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Lee MJ, Eden J, Gurgone S, Berger DJ, Borzelli D, d'Avella A, Mehring C, Burdet E. Control limitations in the null-space of the wrist muscle system. Sci Rep 2024; 14:20634. [PMID: 39232018 PMCID: PMC11375119 DOI: 10.1038/s41598-024-69353-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 08/03/2024] [Indexed: 09/06/2024] Open
Abstract
The redundancy present within the musculoskeletal system may offer a non-invasive source of signals for movement augmentation, where the set of muscle activations that do not produce force/torque (muscle-to-force null-space) could be controlled simultaneously to the natural limbs. Here, we investigated the viability of extracting movement augmentation control signals from the muscles of the wrist complex. Our study assessed (i) if controlled variation of the muscle activation patterns in the wrist joint's null-space is possible; and (ii) whether force and null-space cursor targets could be reached concurrently. During the null-space target reaching condition, participants used muscle-to-force null-space muscle activation to move their cursor towards a displayed target while minimising the exerted force as visualised through the cursor's size. Initial targets were positioned to require natural co-contraction in the null-space and if participants showed a consistent ability to reach for their current target, they would rotate 5∘ incrementally to generate muscle activation patterns further away from their natural co-contraction. In contrast, during the concurrent target reaching condition participants were required to match a target position and size, where their cursor position was instead controlled by their exerted flexion-extension and radial-ulnar deviation, while its size was changed by their natural co-contraction magnitude. The results collected from 10 participants suggest that while there was variation in each participant's co-contraction behaviour, most did not possess the ability to control this variation for muscle-to-force null-space virtual reaching. In contrast, participants did show a direction and target size dependent ability to vary isometric force and co-contraction activity concurrently. Our results indicate the limitations of using the muscle-to-force null-space activity of joints with a low level of redundancy as a possible command signal for movement augmentation.
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Affiliation(s)
- Meng-Jung Lee
- Bernstein Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany.
- Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.
| | - Jonathan Eden
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, W12 0BZ, UK.
- Mechanical Engineering Department, The University of Melbourne, Victoria, Australia.
| | - Sergio Gurgone
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, 1-4, Yamadaoka, Suita, Osaka, Japan
| | - Denise J Berger
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
- Department of Systems Medicine and Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy
| | - Daniele Borzelli
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Andrea d'Avella
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Carsten Mehring
- Bernstein Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany
- Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
- BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany
| | - Etienne Burdet
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, W12 0BZ, UK
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2
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Carroll AM, Pruitt DT, Riley JR, Danaphongse TT, Rennaker RL, Engineer CT, Hays SA, Kilgard MP. Vagus nerve stimulation during training fails to improve learning in healthy rats. Sci Rep 2024; 14:18955. [PMID: 39147873 PMCID: PMC11327266 DOI: 10.1038/s41598-024-69666-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 08/07/2024] [Indexed: 08/17/2024] Open
Abstract
Learning new skills requires neuroplasticity. Vagus nerve stimulation (VNS) during sensory and motor events can increase neuroplasticity in networks related to these events and might therefore serve to facilitate learning on sensory and motor tasks. We tested if VNS could broadly improve learning on a wide variety of tasks across different skill domains in healthy, female adult rats. VNS was paired with presentation of stimuli or on successful trials during training, strategies known to facilitate plasticity and improve recovery in models of neurological disorders. VNS failed to improve either rate of learning or performance for any of the tested tasks, which included skilled forelimb motor control, speech sound discrimination, and paired-associates learning. These results contrast recent findings from multiple labs which found VNS pairing during training produced learning enhancements across motor, auditory, and cognitive domains. We speculate that these contrasting results may be explained by key differences in task designs, training timelines and animal handling approaches, and that while VNS may be able to facilitate rapid and early learning processes in healthy subjects, it does not broadly enhance learning for difficult tasks.
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Affiliation(s)
- Alan M Carroll
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA.
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA.
| | - David T Pruitt
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
| | - Jonathan R Riley
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
| | - Tanya T Danaphongse
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
| | - Robert L Rennaker
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
| | - Crystal T Engineer
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
| | - Seth A Hays
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
| | - Michael P Kilgard
- The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA
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3
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Wu S, Zhang X, Wang Y. Neural Manifold Constraint for Spike Prediction Models Under Behavioral Reinforcement. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2772-2781. [PMID: 39074025 DOI: 10.1109/tnsre.2024.3435568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Spike prediction models effectively predict downstream spike trains from upstream neural activity for neural prostheses. Such prostheses could potentially restore damaged neural communication pathways using predicted patterns to guide electrical stimulations on downstream. Since the ground truth of downstream neural activity is unavailable for subjects with the damage, reinforcement learning (RL) with behavior-level rewards becomes necessary for model training. However, existing models do not involve any constraint on the generated firing patterns and neglect the correlations among neural activities. Thus, the model outputs can greatly deviate from the natural range of neural activities, causing concerns for clinical usage. This study proposes the neural manifold constraint to solve this problem, shaping RL-generated spike trains in the feature space. The constraint terms describe the first and second order statistics of the neural manifold estimated from neural recordings during subjects' freely moving period. Then, the models can be optimized within the neural manifold by behavioral reinforcement. We test the method to predict primary motor cortex (M1) spikes from medial prefrontal (mPFC) spikes when rats perform the two-lever discrimination task. Results show that the neural activity generated by constrained models resembles the real M1 recordings. Compared with models without constraints, our approach achieves similar behavioral success rates, but reduces the mean squared error of neural firing by 61%. The constraints also increase the model's robustness across data segments and induce realistic neural correlations. Our method provides a promising tool to restore transregional communication with high behavioral performance and more realistic microscopic patterns.
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4
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Silva AB, Littlejohn KT, Liu JR, Moses DA, Chang EF. The speech neuroprosthesis. Nat Rev Neurosci 2024; 25:473-492. [PMID: 38745103 DOI: 10.1038/s41583-024-00819-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2024] [Indexed: 05/16/2024]
Abstract
Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore natural communication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinical epilepsy monitoring and subsequently in individuals with paralysis as part of early feasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapid and substantial progress in decoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.
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Affiliation(s)
- Alexander B Silva
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - Kaylo T Littlejohn
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Jessie R Liu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - David A Moses
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA.
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5
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Driscoll LN, Shenoy K, Sussillo D. Flexible multitask computation in recurrent networks utilizes shared dynamical motifs. Nat Neurosci 2024; 27:1349-1363. [PMID: 38982201 PMCID: PMC11239504 DOI: 10.1038/s41593-024-01668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 04/26/2024] [Indexed: 07/11/2024]
Abstract
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses revealed learned computational strategies mirroring the modular subtask structure of the training task set. Dynamical motifs, which are recurring patterns of neural activity that implement specific computations through dynamics, such as attractors, decision boundaries and rotations, were reused across tasks. For example, tasks requiring memory of a continuous circular variable repurposed the same ring attractor. We showed that dynamical motifs were implemented by clusters of units when the unit activation function was restricted to be positive. Cluster lesions caused modular performance deficits. Motifs were reconfigured for fast transfer learning after an initial phase of learning. This work establishes dynamical motifs as a fundamental unit of compositional computation, intermediate between neuron and network. As whole-brain studies simultaneously record activity from multiple specialized systems, the dynamical motif framework will guide questions about specialization and generalization.
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Affiliation(s)
- Laura N Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Krishna Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
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6
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Kim JH, Daie K, Li N. A combinatorial neural code for long-term motor memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597627. [PMID: 38895416 PMCID: PMC11185691 DOI: 10.1101/2024.06.05.597627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouse's lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.
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7
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Oesch LT, Ryan MB, Churchland AK. From innate to instructed: A new look at perceptual decision-making. Curr Opin Neurobiol 2024; 86:102871. [PMID: 38569230 PMCID: PMC11162954 DOI: 10.1016/j.conb.2024.102871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024]
Abstract
Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.
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Affiliation(s)
- Lukas T Oesch
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
| | - Michael B Ryan
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States. https://twitter.com/NeuroMikeRyan
| | - Anne K Churchland
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States.
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8
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Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity that shapes adaptation. Nat Commun 2024; 15:4084. [PMID: 38744847 PMCID: PMC11094149 DOI: 10.1038/s41467-024-48008-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
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Affiliation(s)
- Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Mila, Québec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK.
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9
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Fortunato C, Bennasar-Vázquez J, Park J, Chang JC, Miller LE, Dudman JT, Perich MG, Gallego JA. Nonlinear manifolds underlie neural population activity during behaviour. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549575. [PMID: 37503015 PMCID: PMC10370078 DOI: 10.1101/2023.07.18.549575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
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Affiliation(s)
- Cátia Fortunato
- Department of Bioengineering, Imperial College London, London UK
| | | | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Joanna C. Chang
- Department of Bioengineering, Imperial College London, London UK
| | - Lee E. Miller
- Department of Neurosciences, Northwestern University, Chicago IL, USA
- Department of Biomedical Engineering, Northwestern University, Chicago IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago IL, USA, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T. Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Matthew G. Perich
- Department of Neurosciences, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada
- Québec Artificial Intelligence Institute (MILA), Montréal, Québec, Canada
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London UK
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10
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Losey DM, Hennig JA, Oby ER, Golub MD, Sadtler PT, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Yu BM, Chase SM. Learning leaves a memory trace in motor cortex. Curr Biol 2024; 34:1519-1531.e4. [PMID: 38531360 PMCID: PMC11097210 DOI: 10.1016/j.cub.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 12/06/2023] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.
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Affiliation(s)
- Darby M Losey
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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11
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Rasero J, Betzel R, Sentis AI, Kraynak TE, Gianaros PJ, Verstynen T. Similarity in evoked responses does not imply similarity in macroscopic network states. Netw Neurosci 2024; 8:335-354. [PMID: 38711543 PMCID: PMC11073549 DOI: 10.1162/netn_a_00354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/17/2023] [Indexed: 05/08/2024] Open
Abstract
It is commonplace in neuroscience to assume that if two tasks activate the same brain areas in the same way, then they are recruiting the same underlying networks. Yet computational theory has shown that the same pattern of activity can emerge from many different underlying network representations. Here we evaluated whether similarity in activation necessarily implies similarity in network architecture by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N = 242). Participants performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. We found that the two tasks were much more different in their network profiles than in their evoked activity patterns at different analytical levels, as well as for a wide range of methodological pipelines. Our results reject the notion that having the same activation patterns means two tasks engage the same underlying representations, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels.
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Affiliation(s)
- Javier Rasero
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Amy Isabella Sentis
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
| | - Thomas E. Kraynak
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter J. Gianaros
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy Verstynen
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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12
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Churchland MM, Shenoy KV. Preparatory activity and the expansive null-space. Nat Rev Neurosci 2024; 25:213-236. [PMID: 38443626 DOI: 10.1038/s41583-024-00796-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.
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Affiliation(s)
- Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
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13
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Losanno E, Badi M, Roussinova E, Bogaard A, Delacombaz M, Shokur S, Micera S. An Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:271-280. [PMID: 38766541 PMCID: PMC11100864 DOI: 10.1109/ojemb.2024.3381475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 02/06/2024] [Accepted: 03/11/2024] [Indexed: 05/22/2024] Open
Abstract
Objective: Brain-body interfaces (BBIs) have emerged as a very promising solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present a preliminary investigation in a monkey of a brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, aiming at achieving ease of learning and long-term robustness. Results: We identified an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkey's neural activity associated to reach-to-grasp movements. We then tested the animal's ability to directly control a computer cursor using cortical activation along the manifold axes. By daily recalibrating only scaling factors, we achieved rapid learning and stable high performance in simple, incremental 2D tasks over more than 12 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger voluntary hand movements. Conclusions: These results represent a proof of concept of manifold-based direct control for BBI applications.
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Affiliation(s)
- E. Losanno
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
| | - M. Badi
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - E. Roussinova
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - A. Bogaard
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - M. Delacombaz
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - S. Shokur
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - S. Micera
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
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14
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Osuna-Orozco R, Zhao Y, Stealey HM, Lu HY, Contreras-Hernandez E, Santacruz SR. Adaptation and learning as strategies to maximize reward in neurofeedback tasks. Front Hum Neurosci 2024; 18:1368115. [PMID: 38590363 PMCID: PMC11000125 DOI: 10.3389/fnhum.2024.1368115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/04/2024] [Indexed: 04/10/2024] Open
Abstract
Introduction Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations. Methods Results for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent. Results and discussion Our analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning.
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Affiliation(s)
- Rodrigo Osuna-Orozco
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States
| | - Yi Zhao
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States
| | - Hannah Marie Stealey
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States
| | - Hung-Yun Lu
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States
| | | | - Samantha Rose Santacruz
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States
- Institute for Neuroscience, University of Texas at Austin, Austin, TX, United States
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15
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Rajeswaran P, Payeur A, Lajoie G, Orsborn AL. Assistive sensory-motor perturbations influence learned neural representations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585972. [PMID: 38562772 PMCID: PMC10983972 DOI: 10.1101/2024.03.20.585972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.
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Affiliation(s)
| | - Alexandre Payeur
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Guillaume Lajoie
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Amy L. Orsborn
- University of Washington, Bioengineering, Seattle, 98115, USA
- University of Washington, Electrical and Computer Engineering, Seattle, 98115, USA
- Washington National Primate Research Center, Seattle, Washington, 98115, USA
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16
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Pun TK, Khoshnevis M, Hosman T, Wilson GH, Kapitonava A, Kamdar F, Henderson JM, Simeral JD, Vargas-Irwin CE, Harrison MT, Hochberg LR. Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.29.582733. [PMID: 38496552 PMCID: PMC10942277 DOI: 10.1101/2024.02.29.582733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.
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17
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Ahmadipour P, Sani OG, Pesaran B, Shanechi MM. Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity. J Neural Eng 2024; 21:026001. [PMID: 38016450 PMCID: PMC10913727 DOI: 10.1088/1741-2552/ad1053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/23/2023] [Accepted: 11/28/2023] [Indexed: 11/30/2023]
Abstract
Objective.Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales.Approach.Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior.Main results.We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior.Significance.Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.
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Affiliation(s)
- Parima Ahmadipour
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Omid G Sani
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Thomas Lord Department of Computer Science, Alfred E. Mann Department of Biomedical Engineering, and the Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
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18
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Nakai S, Kitanishi T, Mizuseki K. Distinct manifold encoding of navigational information in the subiculum and hippocampus. SCIENCE ADVANCES 2024; 10:eadi4471. [PMID: 38295173 PMCID: PMC10830115 DOI: 10.1126/sciadv.adi4471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 12/29/2023] [Indexed: 02/02/2024]
Abstract
The subiculum (SUB) plays a crucial role in spatial navigation and encodes navigational information differently from the hippocampal CA1 area. However, the representation of subicular population activity remains unknown. Here, we investigated the neuronal population activity recorded extracellularly from the CA1 and SUB of rats performing T-maze and open-field tasks. The trajectory of population activity in both areas was confined to low-dimensional neural manifolds homoeomorphic to external space. The manifolds conveyed position, speed, and future path information with higher decoding accuracy in the SUB than in the CA1. The manifolds exhibited common geometry across rats and regions for the CA1 and SUB and between tasks in the SUB. During post-task ripples in slow-wave sleep, population activity represented reward locations/events more frequently in the SUB than in CA1. Thus, the CA1 and SUB encode information distinctly into the neural manifolds that underlie navigational information processing during wakefulness and sleep.
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Affiliation(s)
- Shinya Nakai
- Department of Physiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka 545-8585, Japan
| | - Takuma Kitanishi
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka 545-8585, Japan
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Meguro, Tokyo 153-8902, Japan
- Komaba Institute for Science, The University of Tokyo, Meguro, Tokyo 153-8902, Japan
- PRESTO, Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
| | - Kenji Mizuseki
- Department of Physiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka 545-8585, Japan
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19
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Altahini S, Arnoux I, Stroh A. Optogenetics 2.0: challenges and solutions towards a quantitative probing of neural circuits. Biol Chem 2024; 405:43-54. [PMID: 37650383 DOI: 10.1515/hsz-2023-0194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/02/2023] [Indexed: 09/01/2023]
Abstract
To exploit the full potential of optogenetics, we need to titrate and tailor optogenetic methods to emulate naturalistic circuit function. For that, the following prerequisites need to be met: first, we need to target opsin expression not only to genetically defined neurons per se, but to specifically target a functional node. Second, we need to assess the scope of optogenetic modulation, i.e. the fraction of optogenetically modulated neurons. Third, we need to integrate optogenetic control in a closed loop setting. Fourth, we need to further safe and stable gene expression and light delivery to bring optogenetics to the clinics. Here, we review these concepts for the human and rodent brain.
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Affiliation(s)
- Saleh Altahini
- Leibniz Institute for Resilience Research, D-55122 Mainz, Germany
| | - Isabelle Arnoux
- Cerebral Physiopathology Laboratory, Center for Interdisciplinary Research in Biology, College de France, Centre national de la recherche scientifique, Institut national de la santé et de la recherche médicale, Université PSL, F-75005 Paris, France
| | - Albrecht Stroh
- Leibniz Institute for Resilience Research, D-55122 Mainz, Germany
- Institute of Pathophysiology, University Medical Center Mainz, D-55128 Mainz, Germany
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20
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Zhang C, Wang H, Tang S, Li Z. Rhesus monkeys learn to control a directional-key inspired brain machine interface via bio-feedback. PLoS One 2024; 19:e0286742. [PMID: 38232123 DOI: 10.1371/journal.pone.0286742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 05/23/2023] [Indexed: 01/19/2024] Open
Abstract
Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount of work has focused on the arguably more successful bio-mimetic approach. However, as chronic recordings have become more accessible and utilization of novel appendages such as computer cursors have become more universal, users can more easily spend time learning in a bio-feedback control paradigm. We believe a simple approach which leverages user learning and few assumptions will provide users with good control ability. To test the feasibility of this idea, we implemented a simple firing-rate-to-motion correspondence rule, assigned groups of neurons to virtual "directional keys" for control of a 2D cursor. Though not strictly required, to facilitate initial control, we selected neurons with similar preferred directions for each group. The groups of neurons were kept the same across multiple recording sessions to allow learning. Two Rhesus monkeys used this BMI to perform a center-out cursor movement task. After about a week of training, monkeys performed the task better and neuronal signal patterns changed on a group basis, indicating learning. While our experiments did not compare this bio-feedback BMI to bio-mimetic BMIs, the results demonstrate the feasibility of our control paradigm and paves the way for further research in multi-dimensional bio-feedback BMIs.
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Affiliation(s)
- Chenguang Zhang
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
| | - Hao Wang
- Institute of Big Data and Artificial Intelligence, China Telecom Corporation Limited Beijing Research Institute, Beijing, China
| | - Shaohua Tang
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China
- School of Systems Science, Beijing Normal University, Beijing, China
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
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21
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Oby ER, Degenhart AD, Grigsby EM, Motiwala A, McClain NT, Marino PJ, Yu BM, Batista AP. Dynamical constraints on neural population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.573543. [PMID: 38260549 PMCID: PMC10802336 DOI: 10.1101/2024.01.03.573543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.
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22
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Griggs WS, Norman SL, Deffieux T, Segura F, Osmanski BF, Chau G, Christopoulos V, Liu C, Tanter M, Shapiro MG, Andersen RA. Decoding motor plans using a closed-loop ultrasonic brain-machine interface. Nat Neurosci 2024; 27:196-207. [PMID: 38036744 PMCID: PMC10774125 DOI: 10.1038/s41593-023-01500-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 10/16/2023] [Indexed: 12/02/2023]
Abstract
Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.
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Affiliation(s)
- Whitney S Griggs
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
| | - Sumner L Norman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Thomas Deffieux
- Physics for Medicine Paris, INSERM, CNRS, ESPCI Paris, PSL Research University, Paris, France
- INSERM Technology Research Accelerator in Biomedical Ultrasound, Paris, France
| | - Florian Segura
- Physics for Medicine Paris, INSERM, CNRS, ESPCI Paris, PSL Research University, Paris, France
- INSERM Technology Research Accelerator in Biomedical Ultrasound, Paris, France
| | | | - Geeling Chau
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Vasileios Christopoulos
- T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
- Department of Bioengineering, University of California, Riverside, Riverside, CA, USA
| | - Charles Liu
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, USA
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA
| | - Mickael Tanter
- Physics for Medicine Paris, INSERM, CNRS, ESPCI Paris, PSL Research University, Paris, France
- INSERM Technology Research Accelerator in Biomedical Ultrasound, Paris, France
| | - Mikhail G Shapiro
- Division of Chemistry & Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA
- Howard Hughes Medical Institute, Pasadena, CA, USA
| | - Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
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23
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Ohki T, Kunii N, Chao ZC. Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0. Rev Neurosci 2023; 34:839-868. [PMID: 36960579 DOI: 10.1515/revneuro-2022-0137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/26/2023] [Indexed: 03/25/2023]
Abstract
There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.
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Affiliation(s)
- Takefumi Ohki
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
| | - Naoto Kunii
- Department of Neurosurgery, The University of Tokyo, Tokyo 113-0033, Japan
| | - Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
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24
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Gurnani H, Cayco Gajic NA. Signatures of task learning in neural representations. Curr Opin Neurobiol 2023; 83:102759. [PMID: 37708653 DOI: 10.1016/j.conb.2023.102759] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/28/2023] [Accepted: 07/20/2023] [Indexed: 09/16/2023]
Abstract
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level.
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Affiliation(s)
- Harsha Gurnani
- Department of Biology, University of Washington, Seattle, WA, USA. https://twitter.com/HarshaGurnani
| | - N Alex Cayco Gajic
- Laboratoire de Neuroscience Cognitives, Ecole Normale Supérieure, Université PSL, Paris, France.
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25
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Xia M, Agca BN, Yoshida T, Choi J, Amjad U, Bose K, Keren N, Zukerman S, Cima MJ, Graybiel AM, Schwerdt HN. Scalable, flexible carbon fiber electrode thread arrays for three-dimensional probing of neurochemical activity in deep brain structures of rodents. Biosens Bioelectron 2023; 241:115625. [PMID: 37708685 PMCID: PMC10591823 DOI: 10.1016/j.bios.2023.115625] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/17/2023] [Accepted: 08/19/2023] [Indexed: 09/16/2023]
Abstract
We developed a flexible "electrode-thread" array for recording dopamine neurochemicals from a lateral distribution of subcortical targets (up to 16) transverse to the axis of insertion. Ultrathin (∼10 μm diameter) carbon fiber (CF) electrode-threads (CFETs) are clustered into a tight bundle to introduce them into the brain from a single-entry point. The individual CFETs splay laterally in deep brain tissue during insertion due to their innate flexibility. This spatial redistribution allows navigation of the CFETs towards deep brain targets spreading horizontally from the axis of insertion. Commercial "linear" arrays provide single-entry insertion but only allow measurements along the axis of insertion. Horizontally configured arrays inflict separate penetrations for each individual channel. We tested functional performance of our CFET arrays in vivo for recording dopamine and for providing lateral spread to multiple distributed sites in the rat striatum. Spatial spread was further characterized in agar brain phantoms as a function of insertion depth. We also developed protocols to slice the embedded CFETs within fixed brain tissue using standard histology. This method allowed extraction of the precise spatial coordinates of the implanted CFETs and their recording sites as integrated with immunohistochemical staining for surrounding anatomical, cytological, and protein expression labels. Our CFET array has the potential to unlock a wide range of applications, from uncovering the role of neuromodulators in synaptic plasticity, to addressing critical safety barriers in clinical translation towards diagnostic and adaptive treatment in Parkinson's disease and major mood disorders.
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Affiliation(s)
- Mingyi Xia
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Busra Nur Agca
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Tomoko Yoshida
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Jiwon Choi
- Department of Bioengineering, University of Pittsburgh, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Usamma Amjad
- Department of Bioengineering, University of Pittsburgh, USA
| | - Kade Bose
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Nikol Keren
- Department of Bioengineering, University of Pittsburgh, USA
| | | | - Michael J Cima
- Koch Institute for Integrative Cancer Research and Department of Materials Science, Massachusetts Institute of Technology, USA
| | - Ann M Graybiel
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Helen N Schwerdt
- Department of Bioengineering, University of Pittsburgh, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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26
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Chen K, Forrest A, Gonzalez Burgos G, Kozai TDY. Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.06.565852. [PMID: 37986883 PMCID: PMC10659303 DOI: 10.1101/2023.11.06.565852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Objective This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders. Approach This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation. Main Results The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations. Significance This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.
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27
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Safaie M, Chang JC, Park J, Miller LE, Dudman JT, Perich MG, Gallego JA. Preserved neural dynamics across animals performing similar behaviour. Nature 2023; 623:765-771. [PMID: 37938772 PMCID: PMC10665198 DOI: 10.1038/s41586-023-06714-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/04/2023] [Indexed: 11/09/2023]
Abstract
Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.
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Affiliation(s)
- Mostafa Safaie
- Department of Bioengineering, Imperial College London, London, UK
| | - Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, TX, USA
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, TX, USA
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada.
- Mila, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada.
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
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28
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Abbasi A, Lassagne H, Estebanez L, Goueytes D, Shulz DE, Ego-Stengel V. Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback. SCIENCE ADVANCES 2023; 9:eadh1328. [PMID: 37738340 PMCID: PMC10516504 DOI: 10.1126/sciadv.adh1328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 08/23/2023] [Indexed: 09/24/2023]
Abstract
Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal spatiotemporal properties of the sensorimotor cortical integration that set constraints on the design of neuroprosthetics.
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Affiliation(s)
| | | | | | - Dorian Goueytes
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay (NeuroPSI), 91400 Saclay, France
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29
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Wu S, Huang C, Snyder A, Smith M, Doiron B, Yu B. Automated customization of large-scale spiking network models to neuronal population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558920. [PMID: 37790533 PMCID: PMC10542160 DOI: 10.1101/2023.09.21.558920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.
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Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Chengcheng Huang
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Snyder
- Department of Neuroscience, University of Rochester, Rochester, NY, USA
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
- Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Matthew Smith
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Byron Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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30
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Heald JB, Wolpert DM, Lengyel M. The Computational and Neural Bases of Context-Dependent Learning. Annu Rev Neurosci 2023; 46:233-258. [PMID: 36972611 PMCID: PMC10348919 DOI: 10.1146/annurev-neuro-092322-100402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.
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Affiliation(s)
- James B Heald
- Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; ,
| | - Daniel M Wolpert
- Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; ,
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom;
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom;
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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31
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Abstract
Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.
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Affiliation(s)
- Maria C Dadarlat
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Ryan A Canfield
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Amy L Orsborn
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
- Washington National Primate Research Center, Seattle, Washington, USA
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32
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Ahmadipour P, Sani OG, Pesaran B, Shanechi MM. Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542509. [PMID: 37398400 PMCID: PMC10312539 DOI: 10.1101/2023.05.26.542509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.
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33
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Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity space that shapes adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.23.541925. [PMID: 37293081 PMCID: PMC10245862 DOI: 10.1101/2023.05.23.541925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes in neural connectivity which determine the activity patterns that can be produced. Here, we sought to understand how a neural population's activity repertoire, acquired through long-term learning, affects short-term adaptation by modeling motor cortical neural population dynamics during de novo learning and subsequent adaptation using recurrent neural networks. We trained these networks on different motor repertoires comprising varying numbers of movements. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization created by the neural population activity patterns corresponding to each movement. This structure facilitated adaptation, but only when small changes in motor output were required, and when the structure of the network inputs, the neural activity space, and the perturbation were congruent. These results highlight trade-offs in skill acquisition and demonstrate how prior experience and external cues during learning can shape the geometrical properties of neural population activity as well as subsequent adaptation.
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Affiliation(s)
- Joanna C. Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G. Perich
- Département de neurosciences, Université de Montréal, Montréal, Canada
| | - Lee E. Miller
- Department of Neuroscience, Northwestern University, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London, UK
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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34
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Xia M, Agca BN, Yoshida T, Choi J, Amjad U, Bose K, Keren N, Zukerman S, Cima MJ, Graybiel AM, Schwerdt HN. Scalable, flexible carbon fiber electrode thread arrays for three-dimensional spatial profiling of neurochemical activity in deep brain structures of rodents. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.15.537033. [PMID: 37131810 PMCID: PMC10153108 DOI: 10.1101/2023.04.15.537033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We developed a flexible "electrode-thread" array for recording dopamine neurochemical activity from a lateral distribution of subcortical targets (up to 16) transverse to the axis of insertion. Ultrathin (∼ 10 µm diameter) carbon fiber (CF) electrode-threads (CFETs) are clustered into a tight bundle to introduce them into the brain from a single entry point. The individual CFETs splay laterally in deep brain tissue during insertion due to their innate flexibility. This spatial redistribution allows navigation of the CFETs towards deep brain targets spreading horizontally from the axis of insertion. Commercial "linear" arrays provide single entry insertion but only allow measurements along the axis of insertion. Horizontally configured neurochemical recording arrays inflict separate penetrations for each individual channel (i.e., electrode). We tested functional performance of our CFET arrays in vivo for recording dopamine neurochemical dynamics and for providing lateral spread to multiple distributed sites in the striatum of rats. Spatial spread was further characterized using agar brain phantoms to measure electrode deflection as a function of insertion depth. We also developed protocols to slice the embedded CFETs within fixed brain tissue using standard histology techniques. This method allowed extraction of the precise spatial coordinates of the implanted CFETs and their recording sites as integrated with immunohistochemical staining for surrounding anatomical, cytological, and protein expression labels. Neurochemical recording operations tested here can be integrated with already widely established capabilities of CF-based electrodes to record single neuron activity and local field potentials, to enable multi-modal recording functions. Our CFET array has the potential to unlock a wide range of applications, from uncovering the role of neuromodulators in synaptic plasticity, to addressing critical safety barriers in clinical translation towards diagnostic and adaptive treatment in Parkinson's disease and major mood disorders.
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Affiliation(s)
- Mingyi Xia
- McGovern Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Busra Nur Agca
- McGovern Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Tomoko Yoshida
- McGovern Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Jiwon Choi
- Department of Bioengineering, University of Pittsburgh
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD
| | - Usamma Amjad
- Department of Bioengineering, University of Pittsburgh
| | - Kade Bose
- McGovern Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Nikol Keren
- Department of Bioengineering, University of Pittsburgh
| | | | - Michael J Cima
- Koch Institute for Integrative Cancer Research and Department of Materials Science, Massachusetts Institute of Technology
| | - Ann M Graybiel
- McGovern Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Helen N Schwerdt
- Department of Bioengineering, University of Pittsburgh
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD
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35
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Makin TR, Micera S, Miller LE. Neurocognitive and motor-control challenges for the realization of bionic augmentation. Nat Biomed Eng 2023; 7:344-348. [PMID: 36050524 PMCID: PMC9975114 DOI: 10.1038/s41551-022-00930-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Robotic fingers and arms that augment the motor abilities of non-disabled individuals are increasingly feasible yet face neurocognitive barriers and hurdles in efferent motor control.
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Affiliation(s)
- Tamar R Makin
- Institute of Cognitive Neuroscience, University College London, London, UK.
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy.
| | - Lee E Miller
- Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
- Shirley Ryan AbilityLab, Chicago, IL, USA.
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36
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Cregg JM, Mirdamadi JL, Fortunato C, Okorokova EV, Kuper C, Nayeem R, Byun AJ, Avraham C, Buonocore A, Winner TS, Mildren RL. Highlights from the 31st Annual Meeting of the Society for the Neural Control of Movement. J Neurophysiol 2023; 129:220-234. [PMID: 36541602 PMCID: PMC9844973 DOI: 10.1152/jn.00500.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Jared M Cregg
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jasmine L Mirdamadi
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Cátia Fortunato
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | | | - Clara Kuper
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rashida Nayeem
- Department of Electrical Engineering, Northeastern University, Boston, Massachusetts
| | - Andrew J Byun
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - Chen Avraham
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beersheva, Israel
| | - Antimo Buonocore
- Werner Reichardt Centre for Integrative Neuroscience, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Department of Educational, Psychological and Communication Sciences, Suor Orsola Benincasa University, Naples, Italy
| | - Taniel S Winner
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
| | - Robyn L Mildren
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
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37
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Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability. Commun Biol 2022; 5:1375. [PMID: 36522455 PMCID: PMC9755311 DOI: 10.1038/s42003-022-04326-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Human behavior is not performed completely as desired, but is influenced by the inherent rhythmicity of the brain. Here we show that anti-phase bimanual coordination stability is regulated by the dynamics of pre-movement neural oscillations in bi-hemispheric primary motor cortices (M1) and supplementary motor area (SMA). In experiment 1, pre-movement bi-hemispheric M1 phase synchrony in beta-band (M1-M1 phase synchrony) was online estimated from 129-channel scalp electroencephalograms. Anti-phase bimanual tapping preceded by lower M1-M1 phase synchrony exhibited significantly longer duration than tapping preceded by higher M1-M1 phase synchrony. Further, the inter-individual variability of duration was explained by the interaction of pre-movement activities within the motor network; lower M1-M1 phase synchrony and spectral power at SMA were associated with longer duration. The necessity of cortical interaction for anti-phase maintenance was revealed by sham-controlled repetitive transcranial magnetic stimulation over SMA in another experiment. Our results demonstrate that pre-movement cortical oscillatory coupling within the motor network unknowingly influences bimanual coordination performance in humans after consolidation, suggesting the feasibility of augmenting human motor ability by covertly monitoring preparatory neural dynamics.
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De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex. eNeuro 2022; 9:ENEURO.0145-22.2022. [PMID: 36376067 PMCID: PMC9721308 DOI: 10.1523/eneuro.0145-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 11/15/2022] Open
Abstract
Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.
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Cometa A, Falasconi A, Biasizzo M, Carpaneto J, Horn A, Mazzoni A, Micera S. Clinical neuroscience and neurotechnology: An amazing symbiosis. iScience 2022; 25:105124. [PMID: 36193050 PMCID: PMC9526189 DOI: 10.1016/j.isci.2022.105124] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction.
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Affiliation(s)
- Andrea Cometa
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Antonio Falasconi
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
- Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Marco Biasizzo
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Jacopo Carpaneto
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Andreas Horn
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology, 10117 Berlin, Germany
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Translational Neural Engineering Lab, School of Engineering, École Polytechnique Fèdèrale de Lausanne, 1015 Lausanne, Switzerland
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40
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Zhang JJ, Yang YL, Hu J, Zhao CF, He XH, Yang QY, Qi XS, Lu H, He C, Liu H. Development and validation of a novel model based on hand knob score and white matter injury on MRI to predict hand function in children with cerebral palsy. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1102. [PMID: 36388818 PMCID: PMC9652546 DOI: 10.21037/atm-22-4112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/30/2022] [Indexed: 08/30/2023]
Abstract
BACKGROUND Childhood hand function is considered to be one of the strongest predictors of the ability to participate in daily activities as children with cerebral palsy (CP) reach adulthood. The manual ability classification system (MACS) is currently the most widely used for grading hand function in children with CP. However, the MACS method is subjective and may be affected by the raters' experience. Hand knob is an important control center for hand movement. Therefor this study aimed to develop and validate an objective model for hand function estimation in children with CP and visualize it as a nomogram. METHODS A total of 70 Children (2-12 years old) with CP underwent magnetic resonance imaging (MRI) scanning, MACS assessment. According to MACS, children with CP were divided into mild impairment group (grade I-III) and severe impairment group (grade IV-V). Hand function prediction models based on (I) hand knob score, (II) clinical features, and (III) the combination of clinical features and hand knob score were developed and validated separately. The models were subjected to stepwise regression according to the maximum likelihood method, and the Akaike information criterion was used to select the best model. Model discrimination was assessed using receiver operating characteristic (ROC) and calibration curves. The nomogram was finally built according to the best model. RESULTS The area under the curve (AUC) of the hand knob score model in the training set was 0.752, the clinical features model was 0.819, and the hand knob score and clinical features combined model was 0.880. The AUC of the hand knob score model in the validation set was 0.765, the clinical features model was 0.782, and the combined model was 0.894. The best model was the hand knob score-clinical features combined model, and the nomogram finally incorporated two assessment items: the hand knob score and white matter injury. The estimated probability of hand function injury degree of the combined model displayed good agreement with the actual occurrence probability. CONCLUSIONS The hand knob score-clinical features combined model can be used to preliminarily assess the degree of hand impairment in children with CP, with good calibration.
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Affiliation(s)
- Jing-Jing Zhang
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Department of Radiology, Mianyang Hospital of T.C.M, Mianyang, China
| | - Yan-Li Yang
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jie Hu
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Chun-Feng Zhao
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xing-Hong He
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Qian-Yu Yang
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xiao-Shan Qi
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Hong Lu
- Department of Radiology, The Seventh People’s Hospital of Chongqing, The Central Hospital Affiliated to Chongqing University of Technology, Chongqing, China
| | - Cheng He
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing, China
| | - Heng Liu
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
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41
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Bretton-Granatoor Z, Stealey H, Santacruz SR, Lewis-Peacock JA. Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data. IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE : HEALTHCARE TECHNOLOGY : [PROCEEDINGS]. IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE 2022; 2022:650-654. [PMID: 36820790 PMCID: PMC9942267 DOI: 10.1109/biocas54905.2022.9948604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/-4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.
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Affiliation(s)
| | | | - Samantha R. Santacruz
- Department of Biomedical Engineering The University of Texas at Austin Austin, TX, USA
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42
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Zippi EL, You AK, Ganguly K, Carmena JM. Selective modulation of cortical population dynamics during neuroprosthetic skill learning. Sci Rep 2022; 12:15948. [PMID: 36153356 PMCID: PMC9509316 DOI: 10.1038/s41598-022-20218-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 09/09/2022] [Indexed: 01/23/2023] Open
Abstract
Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.
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Affiliation(s)
- Ellen L. Zippi
- grid.47840.3f0000 0001 2181 7878Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720 USA
| | - Albert K. You
- grid.47840.3f0000 0001 2181 7878Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720 USA
| | - Karunesh Ganguly
- grid.410372.30000 0004 0419 2775Neurology and Rehabilitation Service, San Francisco VA Medical Center, San Francisco, CA 94121 USA ,grid.266102.10000 0001 2297 6811Department of Neurology, University of California, San Francisco, CA 94143 USA
| | - Jose M. Carmena
- grid.47840.3f0000 0001 2181 7878Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720 USA ,grid.47840.3f0000 0001 2181 7878Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720 USA
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43
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Loriette C, Amengual JL, Ben Hamed S. Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior. Front Neurosci 2022; 16:811736. [PMID: 36161174 PMCID: PMC9492914 DOI: 10.3389/fnins.2022.811736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.
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Affiliation(s)
- Célia Loriette
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon 1, Bron, France
| | | | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon 1, Bron, France
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44
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Small, correlated changes in synaptic connectivity may facilitate rapid motor learning. Nat Commun 2022; 13:5163. [PMID: 36056006 PMCID: PMC9440011 DOI: 10.1038/s41467-022-32646-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 08/08/2022] [Indexed: 11/08/2022] Open
Abstract
Animals rapidly adapt their movements to external perturbations, a process paralleled by changes in neural activity in the motor cortex. Experimental studies suggest that these changes originate from altered inputs (Hinput) rather than from changes in local connectivity (Hlocal), as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent neural network to qualitatively test this interpretation. As expected, Hinput resulted in small activity changes and largely preserved covariance. Surprisingly given the presumed dependence of stable covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in activity and covariance, still within the range of experimental recordings. This similarity is due to Hlocal only requiring small, correlated connectivity changes for successful adaptation. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.
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45
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Gallego-Carracedo C, Perich MG, Chowdhury RH, Miller LE, Gallego JÁ. Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner. eLife 2022; 11:73155. [PMID: 35968845 PMCID: PMC9470163 DOI: 10.7554/elife.73155] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, the 'latent dynamics'. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.
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Affiliation(s)
| | - Matthew G Perich
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Raeed H Chowdhury
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Evanston, United States
| | - Juan Álvaro Gallego
- Department of Bioengineering, Imperial College London, London, United Kingdom
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46
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Bowles S, Hickman J, Peng X, Williamson WR, Huang R, Washington K, Donegan D, Welle CG. Vagus nerve stimulation drives selective circuit modulation through cholinergic reinforcement. Neuron 2022; 110:2867-2885.e7. [PMID: 35858623 DOI: 10.1016/j.neuron.2022.06.017] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/22/2022] [Accepted: 06/17/2022] [Indexed: 12/23/2022]
Abstract
Vagus nerve stimulation (VNS) is a neuromodulation therapy for a broad and expanding set of neurologic conditions. However, the mechanism through which VNS influences central nervous system circuitry is not well described, limiting therapeutic optimization. VNS leads to widespread brain activation, but the effects on behavior are remarkably specific, indicating plasticity unique to behaviorally engaged neural circuits. To understand how VNS can lead to specific circuit modulation, we leveraged genetic tools including optogenetics and in vivo calcium imaging in mice learning a skilled reach task. We find that VNS enhances skilled motor learning in healthy animals via a cholinergic reinforcement mechanism, producing a rapid consolidation of an expert reach trajectory. In primary motor cortex (M1), VNS drives precise temporal modulation of neurons that respond to behavioral outcome. This suggests that VNS may accelerate motor refinement in M1 via cholinergic signaling, opening new avenues for optimizing VNS to target specific disease-relevant circuitry.
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Affiliation(s)
- Spencer Bowles
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Jordan Hickman
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Xiaoyu Peng
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - W Ryan Williamson
- IDEA Core, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Rongchen Huang
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Kayden Washington
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Dane Donegan
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Cristin G Welle
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA.
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47
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Wolpaw JR, Kamesar A. Heksor: The CNS substrate of an adaptive behavior. J Physiol 2022; 600:3423-3452. [PMID: 35771667 PMCID: PMC9545119 DOI: 10.1113/jp283291] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 06/20/2022] [Indexed: 11/16/2022] Open
Abstract
Over the past half‐century, the largely hardwired central nervous system (CNS) of 1970 has become the ubiquitously plastic CNS of today, in which change is the rule not the exception. This transformation complicates a central question in neuroscience: how are adaptive behaviours – behaviours that serve the needs of the individual – acquired and maintained through life? It poses a more basic question: how do many adaptive behaviours share the ubiquitously plastic CNS? This question compels neuroscience to adopt a new paradigm. The core of this paradigm is a CNS entity with unique properties, here given the name heksor from the Greek hexis. A heksor is a distributed network of neurons and synapses that changes itself as needed to maintain the key features of an adaptive behaviour, the features that make the behaviour satisfactory. Through their concurrent changes, the numerous heksors that share the CNS negotiate the properties of the neurons and synapses that they all use. Heksors keep the CNS in a state of negotiated equilibrium that enables each heksor to maintain the key features of its behaviour. The new paradigm based on heksors and the negotiated equilibrium they create is supported by animal and human studies of interactions among new and old adaptive behaviours, explains otherwise inexplicable results, and underlies promising new approaches to restoring behaviours impaired by injury or disease. Furthermore, the paradigm offers new and potentially important answers to extant questions, such as the generation and function of spontaneous neuronal activity, the aetiology of muscle synergies, and the control of homeostatic plasticity.
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Affiliation(s)
- Jonathan R Wolpaw
- Director, National Center for Adaptive Neurotechnologies, Professor of Biomedical Sciences, State University of New York at Albany, Albany Stratton VA Medical Center, Albany, NY, 12208
| | - Adam Kamesar
- Professor of Judaeo-Hellenistic Literature, Hebrew Union College, Cincinnati, Ohio, 45220
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48
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Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task. Nat Commun 2022; 13:3638. [PMID: 35752622 PMCID: PMC9233699 DOI: 10.1038/s41467-022-31440-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/16/2022] [Indexed: 02/07/2023] Open
Abstract
Acquisition of new skills has the potential to disturb existing network function. To directly assess whether previously acquired cortical function is altered during learning, mice were trained in an abstract task in which selected activity patterns were rewarded using an optical brain-computer interface device coupled to primary visual cortex (V1) neurons. Excitatory neurons were longitudinally recorded using 2-photon calcium imaging. Despite significant changes in local neural activity during task performance, tuning properties and stimulus encoding assessed outside of the trained context were not perturbed. Similarly, stimulus tuning was stable in neurons that remained responsive following a different, visual discrimination training task. However, visual discrimination training increased the rate of representational drift. Our results indicate that while some forms of perceptual learning may modify the contribution of individual neurons to stimulus encoding, new skill learning is not inherently disruptive to the quality of stimulus representation in adult V1.
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49
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Arlt C, Barroso-Luque R, Kira S, Bruno CA, Xia N, Chettih SN, Soares S, Pettit NL, Harvey CD. Cognitive experience alters cortical involvement in goal-directed navigation. eLife 2022; 11:76051. [PMID: 35735909 PMCID: PMC9259027 DOI: 10.7554/elife.76051] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Neural activity in the mammalian cortex has been studied extensively during decision tasks, and recent work aims to identify under what conditions cortex is actually necessary for these tasks. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same navigation decision task, revealing past learning as a critical determinant of whether cortex is necessary for goal-directed navigation. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multiarea calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron–neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in goal-directed navigation.
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Affiliation(s)
- Charlotte Arlt
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | | | - Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Carissa A Bruno
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Ningjing Xia
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Selmaan N Chettih
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Sofia Soares
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Noah L Pettit
- Department of Neurobiology, Harvard Medical School, Boston, United States
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50
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Johnston R, Snyder AC, Schibler RS, Smith MA. EEG Signals Index a Global Signature of Arousal Embedded in Neuronal Population Recordings. eNeuro 2022; 9:ENEURO.0012-22.2022. [PMID: 35606150 PMCID: PMC9186107 DOI: 10.1523/eneuro.0012-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 01/01/2023] Open
Abstract
Electroencephalography (EEG) has long been used to index brain states, from early studies describing activity in the presence and absence of visual stimulation to modern work employing complex perceptual tasks. These studies have shed light on brain-wide signals but often lack explanatory power at the single neuron level. Similarly, single neuron recordings can suffer from an inability to measure brain-wide signals accessible using EEG. Here, we combined these techniques while monkeys performed a change detection task and discovered a novel link between spontaneous EEG activity and a neural signal embedded in the spiking responses of neuronal populations. This "slow drift" was associated with fluctuations in the subjects' arousal levels over time: decreases in prestimulus α power were accompanied by increases in pupil size and decreases in microsaccade rate. These results show that brain-wide EEG signals can be used to index modes of activity present in single neuron recordings, that in turn reflect global changes in brain state that influence perception and behavior.
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Affiliation(s)
- Richard Johnston
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Adam C Snyder
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, PA 14627
- Department of Neuroscience, University of Rochester, Rochester, NY 14642
- Center for Visual Science, University of Rochester, Rochester, NY 14627
| | | | - Matthew A Smith
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
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