1
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Reis FMCV, Maesta-Pereira S, Ollivier M, Schuette PJ, Sethi E, Miranda BA, Iniguez E, Chakerian M, Vaughn E, Sehgal M, Nguyen DCT, Yuan FTH, Torossian A, Ikebara JM, Kihara AH, Silva AJ, Kao JC, Khakh BS, Adhikari A. Control of feeding by a bottom-up midbrain-subthalamic pathway. Nat Commun 2024; 15:2111. [PMID: 38454000 PMCID: PMC10920831 DOI: 10.1038/s41467-024-46430-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
Investigative exploration and foraging leading to food consumption have vital importance, but are not well-understood. Since GABAergic inputs to the lateral and ventrolateral periaqueductal gray (l/vlPAG) control such behaviors, we dissected the role of vgat-expressing GABAergic l/vlPAG cells in exploration, foraging and hunting. Here, we show that in mice vgat l/vlPAG cells encode approach to food and consumption of both live prey and non-prey foods. The activity of these cells is necessary and sufficient for inducing food-seeking leading to subsequent consumption. Activation of vgat l/vlPAG cells produces exploratory foraging and compulsive eating without altering defensive behaviors. Moreover, l/vlPAG vgat cells are bidirectionally interconnected to several feeding, exploration and investigation nodes, including the zona incerta. Remarkably, the vgat l/vlPAG projection to the zona incerta bidirectionally controls approach towards food leading to consumption. These data indicate the PAG is not only a final downstream target of top-down exploration and foraging-related inputs, but that it also influences these behaviors through a bottom-up pathway.
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Affiliation(s)
- Fernando M C V Reis
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
| | - Sandra Maesta-Pereira
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Matthias Ollivier
- Department of Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Peter J Schuette
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Ekayana Sethi
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Blake A Miranda
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Emily Iniguez
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Meghmik Chakerian
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Eric Vaughn
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Megha Sehgal
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, USA
| | - Darren C T Nguyen
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Faith T H Yuan
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Anita Torossian
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Juliane M Ikebara
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, 09606-070, Brazil
| | - Alexandre H Kihara
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, 09606-070, Brazil
| | - Alcino J Silva
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, USA
- Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, USA
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Baljit S Khakh
- Department of Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Avishek Adhikari
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
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2
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Kleinman M, Wang T, Xiao D, Feghhi E, Lee K, Carr N, Li Y, Hadidi N, Chandrasekaran C, Kao JC. A cortical information bottleneck during decision-making. bioRxiv 2023:2023.07.12.548742. [PMID: 37502862 PMCID: PMC10369960 DOI: 10.1101/2023.07.12.548742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) to form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism for how cortex may form these optimal representations, we trained a multi-area recurrent neural network (RNN) to perform the task. Remarkably, DLPFC and PMd resembling representations emerged in the early and late areas of the multi-area RNN, respectively. The DLPFC-resembling area partially orthogonalized choice information and task inputs and this choice information was preferentially propagated to downstream areas through selective alignment with inter-area connections, while remaining task information was not. Our results suggest that cortex uses multi-area computation to form minimal sufficient representations by preferential propagation of relevant information between areas.
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Affiliation(s)
- Michael Kleinman
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Tian Wang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Derek Xiao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Ebrahim Feghhi
- Neurosciences Program, University of California, Los Angeles, CA, USA
| | - Kenji Lee
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Nicole Carr
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yuke Li
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Nima Hadidi
- Neurosciences Program, University of California, Los Angeles, CA, USA
| | - Chandramouli Chandrasekaran
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Jonathan C. Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
- Neurosciences Program, University of California, Los Angeles, CA, USA
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3
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Gill JL, Schneiders JA, Stangl M, Aghajan ZM, Vallejo M, Hiller S, Topalovic U, Inman CS, Villaroman D, Bari A, Adhikari A, Rao VR, Fanselow MS, Craske MG, Krahl SE, Chen JWY, Vick M, Hasulak NR, Kao JC, Koek RJ, Suthana N, Langevin JP. A pilot study of closed-loop neuromodulation for treatment-resistant post-traumatic stress disorder. Nat Commun 2023; 14:2997. [PMID: 37225710 PMCID: PMC10209131 DOI: 10.1038/s41467-023-38712-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/12/2023] [Indexed: 05/26/2023] Open
Abstract
The neurophysiological mechanisms in the human amygdala that underlie post-traumatic stress disorder (PTSD) remain poorly understood. In a first-of-its-kind pilot study, we recorded intracranial electroencephalographic data longitudinally (over one year) in two male individuals with amygdala electrodes implanted for the management of treatment-resistant PTSD (TR-PTSD) under clinical trial NCT04152993. To determine electrophysiological signatures related to emotionally aversive and clinically relevant states (trial primary endpoint), we characterized neural activity during unpleasant portions of three separate paradigms (negative emotional image viewing, listening to recordings of participant-specific trauma-related memories, and at-home-periods of symptom exacerbation). We found selective increases in amygdala theta (5-9 Hz) bandpower across all three negative experiences. Subsequent use of elevations in low-frequency amygdala bandpower as a trigger for closed-loop neuromodulation led to significant reductions in TR-PTSD symptoms (trial secondary endpoint) following one year of treatment as well as reductions in aversive-related amygdala theta activity. Altogether, our findings provide early evidence that elevated amygdala theta activity across a range of negative-related behavioral states may be a promising target for future closed-loop neuromodulation therapies in PTSD.
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Affiliation(s)
- Jay L Gill
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Medical Scientist Training Program, University of California, Los Angeles, CA, USA
| | - Julia A Schneiders
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Research and Development Service; Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Matthias Stangl
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Zahra M Aghajan
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Neurosurgery, University of California, Los Angeles, CA, USA
| | - Mauricio Vallejo
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Sonja Hiller
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Uros Topalovic
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Cory S Inman
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Diane Villaroman
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Ausaf Bari
- Department of Neurosurgery, University of California, Los Angeles, CA, USA
| | - Avishek Adhikari
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Vikram R Rao
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Michael S Fanselow
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Michelle G Craske
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Scott E Krahl
- Research and Development Service; Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Department of Neurosurgery, University of California, Los Angeles, CA, USA
| | - James W Y Chen
- Neurology Service; Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Department of Neurology, University of California, Los Angeles, CA, USA
| | | | - Nicholas R Hasulak
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Phoenix Research Consulting LLC, Gilbert, AZ, USA
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Ralph J Koek
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Psychiatry and Mental Health Service; Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- Department of Neurosurgery, University of California, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, CA, USA.
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
| | - Jean-Philippe Langevin
- Department of Neurosurgery, University of California, Los Angeles, CA, USA.
- Neurosurgery Service; Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
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4
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Wang W, Schuette PJ, La-Vu MQ, Torossian A, Tobias BC, Ceko M, Kragel PA, Reis FMCV, Ji S, Sehgal M, Maesta-Pereira S, Chakerian M, Silva AJ, Canteras NS, Wager T, Kao JC, Adhikari A. Dorsal premammillary projection to periaqueductal gray controls escape vigor from innate and conditioned threats. eLife 2021; 10:e69178. [PMID: 34468312 PMCID: PMC8457830 DOI: 10.7554/elife.69178] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 08/28/2021] [Indexed: 02/04/2023] Open
Abstract
Escape from threats has paramount importance for survival. However, it is unknown if a single circuit controls escape vigor from innate and conditioned threats. Cholecystokinin (cck)-expressing cells in the hypothalamic dorsal premammillary nucleus (PMd) are necessary for initiating escape from innate threats via a projection to the dorsolateral periaqueductal gray (dlPAG). We now show that in mice PMd-cck cells are activated during escape, but not other defensive behaviors. PMd-cck ensemble activity can also predict future escape. Furthermore, PMd inhibition decreases escape speed from both innate and conditioned threats. Inhibition of the PMd-cck projection to the dlPAG also decreased escape speed. Intriguingly, PMd-cck and dlPAG activity in mice showed higher mutual information during exposure to innate and conditioned threats. In parallel, human functional magnetic resonance imaging data show that a posterior hypothalamic-to-dlPAG pathway increased activity during exposure to aversive images, indicating that a similar pathway may possibly have a related role in humans. Our data identify the PMd-dlPAG circuit as a central node, controlling escape vigor elicited by both innate and conditioned threats.
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Affiliation(s)
- Weisheng Wang
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Peter J Schuette
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Mimi Q La-Vu
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Anita Torossian
- University of California, Los AngelesLos AngelesUnited States
| | - Brooke C Tobias
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Marta Ceko
- Institute of Cognitive Science, University of ColoradoBoulderUnited States
| | | | - Fernando MCV Reis
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Shiyu Ji
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Megha Sehgal
- Department of Neurobiology, University of California, Los AngelesLos AngelesUnited States
| | | | - Meghmik Chakerian
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Alcino J Silva
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
- Department of Neurobiology, University of California, Los AngelesLos AngelesUnited States
- Department of Psychiatry & Biobehavioral Sciences, University of California, Los AngelesLos AngelesUnited States
| | | | - Tor Wager
- University of ColoradoBoulderUnited States
| | - Jonathan C Kao
- Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
| | - Avishek Adhikari
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
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5
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Reis FMCV, Liu J, Schuette PJ, Lee JY, Maesta-Pereira S, Chakerian M, Wang W, Canteras NS, Kao JC, Adhikari A. Shared Dorsal Periaqueductal Gray Activation Patterns during Exposure to Innate and Conditioned Threats. J Neurosci 2021; 41:5399-5420. [PMID: 33883203 PMCID: PMC8221602 DOI: 10.1523/jneurosci.2450-20.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 04/08/2021] [Accepted: 04/12/2021] [Indexed: 02/02/2023] Open
Abstract
The brainstem dorsal periaqueductal gray (dPAG) has been widely recognized as being a vital node orchestrating the responses to innate threats. Intriguingly, recent evidence also shows that the dPAG mediates defensive responses to fear conditioned contexts. However, it is unknown whether the dPAG displays independent or shared patterns of activation during exposure to innate and conditioned threats. It is also unclear how dPAG ensembles encode and predict diverse defensive behaviors. To address this question, we used miniaturized microscopes to obtain recordings of the same dPAG ensembles during exposure to a live predator and a fear conditioned context in male mice. dPAG ensembles encoded not only distance to threat, but also relevant features, such as predator speed and angular offset between mouse and threat. Furthermore, dPAG cells accurately encoded numerous defensive behaviors, including freezing, stretch-attend postures, and escape. Encoding of behaviors and of distance to threat occurred independently in dPAG cells. dPAG cells also displayed a shared representation to encode these behaviors and distance to threat across innate and conditioned threats. Last, we also show that escape could be predicted by dPAG activity several seconds in advance. Thus, dPAG activity dynamically tracks key kinematic and behavioral variables during exposure to threats, and exhibits similar patterns of activation during defensive behaviors elicited by innate or conditioned threats. These data indicate that a common pathway may be recruited by the dPAG during exposure to a wide variety of threat modalities.SIGNIFICANCE STATEMENT The dorsal periaqueductal gray (dPAG) is critical to generate defensive behaviors during encounters with threats of multiple modalities. Here we use longitudinal calcium transient recordings of dPAG ensembles in freely moving mice to show that this region uses shared patterns of activity to represent distance to an innate threat (a live predator) and a conditioned threat (a shock grid). We also show that dPAG neural activity can predict diverse defensive behaviors. These data indicate the dPAG uses conserved population-level activity patterns to encode and coordinate defensive behaviors during exposure to both innate and conditioned threats.
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Affiliation(s)
- Fernando M C V Reis
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
| | - Jinhan Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, California 90095
| | - Peter J Schuette
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
| | - Johannes Y Lee
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, California 90095
| | - Sandra Maesta-Pereira
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
| | - Meghmik Chakerian
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
| | - Weisheng Wang
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
| | - Newton S Canteras
- Department of Anatomy, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP 05508-000, Brazil
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, California 90095
| | - Avishek Adhikari
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
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6
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Olsen S, Zhang J, Liang KF, Lam M, Riaz U, Kao JC. An artificial intelligence that increases simulated brain-computer interface performance. J Neural Eng 2021; 18. [PMID: 33978599 DOI: 10.1088/1741-2552/abfaaa] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/22/2021] [Indexed: 12/14/2022]
Abstract
Objective.Brain-computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard.Approach.Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI's trajectories.Main results.We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to 'dial in' on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control.Significance.This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.
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Affiliation(s)
- Sebastian Olsen
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Jianwei Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Ken-Fu Liang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Michelle Lam
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Usama Riaz
- Department of Computer Science, University of California, Los Angeles, CA 90024, United States of America
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America.,Neurosciences Program, University of California, Los Angeles, CA 90024, United States of America
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7
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Reis FM, Lee JY, Maesta-Pereira S, Schuette PJ, Chakerian M, Liu J, La-Vu MQ, Tobias BC, Ikebara JM, Kihara AH, Canteras NS, Kao JC, Adhikari A. Dorsal periaqueductal gray ensembles represent approach and avoidance states. eLife 2021; 10:64934. [PMID: 33955356 PMCID: PMC8133778 DOI: 10.7554/elife.64934] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 05/05/2021] [Indexed: 12/20/2022] Open
Abstract
Animals must balance needs to approach threats for risk assessment and to avoid danger. The dorsal periaqueductal gray (dPAG) controls defensive behaviors, but it is unknown how it represents states associated with threat approach and avoidance. We identified a dPAG threatavoidance ensemble in mice that showed higher activity farther from threats such as the open arms of the elevated plus maze and a predator. These cells were also more active during threat avoidance behaviors such as escape and freezing, even though these behaviors have antagonistic motor output. Conversely, the threat approach ensemble was more active during risk assessment behaviors and near threats. Furthermore, unsupervised methods showed that avoidance/approach states were encoded with shared activity patterns across threats. Lastly, the relative number of cells in each ensemble predicted threat avoidance across mice. Thus, dPAG ensembles dynamically encode threat approach and avoidance states, providing a flexible mechanism to balance risk assessment and danger avoidance.
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Affiliation(s)
- Fernando McV Reis
- Department of Psychology, University of California, Los Angeles, Los Angeles, United States
| | - Johannes Y Lee
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States
| | - Sandra Maesta-Pereira
- Department of Psychology, University of California, Los Angeles, Los Angeles, United States
| | - Peter J Schuette
- Department of Psychology, University of California, Los Angeles, Los Angeles, United States
| | - Meghmik Chakerian
- Department of Psychology, University of California, Los Angeles, Los Angeles, United States
| | - Jinhan Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States
| | - Mimi Q La-Vu
- Department of Psychology, University of California, Los Angeles, Los Angeles, United States
| | - Brooke C Tobias
- Department of Psychology, University of California, Los Angeles, Los Angeles, United States
| | - Juliane M Ikebara
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, São Paulo, Brazil
| | - Alexandre Hiroaki Kihara
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, São Paulo, Brazil
| | - Newton S Canteras
- Department of Anatomy, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States
| | - Avishek Adhikari
- Department of Psychology, University of California, Los Angeles, Los Angeles, United States
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8
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Peixoto D, Verhein JR, Kiani R, Kao JC, Nuyujukian P, Chandrasekaran C, Brown J, Fong S, Ryu SI, Shenoy KV, Newsome WT. Decoding and perturbing decision states in real time. Nature 2021; 591:604-609. [PMID: 33473215 DOI: 10.1038/s41586-020-03181-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 12/09/2020] [Indexed: 01/01/2023]
Abstract
In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment1. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject's upcoming decision2. Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind3. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.
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Affiliation(s)
- Diogo Peixoto
- Neurobiology Department, Stanford University, Stanford, CA, USA. .,Champalimaud Neuroscience Programme, Lisbon, Portugal. .,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Jessica R Verhein
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. .,Neurosciences Graduate Program, Stanford University, Stanford, CA, USA. .,Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA.
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY, USA
| | - Jonathan C Kao
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.,Electrical Engineering Department, Stanford University, Stanford, CA, USA.,Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA.,Neurosciences Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Paul Nuyujukian
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.,Electrical Engineering Department, Stanford University, Stanford, CA, USA.,Bioengineering Department, Stanford University, Stanford, CA, USA.,Neurosurgery Department, Stanford University, Stanford, CA, USA.,Bio-X Institute, Stanford University, Stanford, CA, USA
| | - Chandramouli Chandrasekaran
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.,Electrical Engineering Department, Stanford University, Stanford, CA, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.,Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA.,Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Julian Brown
- Neurobiology Department, Stanford University, Stanford, CA, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Sania Fong
- Neurobiology Department, Stanford University, Stanford, CA, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA, USA.,Neurosurgery Department, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Krishna V Shenoy
- Neurobiology Department, Stanford University, Stanford, CA, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.,Electrical Engineering Department, Stanford University, Stanford, CA, USA.,Bioengineering Department, Stanford University, Stanford, CA, USA.,Bio-X Institute, Stanford University, Stanford, CA, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - William T Newsome
- Neurobiology Department, Stanford University, Stanford, CA, USA. .,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. .,Bio-X Institute, Stanford University, Stanford, CA, USA.
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9
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Jiang X, Saggar H, Ryu SI, Shenoy KV, Kao JC. Structure in Neural Activity during Observed and Executed Movements Is Shared at the Neural Population Level, Not in Single Neurons. Cell Rep 2020; 32:108148. [PMID: 32905762 DOI: 10.1016/j.celrep.2020.108148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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10
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11
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Jiang X, Saggar H, Ryu SI, Shenoy KV, Kao JC. Structure in Neural Activity during Observed and Executed Movements Is Shared at the Neural Population Level, Not in Single Neurons. Cell Rep 2020; 32:108006. [DOI: 10.1016/j.celrep.2020.108006] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 05/24/2020] [Accepted: 07/16/2020] [Indexed: 12/30/2022] Open
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12
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Nason SR, Vaskov AK, Willsey MS, Welle EJ, An H, Vu PP, Bullard AJ, Nu CS, Kao JC, Shenoy KV, Jang T, Kim HS, Blaauw D, Patil PG, Chestek CA. A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces. Nat Biomed Eng 2020; 4:973-983. [PMID: 32719512 DOI: 10.1038/s41551-020-0591-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 06/24/2020] [Indexed: 12/18/2022]
Abstract
The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.
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Affiliation(s)
- Samuel R Nason
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Alex K Vaskov
- Robotics Graduate Program, University of Michigan, Ann Arbor, MI, USA
| | - Matthew S Willsey
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.,Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Elissa J Welle
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Hyochan An
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Philip P Vu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Autumn J Bullard
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Chrono S Nu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA.,Neurosciences Program, University of California, Los Angeles, Los Angeles, CA, 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.,The Bio-X Program, Stanford University, Stanford, CA, USA.,Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Taekwang Jang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.,Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland
| | - Hun-Seok Kim
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - David Blaauw
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.,Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Neurology, University of Michigan Medical School, Ann Arbor, MI, USA.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. .,Robotics Graduate Program, University of Michigan, Ann Arbor, MI, USA. .,Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. .,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.
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13
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Abstract
Recurrent neural networks (RNNs) are increasingly being used to model complex cognitive and motor tasks performed by behaving animals. RNNs are trained to reproduce animal behavior while also capturing key statistics of empirically recorded neural activity. In this manner, the RNN can be viewed as an in silico circuit whose computational elements share similar motifs with the cortical area it is modeling. Furthermore, because the RNN's governing equations and parameters are fully known, they can be analyzed to propose hypotheses for how neural populations compute. In this context, we present important considerations when using RNNs to model motor behavior in a delayed reach task. First, by varying the network's nonlinear activation and rate regularization, we show that RNNs reproducing single-neuron firing rate motifs may not adequately capture important population motifs. Second, we find that even when RNNs reproduce key neurophysiological features on both the single neuron and population levels, they can do so through distinctly different dynamical mechanisms. To distinguish between these mechanisms, we show that an RNN consistent with a previously proposed dynamical mechanism is more robust to input noise. Finally, we show that these dynamics are sufficient for the RNN to generalize to tasks it was not trained on. Together, these results emphasize important considerations when using RNN models to probe neural dynamics.NEW & NOTEWORTHY Artificial neurons in a recurrent neural network (RNN) may resemble empirical single-unit activity but not adequately capture important features on the neural population level. Dynamics of RNNs can be visualized in low-dimensional projections to provide insight into the RNN's dynamical mechanism. RNNs trained in different ways may reproduce neurophysiological motifs but do so with distinctly different mechanisms. RNNs trained to only perform a delayed reach task can generalize to perform tasks where the target is switched or the target location is changed.
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Affiliation(s)
- Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California.,Neurosciences Program, University of California, Los Angeles, California
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14
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Jiang X, Ryu SI, Shenoy KV, Kao JC. Single Neuron Firing Rate Statistics in Motor Cortex During Execution and Observation of Movement. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018:981-986. [PMID: 30440555 DOI: 10.1109/embc.2018.8512445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mirror neurons, which fire during both the execution and observation of movement, are believed to play an important role in motor processing and learning. However, much work still remains to understand the similarities and differences in how these neurons compute in the motor cortex during movement execution and observation. Here, we performed experiments where a monkey both executes and observes a center-out-and-back task within the same experimental session. By recording from putatively the same neural population, we were able to analyze and compare single neuron statistics between movement execution and observation. We found that a majority of neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) have statistically different firing rate statistics between movement execution and observation. As a result of this difference, we then wondered if neurons during movement observation exhibited a similar characteristic to those during movement execution: changing of preferred directions as a function of movement speed. Interestingly, we found that while observed movement speed is encoded in the neural population, it only alters a small proportion of the neuron's firing rate statistics. These results suggest that neural populations in Ml and PMd process information related to movement differently between execution and observation.
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15
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Stavisky SD, Kao JC, Nuyujukian P, Pandarinath C, Blabe C, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV. Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements. Sci Rep 2018; 8:16357. [PMID: 30397281 PMCID: PMC6218537 DOI: 10.1038/s41598-018-34711-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 10/24/2018] [Indexed: 12/26/2022] Open
Abstract
Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector's position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.
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Affiliation(s)
- Sergey D Stavisky
- Neurosurgery Department, Stanford University, Stanford, CA, USA.
- Electrical Engineering Department, Stanford University, Stanford, CA, USA.
| | - Jonathan C Kao
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Electrical and Computer Engineering Department, University of California at Los Angeles, Los Angeles, CA, USA
| | - Paul Nuyujukian
- Neurosurgery Department, Stanford University, Stanford, CA, USA
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Bioengineering Department, Stanford University, Stanford, CA, USA
- Stanford Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Program, Stanford University, Stanford, CA, USA
| | - Chethan Pandarinath
- Neurosurgery Department, Stanford University, Stanford, CA, USA
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
| | - Christine Blabe
- Neurosurgery Department, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Neurosurgery Department, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Leigh R Hochberg
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science Brown University, Providence, RI, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jaimie M Henderson
- Neurosurgery Department, Stanford University, Stanford, CA, USA
- Stanford Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Program, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Bioengineering Department, Stanford University, Stanford, CA, USA
- Stanford Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Program, Stanford University, Stanford, CA, USA
- Neurobiology Department, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
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16
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Pandarinath C, O'Shea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann EM, Kaufman MT, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV, Abbott LF, Sussillo D. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat Methods 2018; 15:805-815. [PMID: 30224673 PMCID: PMC6380887 DOI: 10.1038/s41592-018-0109-9] [Citation(s) in RCA: 242] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 06/28/2018] [Indexed: 01/28/2023]
Abstract
Neuroscience is experiencing a revolution in which simultaneous recording
of many thousands of neurons is revealing population dynamics that are not
apparent from single-neuron responses. This structure is typically extracted
from trial-averaged data, but deeper understanding requires studying
single-trial phenomena, which is challenging due to incomplete sampling of the
neural population, trial-to-trial variability, and fluctuations in action
potential timing. We introduce Latent Factor Analysis via Dynamical Systems
(LFADS), a deep learning method to infer latent dynamics from single-trial
neural spiking data. LFADS uses a nonlinear dynamical system to infer the
dynamics underlying observed spiking activity and to extract
‘de-noised’ single-trial firing rates. When applied to a variety
of monkey and human motor cortical datasets, LFADS predicts observed behavioral
variables with unprecedented accuracy, extracts precise estimates of neural
dynamics on single trials, infers perturbations to those dynamics that correlate
with behavioral choices, and combines data from non-overlapping recording
sessions spanning months to improve inference of underlying dynamics.
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Affiliation(s)
- Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA. .,Department of Neurosurgery, Emory University, Atlanta, GA, USA. .,Department of Neurosurgery, Stanford University, Stanford, CA, USA. .,Department of Electrical Engineering, Stanford University, Stanford, CA, USA. .,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Jasmine Collins
- Google AI, Google Inc., Mountain View, CA, USA.,University of California, Berkeley, Berkeley, CA, USA
| | - Rafal Jozefowicz
- Google AI, Google Inc., Mountain View, CA, USA.,OpenAI, San Francisco, CA, USA
| | - Sergey D Stavisky
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA.,Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Jonathan C Kao
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eric M Trautmann
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Matthew T Kaufman
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Leigh R Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Veterans Affairs Medical Center, Providence, RI, USA.,Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA.,Department of Neurobiology, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Bio-X Program, Stanford University, Stanford, CA, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - L F Abbott
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.,Department of Neuroscience, Columbia University, New York, NY, USA.,Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA. .,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA. .,Google AI, Google Inc., Mountain View, CA, USA.
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17
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Haddox CL, Shenoy N, Shah KK, Kao JC, Jain S, Halfdanarson TR, Wijdicks EF, Goetz MP. Pembrolizumab induced bulbar myopathy and respiratory failure with necrotizing myositis of the diaphragm. Ann Oncol 2018; 28:673-675. [PMID: 27993808 PMCID: PMC5391710 DOI: 10.1093/annonc/mdw655] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- C L Haddox
- Departments of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - N Shenoy
- Departments of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - K K Shah
- Anatomic/Clinical Pathology, Mayo Clinic, Rochester, MN, USA
| | - J C Kao
- Neurology , Mayo Clinic, Rochester, MN, USA
| | - S Jain
- Departments of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - T R Halfdanarson
- Departments of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - E F Wijdicks
- Anatomic/Clinical Pathology, Mayo Clinic, Rochester, MN, USA
| | - M P Goetz
- Departments of Medical Oncology, Mayo Clinic, Rochester, MN, USA
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18
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Kao JC, Ryu SI, Shenoy KV. Leveraging historical knowledge of neural dynamics to rescue decoder performance as neural channels are lost: "Decoder hysteresis". Annu Int Conf IEEE Eng Med Biol Soc 2018; 2015:1061-6. [PMID: 26736448 DOI: 10.1109/embc.2015.7318548] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
An intracortical brain-machine interface (BMI) decodes spiking activity recorded from motor cortical neurons to drive a prosthetic device (e.g., a computer cursor or robotic arm). As the number of recorded neurons decreases over time due to decay in recording quality, the performance of a BMI decreases. We asked: can degrading BMI performance be rescued by using prior information from when more neurons were observed? This would entail augmenting a decoder by using previously learned knowledge about motor cortex (at an earlier point in the array lifetime). We implemented this idea by modeling low-dimensional dynamics of the neural population, which describe how the population evolves through time. We posit that if the neural dynamics accurately reflect properties of motor cortex, then having a better estimate of these dynamics should result in a better decoder. Using previously collected (offline) experimental data, we found that a decoder using dynamics inferred in the past (when more neural channels were available) outperformed the same decoder using dynamics inferred from the (fewer) remaining neural channels. These results suggest that neural dynamics capture important features of the neural population responses in motor cortex, and that knowledge of these dynamics may rescue BMI performance even as array signal quality degrades.
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Abstract
OBJECTIVE Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. APPROACH We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. MAIN RESULTS We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error 'detect-and-act' system that attempts to automatically 'undo' or 'prevent' mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). SIGNIFICANCE Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.
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Affiliation(s)
- Nir Even-Chen
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
| | - Sergey D. Stavisky
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Neurosurgery, Stanford University, Stanford, CA 94305 USA
| | - Jonathan C. Kao
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA 90095 USA
| | - Stephen I. Ryu
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Neurosurgery at Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Krishna V. Shenoy
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA 94305
- The Bio-X Program, Stanford University, Stanford, CA 94305
- The Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305
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20
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Kao JC, Ryu SI, Shenoy KV. Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces. Sci Rep 2017; 7:7395. [PMID: 28784984 PMCID: PMC5547077 DOI: 10.1038/s41598-017-06029-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/07/2017] [Indexed: 01/09/2023] Open
Abstract
Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.
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Affiliation(s)
- Jonathan C Kao
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, 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
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA. .,Department of Neurobiology, Stanford University, Stanford, CA, 94305, USA. .,Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA. .,Neurosciences Program, Stanford University, Stanford, CA, 94305, USA. .,Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA.
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21
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Stavisky SD, Kao JC, Ryu SI, Shenoy KV. Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions. Neuron 2017. [PMID: 28625485 DOI: 10.1016/j.neuron.2017.05.023] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions.
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Affiliation(s)
- Sergey D Stavisky
- Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA.
| | - Jonathan C Kao
- Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Krishna V Shenoy
- Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Neurobiology and Bioengineering Departments, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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22
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Sussillo D, Stavisky SD, Kao JC, Ryu SI, Shenoy KV. Corrigendum: Making brain-machine interfaces robust to future neural variability. Nat Commun 2017; 8:14490. [PMID: 28106034 PMCID: PMC5263867 DOI: 10.1038/ncomms14490] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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O'Shea DJ, Trautmann E, Chandrasekaran C, Stavisky S, Kao JC, Sahani M, Ryu S, Deisseroth K, Shenoy KV. The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces. Exp Neurol 2017; 287:437-451. [PMID: 27511294 PMCID: PMC5154795 DOI: 10.1016/j.expneurol.2016.08.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 06/19/2016] [Accepted: 08/04/2016] [Indexed: 01/08/2023]
Abstract
A central goal of neuroscience is to understand how populations of neurons coordinate and cooperate in order to give rise to perception, cognition, and action. Nonhuman primates (NHPs) are an attractive model with which to understand these mechanisms in humans, primarily due to the strong homology of their brains and the cognitively sophisticated behaviors they can be trained to perform. Using electrode recordings, the activity of one to a few hundred individual neurons may be measured electrically, which has enabled many scientific findings and the development of brain-machine interfaces. Despite these successes, electrophysiology samples sparsely from neural populations and provides little information about the genetic identity and spatial micro-organization of recorded neurons. These limitations have spurred the development of all-optical methods for neural circuit interrogation. Fluorescent calcium signals serve as a reporter of neuronal responses, and when combined with post-mortem optical clearing techniques such as CLARITY, provide dense recordings of neuronal populations, spatially organized and annotated with genetic and anatomical information. Here, we advocate that this methodology, which has been of tremendous utility in smaller animal models, can and should be developed for use with NHPs. We review here several of the key opportunities and challenges for calcium-based optical imaging in NHPs. We focus on motor neuroscience and brain-machine interface design as representative domains of opportunity within the larger field of NHP neuroscience.
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Affiliation(s)
- Daniel J O'Shea
- Neurosciences Program, Stanford University, Stanford, CA 94305, United States
| | - Eric Trautmann
- Neurosciences Program, Stanford University, Stanford, CA 94305, United States
| | | | - Sergey Stavisky
- Neurosciences Program, Stanford University, Stanford, CA 94305, United States
| | - Jonathan C Kao
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States
| | - Maneesh Sahani
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States; Gatsby Computational Neuroscience Unit, University College London, London W1T 4JG, United Kingdom
| | - Stephen Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, United States
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA 94305, United States; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, United States
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, United States; Deparment of Neurobiology, Stanford University, Stanford, CA 94305, United States.
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Abstract
Brain-computer interfaces (BCIs) record brain activity and translate the information into useful control signals. They can be used to restore function to people with paralysis by controlling end effectors such as computer cursors and robotic limbs. Communication neural prostheses are BCIs that control user interfaces on computers or mobile devices. Here we demonstrate a communication prosthesis by simulating a typing task with two rhesus macaques implanted with electrode arrays. The monkeys used two of the highest known performing BCI decoders to type out words and sentences when prompted one symbol/letter at a time. On average, Monkeys J and L achieved typing rates of 10.0 and 7.2 words per minute (wpm), respectively, copying text from a newspaper article using a velocity-only two dimensional BCI decoder with dwell-based symbol selection. With a BCI decoder that also featured a discrete click for key selection, typing rates increased to 12.0 and 7.8 wpm. These represent the highest known achieved communication rates using a BCI. We then quantified the relationship between bitrate and typing rate and found it approximately linear: typing rate in wpm is nearly three times bitrate in bits per second. We also compared the metrics of achieved bitrate and information transfer rate and discuss their applicability to real-world typing scenarios. Although this study cannot model the impact of cognitive load of word and sentence planning, the findings here demonstrate the feasibility of BCIs to serve as communication interfaces and represent an upper bound on the expected achieved typing rate for a given BCI throughput.
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Affiliation(s)
- Paul Nuyujukian
- Neurosurgery Department, the Electrical Engineering Department, the Bioengineering Department, and Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305 USA
| | - Jonathan C Kao
- Electrical Engineering Department, Stanford University, Stanford, CA 94305 USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA 94305 USA and also with Palo Alto Medical Foundation, Palo Alto, CA 94550 USA
| | - Krishna V Shenoy
- Electrical Engineering Department, the Bioengineering Department, the Neurobiology Department, and Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305 USA and also with Howard Hughes Medical Institute, Chevy Chase, MD 20815 USA
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Even-Chen N, Stavisky SD, Kao JC, Ryu SI, Shenoy KV. Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:71-5. [PMID: 26736203 DOI: 10.1109/embc.2015.7318303] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.
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Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models. IEEE Trans Biomed Eng 2016; 64:935-945. [PMID: 27337709 DOI: 10.1109/tbme.2016.2582691] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys ( ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.
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Affiliation(s)
| | - Paul Nuyujukian
- Department of Electrical Engineering, Department of Bioengineering, and School of Medicine and NeurosurgeryStanford University
| | - Stephen I Ryu
- Department of Electrical EngineeringStanford University
| | - Krishna V Shenoy
- Department of Electrical Engineering, Department of Bioengineering, and Department of NeurobiologyStanford University
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Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:3041-4. [PMID: 25570632 DOI: 10.1109/embc.2014.6944264] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The best-performing brain-machine interfaces (BMIs) to date decode movement intention from intracortically recorded spikes, but these signals may be lost over time. A way to increase the useful lifespan of BMIs is to make more comprehensive use of available neural signals. Recent studies have demonstrated that the local field potential (LFP), a potentially more robust signal, can also be used to control a BMI. However, LFP-driven performance has fallen short of the best spikes-driven performance. Here we report a biomimetic BMI driven by low-frequency LFP that enabled a rhesus monkey to acquire and hold randomly placed targets with 99% success rate. Although LFP-driven performance was still worse than when decoding spikes, to the best of our knowledge this represents the highest-performing LFP-based BMI. We also demonstrate a new hybrid BMI that decodes cursor velocity using both spikes and LFP. This hybrid decoder improved performance over spikes-only decoding. Our results suggest that LFP can complement spikes when spikes are available or provide an alternative control signal if spikes are absent.
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Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. J Neural Eng 2015; 12:036009. [PMID: 25946198 DOI: 10.1088/1741-2560/12/3/036009] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. APPROACH Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. MAIN RESULTS LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. SIGNIFICANCE These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.
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Abstract
OBJECTIVE Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. APPROACH We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. MAIN RESULTS We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr(-1). In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. SIGNIFICANCE The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make neural prostheses more practical to use.
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Affiliation(s)
- Paul Nuyujukian
- Department of Bioengineering, Stanford University, Stanford, CA. School of Medicine, Stanford University, Stanford, CA. Department of Neurosurgery, Stanford University, Stanford, CA
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30
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Abstract
OBJECTIVE The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF). APPROACH This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm. MAIN RESULTS Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied. SIGNIFICANCE These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.
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Abstract
Communication neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously moving cursor neural prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject-specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this paper, we present the highest performing free-paced neural prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits/s. These findings demonstrate that meaningful high performance can be achieved using an intracortical neural prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities.
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Affiliation(s)
- Paul Nuyujukian
- Bioengineering Department and School of Medicine and Neurosurgery Department, Stanford University, Stanford, CA, USA
| | - Joline M Fan
- Bioengineering Department and School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan C Kao
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Bioengineering Department and Neurobiology Department and the Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA
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Kao JC, Nuyujukian P, Stavisky S, Ryu SI, Ganguli S, Shenoy KV. Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013:293-298. [PMID: 24109682 DOI: 10.1109/embc.2013.6609495] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The intraday robustness of brain-machine interfaces (BMIs) is important to their clinical viability. In particular, BMIs must be robust to intraday perturbations in neuron firing rates, which may arise from several factors including recording loss and external noise. Using a state-of-the-art decode algorithm, the Recalibrated Feedback Intention Trained Kalman filter (ReFIT-KF) we introduce two novel modifications: (1) a normalization of the firing rates, and (2) a reduction of the dimensionality of the data via principal component analysis (PCA). We demonstrate in online studies that a ReFIT-KF equipped with normalization and PCA (NPC-ReFIT-KF) (1) achieves comparable performance to a standard ReFIT-KF when at least 60% of the neural variance is captured, and (2) is more robust to the undetected loss of channels. We present intuition as to how both modifications may increase the robustness of BMIs, and investigate the contribution of each modification to robustness. These advances, which lead to a decoder achieving state-of-the-art performance with improved robustness, are important for the clinical viability of BMI systems.
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Sussillo D, Nuyujukian P, Fan JM, Kao JC, Stavisky SD, Ryu S, Shenoy K. A recurrent neural network for closed-loop intracortical brain-machine interface decoders. J Neural Eng 2012; 9:026027. [PMID: 22427488 DOI: 10.1088/1741-2560/9/2/026027] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.
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Affiliation(s)
- David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305-9505, USA.
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Wu CH, Kao JC, Chang CJ. Analysis of outpatient referral failures. J Fam Pract 1996; 42:498-502. [PMID: 8642368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
BACKGROUND The purpose of this study was to measure the rate at which outpatient referrals failed to be completed, and to analyze predisposing factors for referral failure in the family practice of a medical center. METHODS Structured questionnaires were completed by referring physicians whenever a referral was initiated during a 4-month period. On the 60th day after referral, an investigator contacted the referred patients by telephone and also reviewed their charts. RESULTS During the 4-month period, 604 referrals (2.28%) were made from 26,476 encounters at the study clinic. Sixty-four patients (10.6%) failed to complete the referral processes within a 60-day period. The most frequent reasons for referral failure were administrative factors, ie, too long a wait (59.4%), and the patient's belief that the referral was not necessary (23.4%). The physician's or patients's opinion of referral necessity, the level of experience of the referring physician, and the method of contact with the consultant all had significant influence on the referral failure rate. CONCLUSIONS Improving administrative efficiency, enhancing communication between physicians and between physicians and patients, assessing the willingness of patients to follow through on a referral, and the method used to initiate the referral by the physician may reduce the referral failure rate.
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Affiliation(s)
- C H Wu
- Department of Family Medicine, National Cheng Kung University Hospital,
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Roth B, Baccanari DP, Sigel CW, Hubbell JP, Eaddy J, Kao JC, Grace ME, Rauckman BS. 2,4-Diamino-5-benzylpyrimidines and analogues as antibacterial agents. 9. Lipophilic trimethoprim analogues as antigonococcal agents. J Med Chem 1988; 31:122-9. [PMID: 3121854 DOI: 10.1021/jm00396a018] [Citation(s) in RCA: 26] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Lipophilic analogues of trimethoprim (1) bearing 3,5-dialkyl-4-hydroxy substituents in the benzene ring are much more active in vitro against Neisseria gonorrhoeae than is 1. The 3,5-diisopropyl-4-hydroxy derivative (2) was selected as a candidate for clinical evaluation as an antigonococcal agent, and as part of the preliminary evaluation it was submitted to extended pharmacokinetic and metabolism studies in dogs. Although the compound was not extensively conjugated by metabolic enzymes, one of the methyl groups was metabolized to produce a 3-isopropyl-4-hydroxy-5-(alpha-carboxyethyl)benzyl derivative (43), which was rapidly excreted. Related analogues were likewise extensively metabolized.
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Affiliation(s)
- B Roth
- Burroughs Wellcome Co., Research Triangle Park, North Carolina 27709
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Kao JC, Perry KL, Kado CI. Indoleacetic acid complementation and its relation to host range specifying genes on the Ti plasmid of Agrobacterium tumefaciens. Mol Gen Genet 1982; 188:425-32. [PMID: 6962315 DOI: 10.1007/bf00330044] [Citation(s) in RCA: 72] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Host range variations were noted when 23 wild-type strains of Agrobacterium tumefaciens were tested on 27 different plant species. Because we have shown previously that host range specificity is conferred by the pTi plasmid, these variations in host specificity implicated genetic differences among pTi plasmids within the A. tumefaciens population that was tested. Host specificity was independent of the type of opine utilized and biotype of the strain used. These data suggested that separate genetic determinants operate for host specificity. This hypothesis was confirmed by Tn5 mutagenesis of the pTi plasmid, which generated mutants affected in host specificity. The regions of host specifying genes were located by displacement analysis of mutant pTi-plasmid-DNA restriction fragments. There are at least two sites on the pTiC58 plasmid: one within the T-region and the other about 75-77 kb to the right of this region. Mutations within the T-region were chemically complemented by indoleacetic acid, which restored the host range of the mutants. Such complementations were not observed with mutants outside the T-region.
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Abstract
1. The absorption, metabolism and excretion of isamoxole, (2-methyl-N-butyl-N-(4-methyloxazol-2-yl)propanamide), a compound with anti-allergy properties, has been studied in the rat and guinea-pig. 2. The compound was well-absorbed by both species after oral doses of 50 to 150 mg/kg. It underwent extensive first-pass metabolism in the liver, and was excreted as a mixture of metabolites, predominantly in the urine, within 48 h. 3. Three major routes of metabolism were involved, namely deacylation, oxidative ring scission and alkyl oxidation. 4. A major plasma and urine metabolite was 1-butyl-3-(1-carboxyethyl)urea, and this was accompanied by low levels of its cyclized product 3-butyl-5-methylhydantoin.
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Fei LM, Chiang CM, Tseng HC, Kao JC. Blood volume of normal Chinese. Chin Med J 1965; 84:440-50. [PMID: 5865194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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