examine both local circuitry and global network dynamics spanning multiple cortical and sub-cortical regions with unparalleled spatial and temporal resolution in humans. Our central goal is to provide a mechanistic account for the major properties of speech behavior in normal speakers and those with language disorders. Our ongoing research is not only deepening understanding of speech and its disorders, but also is leading directly to safer mapping methods to preserve language function during brain surgery.” | neuromorphic softwaresoftware | 56 | Intonational speech prosody encoding | Perceptual restoration of masked speech | Decoding the English phonetic inventory | Categorical speech encoding in the Human Superior Temporal Gyrus | Scangos K.W., Khambhati A.N., Daly P.M., Makhoul G.S., Sugrue L.P., Zamanian H., Liu T.X., Rao V.R., Sellers K.K., Dawes H.E., Starr P.A, Krystal A.D., & Chang E.F. (2021). Closed-loop neuromodulation in an individual with treatment-resistant depression. Nature Medicine. https://doi.org/10.1038/s41591-021-01480-w | Scangos K.W., Khambhati A.N., Daly P.M., Owen L.W., Manning J.R., Ambrose J.B., Austin E., Dawes H.E., Krystal A.D., & Chang E.F. (2021). Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2021.746499 | ||
Carnegie Mellon University | “Broadly speaking, our laboratory investigates how sensory feedback impacts the neural representation of motor intent. One of the major tools we use is the brain-computer interface (BCI). These devices allow us to tap into the output of a network of neurons and use that recorded activity to directly drive a device, like a computer cursor. By creating a defined link between neural activity and behavior, BCIs provide a unique window for probing brain processes that would otherwise remain covert, like learning, adaptation, and the dynamic evolution of the intent signal. Ultimately a basic understanding of these phenomena will not only inform us about the fundamental limits of motor control processes, but will also help propel the development of new neural prosthetic devices. Our research has two main thrusts. First, we develop novel computational and experimental techniques that leverage BCIs as a research tool for investigating the neural mechanisms of sensorimotor adaptation and skill acquisition. Second, we design new BCI decoding algorithms to enhance the performance of these devices and hasten their clinical translation.” | braingate | 10 | Internal Model Estimation (IME) | Neural Reassociation | Neural Redundancy | Learning is shaped by abrupt changes in neural engagement JA Hennig, ..., AP Batista*, SM Chase*, BM Yu* Nature Neuroscience (2021) | Distinct kinematic adjustments over multiple timescales accompany locomotor skill development in mice KP Nguyen, A Sharma, M Gil-Silva, AH Gittis*, SM Chase* Neuroscience (2021) | |||
University of Michigan | “We focus on brain machine interface (BMI) systems using 100 channel arrays implanted in motor and premotor cortex. The goal of this research is to eventually develop clinically viable systems to enable paralyzed individuals to control prosthetic limbs, as well as their own limbs using functional electrical stimulation and assistive exoskeletons. We also seek to expand the bandwidth of this neural interface using novel electrodes, circuits, and algorithms.” | utah arrays | 12 | Neural Control of Finger Motion | Electrodes and Electronics | Systems Neuroscience | Yan D, Jiman AA, Bottorff EC, Patel PR, Meli D, Welle EJ, Ratze DC, Havton LA, Chestek CA, Kemp SW, Bruns TM. Ultraflexible and Stretchable Intrafascicular Peripheral Nerve Recording Device with Axon‐Dimension, Cuff‐Less Microneedle Electrode Array. Small. 2022 May 1:2200311. pdf | Lim J, Lee J, Moon E, Barrow M, Atzeni G, Letner JG, Costello JT, Nason SR, Patel PR, Sun Y, Patil PG. A light-tolerant wireless neural recording IC for motor prediction with near-infrared-based power and data telemetry. IEEE Journal of Solid-State Circuits. 2022 Jan 24;57(4):1061-74. pdf | |||
Stanford University | "The goal of our research is to develop an artificial retina -- an electronic implant that will restore vision to people blinded by retinal degeneration. We focus on a combination of basic and applied research to develop an implant that can reproduce the electrical signals that the retina normally transmits to the brain. To accomplish this goal, we work closely with collaborators in fields spanning neurophysiology, electrical engineering, materials science, retinal surgery, visual behavior, and computational neuroscience. This collaboration constitutes the Stanford Artificial Retina Project, funded in part by the Stanford Neurotechnology Initiative.” | retinal implants | 13 | The goal of our research is to understand how the neural circuitry of the retina encodes visual information, and to use this knowledge in the development of artificial retinas for treating incurable blindness. | Shah NP, Brackbill N, Samarakoon R, Rhoades C, Kling A, Sher A, Litke A, Singer Y, Shlens J, Chichilnisky EJ (2022). Individual variability of neural computations in the primate retina . Neuron . 2022 Feb 16;110(4):698-708.e5. [reprint] [preview] | Vilkhu, RS, Madugula, S S, Grosberg, LE, Gogliettino, AR, Hottowy, P, Dabrowski, W, Sher, A, Litke, AM, Mitra, S, Chichilnisky, EJ (2021) Spatially patterned bi-electrode epiretinal stimulation for axon avoidance at cellular resolution . J. Neur. Eng . 18 (06607) [reprint] | |||||
Columbia University | “We take a computational perspective, and ask how motor commands might be produced by recurrently connected networks with strong internal dynamics. We ask whether empirical neural data, collected during the performance of motor tasks, reflects basic computational principles that can be inferred from existing theory. Conversely, we ask when existing theory should be modified to incorporate principles gleaned from examination of empirical data. To pursue this approach, we collaborate extensively with colleagues in the Grossman Center for the Statistics of Mind, and within the Center for Theoretical Neuroscience.” | software | 8 | Pattern generation in motor cortex | Computational contributions of different motor areas | Intelligent control of motor unit activity | Prosthetic decode for locomotion | Independent generation of sequence elements by motor cortex. Nature NeuroscienceNature Neuroscience. February 2021 | Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of ComputationAbigail A. RussoRamin KhajehSean R. BittnerSean M. PerkinsJohn P. CunninghamL.F. AbbottMark M. ChurchlandNeuron. August 2020 | ||
University of Pittsburgh | “To improve the quality of life of individuals with neurological impairments by advancing the scientific understanding of motor and somatosensory systems to engineer new rehabilitation therapies and technologies. We strive to provide an exciting, challenging, and supportive environment where all individuals can thrive as we work towards a common mission.” | braingate | 58 | Neuroprosthetics: *Intracortical Brain-Computer Interfaces *Peripheral Interfaces for Upper-Limb Prosthetics *Peripheral Interfaces for Lower-Limb Prosthetics *Pre-clinical Sensory Restoration *Restoring Bladder Function *Spinal Cord Stimulation for the restoration of movement *Computer models of Neurostimulation | Imaging: *Covert Mapping of Sensorimotor Activation | Rehabilitation: *Spinal Cord Injury Model Systems Alliance for Regenerative *Rehabilitation Research and Training (AR3T) *Activity Monitoring in Individuals with Spinal Cord Injury | Elvira Pirondini, Nawal Kinany, Cécile LeSueur, Joseph C.Griffis, Gordon L.Shulman, Maurizio Corbetta, Dimitri Van DeVilleab, Neurolmage, Volume 255, 15 July 2022, 119201 DOI :http://www.sciencedirect.com/science/article/pii/S1053811922003251?via%3Dihub#! | Elvira Pirondini, Nawal Kinany, Cécile LeSueur, Joseph C.Griffis, Gordon L.Shulman, Maurizio Corbetta, Dimitri Van DeVilleab, Neurolmage, Volume 255, 15 July 2022, 119201 DOI :http://www.sciencedirect.com/science/article/pii/S1053811922003251?via%3Dihub#! | |||
EPFL | " | external electrical stimulation for motor | Investigation of neural and biomechanical impairments leading to pathological toe and heel gaits using neuromusculoskeletal modelling A. Bruel; S. Ben Ghorbel; A. Di Russo; D. Stanev; S. Armand et al. | Implanted System for Orthostatic Hypotension in Multiple-System Atrophy J. W. Squair; M. Berney; M. C. Jimenez; N. Hankov; R. Demesmaeker et al. | |||||||
Columbia University | “My research group studies machine learning and its application to science and industry, including in particular using the tools of artificial intelligence to understand biological intelligence and other complex processes. I am a Professor of Statistics at Columbia University and an investigator at the Zuckerman Mind Brain Behavior Institute and Center for Theoretical Neuroscience. Prior to Columbia I was a research fellow in the Machine Learning Group at Cambridge. I have a Ph.D. in electrical engineering from Stanford and an undergraduate degree in computer science from Dartmouth. I have also worked with and advised a number of companies.” | machine learning | Biological intelligence and other complex processes | 1. L Wu, G Pleiss, JP Cunningham (2022) "Variational Nearest Neighbor Gaussian Processes" In Review. | |||||||
Brown University | “Our laboratory investigates how the brain turns thought into voluntary behavior. We seek to expand our understanding of the basic principles of neural computation, and use this knowlege to improve the quality of life of persons with paralysis.We study how populations of neurons represent and transform information as motor plans evolve towards movement execution. This approach has required the creation of novel technology to simulaneously record the activity of hundreds of individual neurons. Our work has led to a clinical trial where humans with paralysis can use neural activity to directly control external devices.” | braingate | 6 | Neural Prosthetics (Brain Machine Interfaces) | Neural Codes | ||||||
University of Washington | “We are investigating the neural mechanisms involved in programming and executing hand movements by recording neural activity in monkeys trained to manually track visual targets. We are particularly interested in studying ''pre-motoneuronal'' cells in motor cortex and spinal cord that produce postspike effects on forelimb muscle activity. By knowing both the response patterns of these cells during movements and their output connections to target muscles we can make important causal inferences about their contribution to movements. We are currently using multichannel electrode arrays to investigate interactions between motor cortex sites during movement.” | motor coordination | 5 | Neural Control of Limb Movement: • Chronic unit recording • Premotor neurons • Motor control | Volitional Control of Neural Activity: • Operant conditioning of neural activity • Neurofeedback • Cell and muscle activity disassociation | Closed Loop Brain Computer Interfaces: • Activity dependent stimulation • Spike timing dependent synaptic plasticity • Beta cycle-triggered stimulation | Synaptic Interactions Between Neurons: • Effects of post-synaptic potentials • Spike-triggered averages • Cross-correlation histograms • Local field potential oscillations | Yun R, Mishler JH, Perlmutter SI, Fetz EE, Paired stimulation for spike-timing dependent plasticity quantified with single neuron responses in primate motor cortex , bioRxiv 2022.05.04.490684; doi: https://doi.org/10.1101/2022.05.04.490684 . | Yun R, Mishler JH, Perlmutter SI, Rao RPN, Fetz EE, Responses of cortical neurons to intracortical microstimulation in awake primates , bioRxiv 2022.03.30.486457; doi: https://doi.org/10.1101/2022.03.30.486457 . | ||
Stanford University | “We are a diverse group of physicists, mathematicians, computer scientists, bioengineers and neurobiologists, dedicated to this quest. We are motivated by two overarching questions: 1. How do fundamental higher level cognitive phenomena like perception, memory, attention, decision making and motor control emerge from the collective interactions of many dynamical degrees of freedom embedded within networks of neurons and synapses? 2. Can principles of high dimensional statistics and computation provide a unified view of how we as neuroscientists can extract biologically meaningful models of neural systems from large datasets, as well as how neural systems themselves can extract predictive models of their environment from the stream of sensorimotor experience? To pursue these questions, we exploit and extend tools and ideas from a diverse array of disciplines, including statistical mechanics, dynamical systems theory, machine learning, information theory, control theory, and high-dimensional statistics, as well as collaborate with experimental neuroscience laboratories collecting physiological data from a range of model organisms, from flies to humans.” | computational neuroscience | 11 | Realizing a trainable Hopfield associative memory in a driven-dis sipative quantum optical neural network . Keeling, J. Cotler, B.L. Lev, S. Ganguli, In prep | GluD2- and Cbln1-mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells Y.H. Takeo, S.A. Shuster, L. Jiang, M. Hu, D.J. Luginbuhl, T. Rlicke, X. Contreras, S. Hippenmeyer, M.J. Wagner, S. Ganguli, L. Luo, Under review PDF | ||||||
UC San Francisco | "The motor network consists of interconnected cortical and subcortical areas. Large scale recordings suggest frameworks for processing such as oscillatory dynamics and cross-area filtering. We aim to delineate the network dynamics of learning and to develop physiologically-inspired neurotechnology to enhance recovery and function. Goal 1. Understand how distributed network activity permits learning and skill consolidation. We are interested in how animals learn new complex and flexible skills. We specifically consider neural processing across awake and sleep in cortex and the basal ganglia. For example, how are awake experiences (task activity) processed during sleep to allow consolidation of a nascent skill (e.g. Gulati et al., Nature Neuro, 2017; Kim et al., Cell 2019)? How does this change with injury (e.g. Kim et al., Cell Reports, 2022)? Goal 2. Neural interfaces to restore neural circuit dynamics. We use principles of network plasticity to integrate bidirectional neural interfaces ("read and write"') into injured brains. We thus aim to restore neural processing and improve function (e.g., Ramanathan et al, Nature Medicine 2018; Khanna et al., Cell, 2021). Goal 3. Translate to patients. In addition to discovering fundamental principles, we are translating our research into clinical neurotechnology (Silversmith et al., Nature Biotechnology, 2021). We co-lead a pilot trial of Brain-Computer Interfaces (BCI) in human subjects with severe motor disability [BRAVO Trial, ClinicalTrials@UCSF]. “ | 23 | Neural reactivations during sleep and memory consolidation | Distributed control of skill (recovery after injury) | Closed-loop neuromodulation | Consolidated neuroprosthetic control | Kim J et al., Impaired sleep-dependent consolidation after stroke – interaction of slow waves, rehabilitation and GABA Cell Reports | Kondapavulur S, Lemke S, Darevsky D, Guo L, Khanna P, Ganguly K. Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration. Nature Communications. | |||
University of Pittsburgh | “Dr. Gaunt’s primary research interests are in the area of sensorimotor control and developing advanced neural interfaces with the brain and peripheral nerves for sensory restoration and motor control. He also works on developing methods to improve bladder function using electrical stimulation of the spinal cord and peripheral nerves. The goal of this work is to understand how humans normally accomplish complex sensorimotor tasks, test these principles using neuroprosthetic technologies, and ultimately leverage this knowledge to develop devices to restore and improve function.” | PNS stimulation | |||||||||
UC San Diego | "The Translational Neuroengineering Lab at UC San Diego conducts interdisciplinary research that integrates domain-specific knowledge from the fields of basic neuroscience, systems engineering, signal processing, and machine learning.” | prostheseselectrodes | 9 | Neurology, Neurosurgery, Psychiatry | Signal Processing | Machine Learning | Behavioral & Cognitive Experiments | High γ Activity in Cortex and Hippocampus Is Correlated with Autonomic Tone during Sleep Abdulwahab Alasfour, Xi Jiang, Jorge Gonzalez-Martinez, Vikash Gilja and Eric Halgren eNeuro 3 November 2021, 8 (6) ENEURO.0194-21.2021; DOI: https://doi.org/10.1523/ENEURO.0194-21.2021 | A. N. Patel et al., "Affective response to volitional input perturbations in obstacle avoidance and target tracking games," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, pp. 6679-6682, doi: 10.1109/EMBC46164.2021.9630523. | ||
University of Chicago | infrared optogenetics | ||||||||||
Stanford University | “Our overriding interest is in aging, cognition, and dementia both at the population level and at the level of individual adults. Our research agenda encompasses cognitive change that occurs as a usual concomitant of normal aging and more debilitating impairment that accompanies Alzheimer’s disease and other forms of dementia. Pathological processes that culminate in Alzheimer dementia are believed to begin years, if not decades, before the onset of mental symptoms. For this reason, we are interested in factors that affect cognitive skills at midlife, a time when therapeutic interventions offer the greatest potential of forestalling late-life impairment, as well at older ages, when cognitive impairment more typically first becomes apparent. One research theme pertains to adult women’s health. Here, we continue to examine cognitive consequences of estrogen, testosterone and other hormones, whose concentrations vary with age.” | Alzheimer’s Disease and Cognitive Aging | Women’s Health and Cognition | Investigator Initiated Clinical Trials | Risk of dementia in patients with inflammatory bowel disease: a Danish population-based study. Alimentary pharmacology & therapeutics Sand, J. R., Troelsen, F. S., Horvath-Puho, E., Henderson, V. W., Sorensen, H. T., Erichsen, R. 2022 | Rest-activity rhythms and cognitive impairment and dementia in older women: Results from the Women's Health Initiative. Journal of the American Geriatrics Society Xiao, Q., Shadyab, A. H., Rapp, S. R., Stone, K. L., Yaffe, K., Sampson, J. N., Chen, J., Hayden, K. M., Henderson, V. W., LaCroix, A. Z. 2022 | |||||
Stanford University | "We are developing a circulation-delivered internal light source for biomedical applications that need light emission deep inside the body, such as optogenetics, fluorescence imaging, and photodynamic therapy. We leverage the demonstrated deep-brain penetration of NIR-II light (1000-1700 nm) to develop infrared optogenetics based on bandgap-engineered semiconducting polymers. We are developing injectable nanoantennas and radiofrequency (RF) generators to modulate and communicate with the biological tissue with free-space radio waves. We are developing a bioinspired approach to synthesize solid-state ceramic phosphors with nanometer sizes and water solubility. Mechanoluminescent materials convert focused ultrasound into light emission.” | radiofrequency (RF) | 15 | A circulatory light source enabled by trap-engineered phosphors | Infrared neural modulation with semiconducting polymers | Tissue modulation with nanoantennas and free-space radio waves | Bioinspired synthesis of water-soluble solid-state nanophosphors | X. Wu, Y. Jiang, N. J. Rommelfanger, F. Yang, Q. Zhou, J. Liu, W. Ren, S. Cai, A. Shin, K. S. Ong, K. Pu and G. Hong, “Tether-free photothermal deep-brain stimulation in freely behaving mice via widefield illumination in the near-infrared II window,” Nat. Biomed. Eng. 2022, DOI: 10.1038/s41551-022-00862-w | S. Jiang, X. Wu, N. J. Rommelfanger, Z. Ou, and G. Hong, “Shedding light on neurons: optical approaches for neuromodulation,” Natl. Sci. Rev. 2022, DOI: 10.1093/nsr/nwac007 | ||
MIT | MRImolecular imaging | 20 | Brain-wide dynamics | Neuroengineering | Molecular imaging | Brain chemistry | Ghosh S, Li N,Schwalm M, Bartelle B, Xie T, Daher J, Singh U, Xie K, DiNapoli N, Evans N, Chung K, Jasanoff A. (2022) “Functional dissection of neural circuitry using a genetic reporter for fMRI” Nat Neurosci 25: 390-398. | Desai M, Sharma J,Slusarczyk AL, Chapin AA, Ohlendorf R, Wisniowska A, Sur M, Jasanoff A. (2021) “Hemodynamic molecular imaging of tumor-associated enzyme activity in the living brain” Elife 10: e70237. | |||
University of Montreal | 1 | Neural mechanisms of the cerebral cortex involved in the planning and control of arm pointing movements | Neural mechanisms of the cerebral cortex in the decision-making processes leading to the choice of movement to be made. | Kalaska JF. Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates. F1000 Faculty Rev-749. Doi:10.12688/f1000research.17161.1. eCollection 2019. | Wang M, Montanède C, Chandrasekaran C, Peixoto D, Shenoy KV, Kalaska JF. Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known. Nature Communications doi.org/10.1038/s41467-019-09460-y (2019). | ||||||
UCLA | “Our research group studies questions at the intersection of neuroscience and computation. In particular, we develop and apply statistical signal processing and machine learning techniques to elucidate how populations of neurons carry out computations in the brain. We also develop experimental and algorithmic techniques for neural engineering applications, including brain-machine interfaces.” | brain stimulationelectrodescomputational neuroscience | 9 | Machine learning and statistics for neuroscience | Deep learning | Brain-machine interfacesPermalink | Learning rule influences recurrent network representations but not attractor structure in decision-making tasks., McMahan B, Kleinman M, Kao JC | A mechanistic multi-area recurrent network model of decision-making Kleinman M, Chandrasekaran C*, Kao JC* | |||
University of Pennsylvania | Making sense of data is possibly the biggest problem in Neuroscience and beyond. We build algorithms to analyze data. We also use theory as well as computational and neural modeling to understand how information is processed in the nervous system, explaining data obtained in collaboration with electrophysiologists and in psychophysical experiments. Lastly, we constrain and develop new technologies aimed at obtaining data about brains. Our conceptual work addresses information processing in the nervous system from two angles: (1) By analyzing and explaining electrophysiological data, we study what neurons do. (2) By analyzing and explaining human behavior, we study what all these neurons do together. Much of our work looks at these questions from a normative or causal viewpoint, asking what problems the nervous system should be solving. This often means taking a Bayesian approach. Bayesian decision theory is the systematic way of calculating how the nervous system may make good decisions in the presence of uncertainty. Causal inference from observational data promises to be a key enabler for progress in science. | computational neuroscience | 9 | analyzing and explaining electrophysiological data | analyzing and explaining human behavior | For love of neuroscience: The Neuromatch movement Konrad Paul Kording Neuron, 2021 (Article) | Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study Jonah Meyerhoff, Tony Liu, Konrad P Kording, Lyle H Ungar, Susan M Kaiser, Chris J Karr, David C Mohr Journal of Medical Internet Research, 2021 (Article) | ||||
Stanford University | drug deliveryBio-inorganic InterfaceMolecular materials | ||||||||||
Northwestern University | “We study the "language" of these control signals and the networks of neurons that produce them. Along with this basic research, we are working to develop neural interfaces that directly connect the brain of a spinal cord injured patient with the outside world. These interfaces will ultimately allow patients to operate a computer or a prosthetic device. They may also bypass the injured spinal cord in order to reactivate paralyzed muscles, or to restore the sense of touch and limb movement." | braingateFES | 10 | Biomimetic Somatosensory Feedback through Intracortical Microstimulation | A Primate Model of an Intra-Cortically Controlled FES Prosthesis for Grasp | Probing Somatosensory Representations in the Cuneate Nucleus of Awake Primates | Stable Neural Interfaces through Artificial Intelligence-Based Manifold Discovery | Jude J, Perich MG, Miller LE, Hennig MH (2022) Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles arXiv | Sombeck J, Heye J, Kumaravelu K, Goetz S, Peterchev AV, Grill WM, Bensmaia SJ, Miller LE (2022) Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array J. Neural Eng. 19 026044 | ||
University of Washington | “We develop neuroprosthetic technology for the treatment of paralysis and other movement disorders. Our current projects include developing techniques to bypass damaged areas of the nervous system & restore control of movement to paralyzed limbs, promoting recovery and perhaps regeneration of damaged neural tissue, and testing methods for physical therapy and rehabilitation of movement disorders.” | Transcutaneous Electrical Spinal Stimulation | 19 | Transcutaneous Spinal Cord Stimulation to Restore Upper Extremity Function in Spinal Cord Injury | Spinal Stimulation and Physical Therapy for Locomotion | Brain Controlled Spinal Interface for Arm Function | Repair: We have observed that stimulation within the spinal cord below an injury leads to improved motor function even beyond the period of stimulation. In the uninjured central nervous system, synchronizing stimulation can be used to strengthen connections among neurons via mechanisms of Hebbian plasticity. We are investigating whether intraspinal stimulation, triggered by appropriate cortical activity, is capable of guiding spared pathways to functional targets after spinal cord injury. | Samejima, S., Caskey, C.D., Inanici, F., Shrivastv, S., Brighton, L. N., Pradarelli, J., Martinez, V., Steele, K.M., Saigal, R., Moritz, C.T., (2022) Multisite transcutaneous spinal stimulation for walking and autonomic recovery in motor-incomplete tetraplegia: a case series. Physical Therapy 102 (1) | Soshi Samejima, Aiva M. Ievins, Adrien Boissenin, Nicholas M. Tolley, Abed Khorasani, Sarah E. Mondello, Chet T. Moritz. (2022) Automated Lever Task with Minimum Antigravity Movement for Rats with Cervical Spinal Cord Injury. Journal of Neuroscience Methods 366, 109433, ISSN 0165-0270 | ||
Stanford University | “Our group’s research is focused on innovation at the analog-to-digital interface and its computational back-end. Our work spans signal conditioning circuits for sensors, high-speed wireline & RF transceivers, as well system-driven circuit design for data-compressive interfaces and embedded machine learning.” | electrodes | 17 | Interface Circuits | Machine Learning Circuits and Systems | P. Caragiulo, A. Ramkaj, A. Arbabian, and B. Murmann, “A 56 GS/s 8-bit 0.011 mm2 4x Delta-Interleaved Switched-Capacitor DAC in 16 nm FinFET CMOS,” to appear, IEEE European Solid-State Circuits Conference, Sep. 2022. | K. Prabhu, A. Gural, Z.F. Khan, R.M. Radway, M. Giordano, K. Koul, R. Doshi, J.W. Kustin, T. Liu, G.B. Lopes, V. Turbiner, W.-S. Khwa, Y.-D. Chih, M.-F. Chang, G. Lallement, B. Murmann, S. Mitra, and P. Raina, “CHIMERA: A 0.92 TOPS, 2.2 TOPS/W Edge AI Accelerator with 2 MByte On-Chip Foundry Resistive RAM for Efficient Training and Inference,” IEEE J. Solid-State Circuits, vol. 57, no. 4, pp. 1013-1026, Apr. 2022. | ||||
Duke University | “Although the Nicolelis Laboratory is best known for pioneering studies in neuronal population coding, Brain Machine Interfaces (BMI) and neuroprosthetics in human patients and non-human primates, we have also developed an integrative approach to studying neurological and psychiatric disorders including Parkinsons disease and epilepsy. We believe this approach will allow the integration of molecular, cellular, systems, and behavioral data in the same animal, producing a more complete understanding of the nature of the neurophysiological alterations associated with these disorders.” | 15 | Kunicki C, Moioli RC, Pais-Vieira M, Peres ASC, Morya E, Nicolelis MAL. Frequency-specifc coupling in fronto-parieto-occipital cortical circuits underlie active tactile discrimination. Rep. 9, Article number: 5105. https://doi.org/10.1038/s41598-019-41516-3. | Shokur S, Donati ARC, Campos DSF, Gitti C, Bao G, Fischer D, Almeida S, Braga VAS, Augusto P, Petty C, Alho EJL, Lebediev M, Song AW, Nicolelis MAL. Training with brain-machine interfaces, visuotactile feedback and assisted locomotion improves sensorimotor, visceral, and psychological signs in chronic paraplegic patients. PLoS ONE13(11): e0206464. https://doi.org/10.1371/journal.pone.0206464, 2018. | |||||||
Stanford University | “Research Mission • understanding causal relationships between multidimensional cortical dynamics and behavior • improving the diagnosis and treatment of brain-related disorders such as stroke and epilepsy • establishing brain-machine interfaces as a platform technology for neurological disorders” | multielectrode arrayselectrodesmodelingdecoding | 8 | Understanding causal relationships between multidimensional cortical dynamics and behavior | Improving the diagnosis and treatment of brain-related disorders such as stroke and epilepsy | Establishing brain-machine interfaces as a platform technology for neurological disorders | Silvernagel MP, Ling A, Nuyujukian P. 2021. A 3D Motion Capture Platform for Motor Systems Neuroscience. IEEE EMBS NER. | Peixoto D*, Verhein JR*, Kiani R, Kao JC, Nuyujukian P, Chandrasekaran C, Brown J, Fong S, Ryu SI, Shenoy KV et al.. 2021. Decoding and perturbing decision states in real time | |||
University of Washington | "We are an interdisciplinary group that explores neural interfaces as adaptive closed-loop systems. We use engineering approaches to leverage neural adaptation for improved interfaces, and use neural interfaces as a tool to study neural mechanisms of learning. The lab also specializes in system integration for advancing neurotechnologies to study neural circuits in awake primates for basic science and towards human translation.” | neuroprostheses | 12 | ADAPTIVE NEURAL INTERFACES | NEURAL INTERFACES TO STUDY LEARNING | NEURAL IMPLANTS FOR STUDYING LARGE-SCALE CIRCUITS | V. R. Athalye, P. Khanna, S. Gowda, A. L. Orsborn, R. M. Costa, and J. M. Carmena (2021). The brain sues invariant dynamics to generalize outputs across movements. Bioarxiv (link, open) | A. K. You, B. Liu, A. Singhal, S. Gowda, H. Moorman, A. L. Orsborn, K. Ganguly, J. M. Carmena (2019). Flexible modulation of neural variance facilitates neuroprosthetic skill learning. Bioarxiv (link, open) | |||
Stanford University | “Optical and electronic technologies for diagnostic, therapeutic, surgical and prosthetic applications, primarily in ophthalmology: • Electro-Neural interfaces: Photovoltaic Retinal Prosthesis; Electronic Control of Vasculature and of the Glands • Optical Imaging and Spectroscopy: Interferometric detection of neural activity • Laser-Tissue Interactions: ◦ Non-damaging retinal laser therapy ◦ Ultrafast laser surgery of transparent tissues • Retinal plasticity: ◦ Cellular preservation and proliferation in response to therapy ◦ Transplanation of retinal cells and neural rewiring” | ultrasoundPhotovoltaic Retinal ProsthesisOptical Imaging and SpectroscopyRetinal plasticity: | 15 | Photovoltaic Restoration of Sight - optoelectronic subretinal prosthesis | Laser-Tissue Interactions – mechanisms and therapeutic applications | Electronic Control of Organs - Blood Vessels and Secretory Glands | Retinal Plasticity - migration and rewiring of the retinal neurons | Electronic Retinal Prostheses, Chapter 1.39 in The Senses: A Comprehensive Reference, 2nd edition, Bernd Fritzsch (Ed), Elsevier 2021. | Ophthalmic Laser Therapy and Surgery. D. Palanker. Chapter 15 in the Handbook of Laser Technology & Applications, 2nd Edition. Ed. Chunlei Guo, Chandra Subhash Singh. CRC Press, 2021 | ||
Georgia Tech | “We aim to understand how large populations of neurons in the brain perform computations and represent intention. We use these insights to develop high-performance, robust, and practical assistive devices for people with disabilities and neurological disorders.” | computational neuroscience | 10 | Deep learning methods to uncover neural population dynamics | Brain-machine interfaces for people with paralysis that achieved record-breaking communication performance | Wimalasena LN, Braun JF, Keshtkaran MR, Alessandro C, Gallego JA, Tresch MC, Miller LE, Pandarinath C. Estimating muscle activation from EMG using deep learning-based dynamical systems models. Journal of Neural Engineering, in press. doi: 10.1088/1741-2552/ac6369. Preprint on BioRxiv: 10.1101/2021.12.01.470827, posted Dec 03, 2021. | Keshtkaran MR*, Sedler AR*, Chowdhury RH**, Tandon R**, Basrai D, Nguyen SL, Sohn H, Jazayeri M, Miller LE, Pandarinath C. A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Nature Methods, in press. Preprint on BioRxiv: 10.1101/2021.01.13.426570, posted Jan 15, 2021. | ||||
Columbia University | statistics | ||||||||||
Western University | "We study the neural mechanisms of reaching, grasping and object manipulation Our lab is trying to explain how various parts of the nervous system – from receptors in the skin and muscles to neurons in the brainstem and cerebral cortex – work in concert to generate skilled movements of the arm and hand. We expect that our work into these fundamental processes will yield better treatments for the diseases and traumatic events that impair hand and arm function.” | motor coordination | 9 | 1. Arbuckle, S.A., Pruszynski, J.A., Diedrichsen, J. (2022) Mapping the integration of sensory information across fingers in human sensorimotor cortex. Journal of Neuroscience 42: 5173-5185. [LINK] | 1. Ariani, G., Pruszynski, J.A., Diedrichsen, J. (2022) Motor planning brings human primary somatosensory cortex into movement-specific preparatory states. eLife 11:e69517. [LINK] | ||||||
University of Washington | “His research interests span computational neuroscience, brain-computer interfaces, and artificial intelligence as well as the Indus script and classical Indian paintings.” | TMSmachine learningcomputational neuroscience | 1 | Brain-Computer Interfaces: • Textbook: Introduction to BCIs • Non-Invasive BCIs • Electrocorticographic BCIs • Brain-to-Brain Interfaces | Computational Neuroscience: • Predictive Coding Model of the Cortex • Bayesian Models of Brain Function • Decision Making in the Brain | Artificial Intelligence: • Unsupervised Learning and Predictive Coding for Vision • What-Where Networks • Reinforcement Learning • Robotics | Modeling Other Minds: Bayesian Inference in Group Decision-Making (Science Advances 2019) | A Bayesian Theory of Conformity in Collective Decision Making (NeurIPS 2019) | |||
Stanford University | "His laboratory has two major research efforts: • Innovation and application of optical imaging technologies for visualizing large-scale neural ensemble dynamics in awake behaving mice and fruit flies. • Imaging and mechanistic studies of the neural ensemble basis for learning and long-term memory in cortical and sub-cortical brain areas.” | In vivo two-photon fluorescence imaging studiesFiber optic fluorescence microendoscopyneuroimaging | 29 | In vivo two-photon fluorescence imaging studies of cerebellar-dependent learning and memory. | Fiber optic fluorescence microendoscopy | Massively parallel brain imaging in live fruit flies | Ebrahimi S, Lecoq J, Rumyantsev O, Tasci T, Zhang Y, Irimia C, Li J, Ganguli S, Schnitzer MJ. (2022) Emergent reliability in sensory cortical coding and inter-area communication. Nature. doi: 10.1038/s41586-022-04724-y. Online ahead of print. See accompanying News and Views. | National Academies of Sciences, Engineering, and Medicine 2022. Physics of Life. Washington, DC: The National Academies Press. https://doi.org/10.17226/26403. | |||
University of Pittsburgh | “As a systems neurophysiology lab, we are interested in the way neural activity drives behavior. Our goal is to describe organizational principles of the time-varying relation between the firing rates of neurons and the behavior this activity generates. Specifically, our research program centers on the relationship between cerebral cortical activity and arm movement. Over the last 25 years, we have found that there is a very good representation of the armis trajectory in the collective firing pattern of frontal cortical activity. This makes it possible to predict the detailed time course of arm, wrist and finger movement that contains many of the behavioral invariants that are characteristic of movement. Research projects in our laboratory are based on this dynamic representation of behavior. The parameter of time is fundamental in our consideration of movement. We have shown that movement generation takes place continuously the cortical prediction of trajectory precedes movement execution with a time interval that is dependent on the figural components of the hands path.” | motor coordination | 3 | Neural correlates of action | Cortical relation to muscle activity | Cortical prosthetics | Others: *Engineered Learning *Tuning function stability *Information estimation *Sensory replacement | Chandrasekaran S, Fifer M, Bickel S, Osborn L, Herrero J, Christie B, Xu J, Murphy RKJ, Singh S, Glasser MF, Collinger JL, Gaunt R, Mehta AD, Schwartz A, Bouton CEHistorical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications Bioelectron Med. 7(1):14 | Kennedy SD, Schwartz ABDistributed processing of movement signaling Proc Natl Acad Sci | ||
Queen's University | “Our research interest is to understand the behavioural, neural, and mechanical aspects of voluntary motor function. We use optimal control theory as a framework to interpret voluntary control which emphasizes the importance of sensory feedback for controlling motor actions. Our studies use the Kinarm robot we invented to quantify planar limb movements. We use small disturbances (usually mechanical, but visual sometimes) to observe how subjects make corrective responses to attain behavioural goals. Effectively, these disturbances allow us to probe the motor circuits to understand the underlying control processes. Most studies quantify changes in muscle electromyographic activity to observe the exact timing when the motor system reflects different functional processes. This research is supported by a grant from NSERC.” | motor coordination | 19 | Human Motor Control | Neural Control of Movements in NHPs | Christiansen, A., Scythes, M., Ritsma, B. R., Scott, S. H., & DePaul, V. (2022). Art skill-based rehabilitation training for upper limb sensorimotor recovery post-stroke: A feasibility study. Clinical Rehabilitation, 02692155221105586. https://doi.org/10.1177/02692155221105586 | Lowrey, C. R., Dukelow, S. P., Bagg, S. D., Ritsma, B., & Scott, S. H. (2022). Impairments in Cognitive Control Using a Reverse Visually Guided Reaching Task Following Stroke. Neurorehabilitation and Neural Repair, 36(7), 449–460. https://doi.org/10.1177/15459683221100510 | ||||
University of Southern California | “Our laboratory develops neurotechnology and studies the brain through modeling, decoding and control of neural dynamics. To do so, we work at the interface of machine learning, statistical inference, signal processing, and control theory to develop algorithmic solutions for problems in basic and clinical neuroscience that involve the collection and manipulation of neural signals. Our work combines algorithm development and modeling with in vivo experimental implementation and testing, and is conducted in close collaboration with a variety of experimental labs. Our lab has developed brain-machine interfaces for decoding and closed-loop control of various brain states to restore lost emotional function in mental disorders and lost motor function in neurological injuries and diseases. Some specific problems of interest include dynamic modeling of high-dimensional multiscale brain network activity, decoding of cognitive, motor, or mood states from neural signals, and developing closed-loop neurotechnologies such as closed-loop deep brain stimulation systems and brain-machine interfaces. These closed-loop technologies have the potential to enable personalized therapies for a wide range of neurological and neuropsychiatric disorders.” | machine learningstatisticssignal processing | 15 | Closed-Loop Multisensory Brain-Computer Interface for Enhanced Decision Accuracy | Song C., Hsieh, H., Pesaran B., Shanechi M. M., “Modeling and Inference Methods for Switching Regime-Dependent Dynamical Systems with Multiscale Neural Observations”, bioRxiv , Jun. 2022 (link ). | Wang C., Pesaran B., Shanechi M.M., “Modeling multiscale causal interactions between spiking and field potential signals during behavior”. Journal of Neural Engineering, Jan. 2022. (link) | |||||
Caltech | “We develop technologies to image and control the function of cells deep inside the body. These technologies take advantage of biomolecules with unusual physical properties allowing them to interact with sound waves and magnetic fields. We apply these tools to problems in synthetic biology, neuroscience, cancer, immunology and the mammalian microbiome.” | ultrasoundbioacousticsbiomagnetismbiophysicsbiochemistry | 38 | Molecular Engineering for Non-Invasive Imaging and Control of Biological Systems | Genetically Encoded Reporters for Ultrasound | Genetically Encoded Reporters, Devices and Basic Mechanisms of MRI and Magnetic Actuation | Ultrasonic and Thermal Control of Biological Systems | Dutka P, Metskas LA, Hurt RA, Salahshoor H, Wang TY, Malounda D, Lu GJ, Chou TS, Shapiro MG*, Jensen GJ*. Structure of Anabaena flos-aquae gas vesicles revealed by cryo-ET. bioRxiv preprint | Kim WS, Min S, Kim SK, Kang S, Davis HC, Bar-Zion A, Malounda D, Kim YH, An S, Lee JH, Bae SH, Lee JG, Kwak M, Cho SW, Shapiro MG*, Cheon J*. Magneto-acoustic protein nanostructures for non-invasive imaging of tissue mechanics in vivo. bioRxiv preprint | ||
Stanford University | “The human brain comprises approximately 100 billion neurons, each with approximately 1,000 connections to other neurons. How do these neurons coordinate to control movement? To discover fundamental principles we conduct electrophysiological experiments with nonhuman primates (NHPs) and people, and create theories and computational analyses to decipher how computations are performed through their collective dynamics (Computation Through Dynamics, CTD)” | motor coordinationprosthetics | 24 | dorsal premotor (PMd) and primary motor (M1d) cortex in the arm, hand and finger regions of NHPs and people. | 140. Sylwestrak EL*, Jo Y*, Vesuna S*, Wang X, Holcomb B, Tien RH, Kim DK, Fenno L, Ramakrishnan C, Allen WE, Chen R, Shenoy KV, Sussillo D, Deisseroth K (2022) Integrated computational and genetic codes of reward in the habenula. Cell. In press. | 139. Paulk AC, Kfir Y, Khanna A, Mustroph M, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson M, Williams ZM, Cash SS. (2022) Large- scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nature Neuroscience . 25:252-263. pdf url | |||||
Northwestern University | “Our laboratory uses neural prosthetics, specifically human-machine interfaces, to pursue the goals of improving both our understanding of brain function as well as restoring movement and communication to people with neurologic impairment from disorders such as stroke, spinal cord injury, traumatic brain injury, and ALS.” | EMGmotor coordination | 12 | Brain-Machine Interfaces to restore movement | Myoelectric-Computer Interfaces for neurorehabilitation | Brain-Machine Interfaces to restore communication | Portable, open-source solutions for estimating wrist position during reaching in people with stroke JZ Nie, JW Nie, NT Hung, RJ Cotton, MW Slutzky – Scientific reports, 2021 | Electromyogram (EMG) removal by adding sources of EMG (ERASE): A novel ICA-based algorithm for removing myoelectric artifacts from EEG. Li, Y., Wang, P.T., Vaidya, M.P., Flint, R.D., Liu, C.Y., Slutzky, M.W., and Do, A.H. Frontiers in Neuroscience 14, 2021, p. 597941. doi: 10.3389/fnins.2020.597941. PMC7873899. | |||
Caltech | “We develop novel biophotonic tomographic technologies for early-cancer detection and functional imaging, using non-ionizing electromagnetic and ultrasonic waves. Unlike ionizing x-ray radiation, non-ionizing electromagnetic waves, such as optical and radio waves, pose no health hazard and at the same time reveal rich contrast mechanisms. Unfortunately, electromagnetic waves in the non-ionizing spectral region do not penetrate biological tissue in straight paths as x-rays do. Consequently, high-resolution tomography that is based on non-ionizing electromagnetic waves alone, as demonstrated by confocal microscopy, two-photon microscopy as well as optical coherence tomography, is limited to superficial imaging within about one optical transport mean free path (~1 mm) of the surface of biological tissue. Ultrasonic imaging, by comparison, provides fine image resolution but has strong speckle artifacts and has no molecular contrast. We have developed ultrasound-mediated imaging modalities by combining electromagnetic and ultrasonic waves synergistically to overcome the above problems. The hybrid modalities yield speckle-free images with high electromagnetic contrast at high ultrasonic resolution in relatively large volumes of biological tissue. Please visit the Research pages to see the specific technologies that we develop.” | cellular imaging | 29 | Compressed Ultrafast Photography (CUP) | Photoacoustic Tomography (PAT) | Thermoacoustic Tomography (TAT) | Ultrasound-modulated (Acousto-) Optical Tomography (UOT) | 1. Lin, L.; Wang, L, V.; "The emerging role of photoacoustic imaging in clinical oncology," Nature Reviews Clinical Oncology 365-384 (2022) [PDF] | 1. [Na, S; Russin, J. J.; Lin, L; Yuan, X]; Hu, P; Jann, K. B.; Yan, L; Maslov, K; Shi, J; Wang, D. J.; Liu, C. Y.; Wang, L. V.; "Massively parallel functional photoacoustic computed tomography of the human brain," Nature Biomedical Engineering 584-592 (2021) [PDF] | ||
MIT | “We are developing genetic techniques for neuroscience, to provide more powerful and precise ways of studying the organization of the brain, and potentially to provide clinical neurology with more effective ways of treating disorders of the brain.” | genetic neuroengineeringneuroimaging | 8 | Targeting neurons based on their connectivity | Targeting neurons based on their gene expression patterns | Monteiro P, Barak B, Lhou Y, McRae R, Rodrigues , Wickersham IR, Feng G. JPhysIol. 2U18 May 29. dor 10.1113/UP275936. | Chatterjee S, Sulivan HA, MacLennan BJ, Xu R, Hou Y, Lavin TK, Lea NE, Michalski JE, Babcock KR, Dietrich S, Matthevvs GA, Beyeler A, Calhoon GG, Glober G, Whitesell JD, Yao S, Cetin A, Harris JA, Zeng H, Tye KM, Reid RC, Wickersham IR. Nat Neurosci. 2018 Apr,21(4): 638-646 | ||||
“From genes to the whole brain, the Wyss Center unifies neuroscience at all scales. By pursuing innovations and new approaches in neurobiology, neuroimaging and neurotechnology, we reveal unique insights into the mechanisms underlying the dynamics of the brain and the treatment of disease. We use this interdisciplinary knowledge to accelerate the development of devices and therapies for unmet medical needs. Our current work addresses indications such as epilepsy, Alzheimer’s disease and locked-in syndrome as a result of ALS (amyotrophic lateral sclerosis) or brainstem stroke.” | motor coordination | 36 | Neurobiology: Advancing our understanding of the brain's complex circuitry | Neurotechnology development: Combining neuroscience, advan ced engineering and manufacturing | Data analysis and Software development: Advanced, multimodal brain data collection and analysis | Spinal Cord fMRI: a new window into the central nervous system. Kinany, N., Pirondini, E., Micera, S., et al. | High-resolution histological mapping of the human brain as a tool for translational psychiatric neuroscience Jorda, T., Scholler, J., Osterop, S., et al.* | ||||
Carnegie Mellon University | “Our group seeks to elucidate how large populations of neurons process information, from encoding sensory stimuli to guiding motor actions. Most neurophysiological studies to date involve studying one neuron at a time. Although one neuron can be informative about the sensory stimulus or motor action, it often doesn't tell the full story. While this provides the motivation for looking across a neural population, the heterogeneity of the activity of different neurons can be baffling” | signal processingmotor coordination | 13 | To develop and apply novel signal processing and machine learning algorithms to explain the high-dimensional structure and timecourse of neural population activity. | To apply this knowledge to the design of next-generation biomedical devices that interface with large populations of neurons. | intersection of signal processing / machine learning, biomedical engineering, and basic neuroscience. | “Learning alters neural activity to simultaneously support memory and action” by D. M. Losey, J. A. Hennig†, E. R. Oby†, M. D. Golub, P. T. Sadtler, K. M. Quick, S. I. Ryu, E. C. Tyler-Kabara, A. P. Batista*, B. M. Yu*, S. M. Chase*. bioRxiv. | “Dimensionality reduction of calcium-imaged neuronal population activity” by T. Koh, W. E. Bishop, T. Kawashima, B. B. Jeon, R. Srinivasan, S. J. Kuhlman, M. B. Ahrens, S. M. Chase*, B. M. Yu*. bioRxiv. | |||
MIT |