51
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Measurement, manipulation and modeling of brain-wide neural population dynamics. Nat Commun 2021; 12:633. [PMID: 33504773 PMCID: PMC7840924 DOI: 10.1038/s41467-020-20371-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 11/24/2020] [Indexed: 12/12/2022] Open
Abstract
Neural recording technologies increasingly enable simultaneous measurement of neural activity from multiple brain areas. To gain insight into distributed neural computations, a commensurate advance in experimental and analytical methods is necessary. We discuss two opportunities towards this end: the manipulation and modeling of neural population dynamics.
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52
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Al Borno M, Vyas S, Shenoy KV, Delp SL. High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff. eLife 2020; 9:57021. [PMID: 33325369 PMCID: PMC7787661 DOI: 10.7554/elife.57021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 12/15/2020] [Indexed: 11/16/2022] Open
Abstract
A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts’ law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration (MD) variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater MD variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts’ law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of ‘good’ control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.
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Affiliation(s)
- Mazen Al Borno
- Department of Bioengineering, Stanford University, Stanford, United States.,Department of Computer Science and Engineering, University of Colorado Denver, Denver, United States
| | - Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, United States
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, United States.,Neurosciences Program, Stanford University, Stanford, United States.,Department of Electrical Engineering, Stanford University, Stanford, United States.,Wu Tsai Neuroscience Institute, Stanford University, Stanford, United States.,Department of Neurobiology, Stanford University, Stanford, United States.,Howard Hughes Medical Institute, Stanford University, Stanford, United States
| | - Scott L Delp
- Department of Bioengineering, Stanford University, Stanford, United States.,Department of Mechanical Engineering, Stanford University, Stanford, United States
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53
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Wang J, Hosseini E, Meirhaeghe N, Akkad A, Jazayeri M. Reinforcement regulates timing variability in thalamus. eLife 2020; 9:55872. [PMID: 33258769 PMCID: PMC7707818 DOI: 10.7554/elife.55872] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 11/06/2020] [Indexed: 01/19/2023] Open
Abstract
Learning reduces variability but variability can facilitate learning. This paradoxical relationship has made it challenging to tease apart sources of variability that degrade performance from those that improve it. We tackled this question in a context-dependent timing task requiring humans and monkeys to flexibly produce different time intervals with different effectors. We identified two opposing factors contributing to timing variability: slow memory fluctuation that degrades performance and reward-dependent exploratory behavior that improves performance. Signatures of these opposing factors were evident across populations of neurons in the dorsomedial frontal cortex (DMFC), DMFC-projecting neurons in the ventrolateral thalamus, and putative target of DMFC in the caudate. However, only in the thalamus were the performance-optimizing regulation of variability aligned to the slow performance-degrading memory fluctuations. These findings reveal how variability caused by exploratory behavior might help to mitigate other undesirable sources of variability and highlight a potential role for thalamocortical projections in this process.
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Affiliation(s)
- Jing Wang
- Department of Bioengineering, University of Missouri, Columbia, United States.,McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Eghbal Hosseini
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Nicolas Meirhaeghe
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, United States
| | - Adam Akkad
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Mehrdad Jazayeri
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
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54
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Vassiliadis P, Derosiere G, Grandjean J, Duque J. Motor training strengthens corticospinal suppression during movement preparation. J Neurophysiol 2020; 124:1656-1666. [DOI: 10.1152/jn.00378.2020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Movement preparation involves a broad suppression in the excitability of the corticospinal pathway, a phenomenon called preparatory suppression. Here, we show that motor training strengthens preparatory suppression and that this strengthening is associated with faster reaction times. Our findings highlight a key role of preparatory suppression in training-driven behavioral improvements.
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Affiliation(s)
- Pierre Vassiliadis
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
- Center for Neuroprosthetics and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Campus Biotech, Geneva, Switzerland
| | - Gerard Derosiere
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Julien Grandjean
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Julie Duque
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
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55
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Maeda RS, Kersten R, Pruszynski JA. Shared internal models for feedforward and feedback control of arm dynamics in non-human primates. Eur J Neurosci 2020; 53:1605-1620. [PMID: 33222285 DOI: 10.1111/ejn.15056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 11/30/2022]
Abstract
Previous work has shown that humans account for and learn novel properties or the arm's dynamics, and that such learning causes changes in both the predictive (i.e., feedforward) control of reaching and reflex (i.e., feedback) responses to mechanical perturbations. Here we show that similar observations hold in old-world monkeys (Macaca fascicularis). Two monkeys were trained to use an exoskeleton to perform a single-joint elbow reaching and to respond to mechanical perturbations that created pure elbow motion. Both of these tasks engaged robust shoulder muscle activity as required to account for the torques that typically arise at the shoulder when the forearm rotates around the elbow joint (i.e., intersegmental dynamics). We altered these intersegmental arm dynamics by having the monkeys generate the same elbow movements with the shoulder joint either free to rotate, as normal, or fixed by the robotic manipulandum, which eliminates the shoulder torques caused by forearm rotation. After fixing the shoulder joint, we found a systematic reduction in shoulder muscle activity. In addition, after releasing the shoulder joint again, we found evidence of kinematic aftereffects (i.e., reach errors) in the direction predicted if failing to compensate for normal arm dynamics. We also tested whether such learning transfers to feedback responses evoked by mechanical perturbations and found a reduction in shoulder feedback responses, as appropriate for these altered arm intersegmental dynamics. Demonstrating this learning and transfer in non-human primates will allow the investigation of the neural mechanisms involved in feedforward and feedback control of the arm's dynamics.
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Affiliation(s)
- Rodrigo S Maeda
- Brain and Mind Institute, Western University, London, ON, Canada.,Robarts Research Institute, Western University, London, ON, Canada.,Department of Psychology, Western University, London, ON, Canada
| | - Rhonda Kersten
- Robarts Research Institute, Western University, London, ON, Canada.,Department of Physiology and Pharmacology, Western University, London, ON, Canada
| | - J Andrew Pruszynski
- Brain and Mind Institute, Western University, London, ON, Canada.,Robarts Research Institute, Western University, London, ON, Canada.,Department of Psychology, Western University, London, ON, Canada.,Department of Physiology and Pharmacology, Western University, London, ON, Canada
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56
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Maintained Representations of the Ipsilateral and Contralateral Limbs during Bimanual Control in Primary Motor Cortex. J Neurosci 2020; 40:6732-6747. [PMID: 32703902 DOI: 10.1523/jneurosci.0730-20.2020] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/26/2022] Open
Abstract
Primary motor cortex (M1) almost exclusively controls the contralateral side of the body. However, M1 activity is also modulated during ipsilateral body movements. Previous work has shown that M1 activity related to the ipsilateral arm is independent of the M1 activity related to the contralateral arm. How do these patterns of activity interact when both arms move simultaneously? We explored this problem by training 2 monkeys (male, Macaca mulatta) in a postural perturbation task while recording from M1. Loads were applied to one arm at a time (unimanual) or both arms simultaneously (bimanual). We found 83% of neurons (n = 236) were responsive to both the unimanual and bimanual loads. We also observed a small reduction in activity magnitude during the bimanual loads for both limbs (25%). Across the unimanual and bimanual loads, neurons largely maintained their preferred load directions. However, there was a larger change in the preferred loads for the ipsilateral limb (∼25%) than the contralateral limb (∼9%). Lastly, we identified the contralateral and ipsilateral subspaces during the unimanual loads and found they captured a significant amount of the variance during the bimanual loads. However, the subspace captured more of the bimanual variance related to the contralateral limb (97%) than the ipsilateral limb (66%). Our results highlight that, even during bimanual motor actions, M1 largely retains its representations of the contralateral and ipsilateral limbs.SIGNIFICANCE STATEMENT Previous work has shown that primary motor cortex (M1) represents information related to the contralateral limb, its downstream target, but also reflects information related to the ipsilateral limb. Can M1 still represent both sources of information when performing simultaneous movements of the limbs? Here we record from M1 during a postural perturbation task. We show that activity related to the contralateral limb is maintained between unimanual and bimanual motor actions, whereas the activity related to the ipsilateral limb undergoes a small change between unimanual and bimanual motor actions. Our results indicate that two independent representations can be maintained and expressed simultaneously in M1.
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57
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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] [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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58
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Liu Y, Brincat SL, Miller EK, Hasselmo ME. A Geometric Characterization of Population Coding in the Prefrontal Cortex and Hippocampus during a Paired-Associate Learning Task. J Cogn Neurosci 2020; 32:1455-1465. [PMID: 32379002 DOI: 10.1162/jocn_a_01569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some "silent" mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.
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59
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Vyas S, O'Shea DJ, Ryu SI, Shenoy KV. Causal Role of Motor Preparation during Error-Driven Learning. Neuron 2020; 106:329-339.e4. [PMID: 32053768 PMCID: PMC7185427 DOI: 10.1016/j.neuron.2020.01.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 11/12/2019] [Accepted: 01/16/2020] [Indexed: 11/28/2022]
Abstract
Current theories suggest that an error-driven learning process updates trial-by-trial to facilitate motor adaptation. How this process interacts with motor cortical preparatory activity-which current models suggest plays a critical role in movement initiation-remains unknown. Here, we evaluated the role of motor preparation during visuomotor adaptation. We found that preparation time was inversely correlated to variance of errors on current trials and mean error on subsequent trials. We also found causal evidence that intracortical microstimulation during motor preparation was sufficient to disrupt learning. Surprisingly, stimulation did not affect current trials, but instead disrupted the update computation of a learning process, thereby affecting subsequent trials. This is consistent with a Bayesian estimation framework where the motor system reduces its learning rate by virtue of lowering error sensitivity when faced with uncertainty. This interaction between motor preparation and the error-driven learning system may facilitate new probes into mechanisms underlying trial-by-trial adaptation.
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Affiliation(s)
- Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Daniel J O'Shea
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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60
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Saha S, Baumert M. Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review. Front Comput Neurosci 2020; 13:87. [PMID: 32038208 PMCID: PMC6985367 DOI: 10.3389/fncom.2019.00087] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/16/2019] [Indexed: 12/05/2022] Open
Abstract
Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.
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Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
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61
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Heming EA, Cross KP, Takei T, Cook DJ, Scott SH. Independent representations of ipsilateral and contralateral limbs in primary motor cortex. eLife 2019; 8:e48190. [PMID: 31625506 PMCID: PMC6824843 DOI: 10.7554/elife.48190] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 10/17/2019] [Indexed: 02/04/2023] Open
Abstract
Several lines of research demonstrate that primary motor cortex (M1) is principally involved in controlling the contralateral side of the body. However, M1 activity has been correlated with both contralateral and ipsilateral limb movements. Why does ipsilaterally-related activity not cause contralateral motor output? To address this question, we trained monkeys to counter mechanical loads applied to their right and left limbs. We found >50% of M1 neurons had load-related activity for both limbs. Contralateral loads evoked changes in activity ~10ms sooner than ipsilateral loads. We also found corresponding population activities were distinct, with contralateral activity residing in a subspace that was orthogonal to the ipsilateral activity. Thus, neural responses for the contralateral limb can be extracted without interference from the activity for the ipsilateral limb, and vice versa. Our results show that M1 activity unrelated to downstream motor targets can be segregated from activity related to the downstream motor output.
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Affiliation(s)
- Ethan A Heming
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
| | - Kevin P Cross
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
| | - Tomohiko Takei
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
- Graduate School of Medicine, The Hakubi Center for Advanced ResearchKyoto UniversityKyotoJapan
| | - Douglas J Cook
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
- Department of SurgeryQueen’s UniversityKingstonCanada
- Department of SurgeryDalhousie UniversityHalifaxCanada
| | - Stephen H Scott
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
- Department of MedicineQueen’s UniversityKingstonCanada
- Department of Biomedical and Molecular SciencesQueen’s UniversityKingstonCanada
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62
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Yousefi A, Basu I, Paulk AC, Peled N, Eskandar EN, Dougherty DD, Cash SS, Widge AS, Eden UT. Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach. Neural Comput 2019; 31:1751-1788. [DOI: 10.1162/neco_a_01196] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants ([Formula: see text]) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.
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Affiliation(s)
- Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, U.S.A
| | - Ishita Basu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A
| | - Angelique C. Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, U.S.A
| | - Noam Peled
- Department of Radiology, MBGH/HST Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA 02114, U.S.A
| | - Emad N. Eskandar
- Department of Neurological Surgery, Albert Einstein College of Medicine, Bronx, NY 10461, U.S.A
| | - Darin D. Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, U.S.A
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, U.S.A
| | - Alik S. Widge
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, U.S.A
| | - Uri T. Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, U.S.A
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63
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Oby ER, Golub MD, Hennig JA, Degenhart AD, Tyler-Kabara EC, Yu BM, Chase SM, Batista AP. New neural activity patterns emerge with long-term learning. Proc Natl Acad Sci U S A 2019; 116:15210-15215. [PMID: 31182595 PMCID: PMC6660765 DOI: 10.1073/pnas.1820296116] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.
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Affiliation(s)
- Emily R Oby
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213
- University of Pittsburgh Brain Institute, Pittsburgh, PA 15213
- Systems Neuroscience Center, University of Pittsburgh, Pittsburgh, PA 15213
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305
| | - Jay A Hennig
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213
- Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Alan D Degenhart
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213
- University of Pittsburgh Brain Institute, Pittsburgh, PA 15213
- Systems Neuroscience Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Elizabeth C Tyler-Kabara
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15213
| | - Byron M Yu
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Steven M Chase
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213
- Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Aaron P Batista
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213;
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213
- University of Pittsburgh Brain Institute, Pittsburgh, PA 15213
- Systems Neuroscience Center, University of Pittsburgh, Pittsburgh, PA 15213
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64
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Bansal S, Murthy KG, Fitzgerald J, Schwartz BL, Joiner WM. Reduced transfer of visuomotor adaptation is associated with aberrant sense of agency in schizophrenia. Neuroscience 2019; 413:108-122. [PMID: 31228588 DOI: 10.1016/j.neuroscience.2019.06.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 06/10/2019] [Accepted: 06/10/2019] [Indexed: 11/27/2022]
Abstract
One deficit associated with schizophrenia (SZ) is the reduced ability to distinguish self-caused sensations from those due to external sources. This reduced sense of agency (SoA, subjective awareness of control over one's actions) is hypothesized to result from a diminished utilization of internal monitoring signals of self-movement (i.e., efference copy) which subsequently impairs forming and utilizing sensory prediction errors (differences between the predicted and actual sensory consequences resulting from movement). Another important function of these internal monitoring signals is the facilitation of higher-order mechanisms related to motor learning and control. Current predictive-coding models of adaptation postulate that the sensory consequences of motor commands are predicted based on internal action-related information, and that ownership and control of motor behavior is modified in various contexts based on predictive processing. Here, we investigated the connections between SoA and motor adaptation. Schizophrenia patients (SZP, N=30) and non-psychiatric control subjects (HC, N=31) adapted to altered movement visual feedback and applied the motor recalibration to untested contexts (i.e., the spatial generalization). Although adaptation was similar for SZP and controls, the extent of generalization was significantly less for SZP; movement trajectories made by patients to the furthest untrained target (135o) before and after adaptation were largely indistinguishable. Interestingly, deficits in generalization were correlated with positive symptoms of psychosis in SZP (e.g., hallucinations). Generalization was also associated with measures of SoA across both SZP and HC, emphasizing the role action awareness plays in motor behavior, and suggesting that misattributing agency, even in HC, manifests in abnormal motor performance.
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Affiliation(s)
- Sonia Bansal
- Department of Neuroscience, George Mason University, Fairfax, Virginia; Mental Health Service Line, Washington DC Veterans Affairs Medical Center, Washington, DC; Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21224
| | - Karthik G Murthy
- Department of Bioengineering, George Mason University, Fairfax, Virginia
| | - Justin Fitzgerald
- Department of Bioengineering, George Mason University, Fairfax, Virginia
| | - Barbara L Schwartz
- Mental Health Service Line, Washington DC Veterans Affairs Medical Center, Washington, DC; Department of Psychiatry, Georgetown University School of Medicine, Washington, DC
| | - Wilsaan M Joiner
- Department of Neuroscience, George Mason University, Fairfax, Virginia; Department of Bioengineering, George Mason University, Fairfax, Virginia; Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia; Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA 95616.
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65
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Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT, O'Shea DJ, Vyas S, Sun X, Ryu SI, Ganguli S, Shenoy KV. Accurate Estimation of Neural Population Dynamics without Spike Sorting. Neuron 2019; 103:292-308.e4. [PMID: 31171448 DOI: 10.1016/j.neuron.2019.05.003] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 02/06/2019] [Accepted: 04/30/2019] [Indexed: 11/25/2022]
Abstract
A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.
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Affiliation(s)
- Eric M Trautmann
- Neurosciences Program, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
| | - Sergey D Stavisky
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Subhaneil Lahiri
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Katherine C Ames
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Neuroscience, Columbia University, New York, NY, USA
| | - Matthew T Kaufman
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Daniel J O'Shea
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Palo Alto Medical Foundation, Palo Alto, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Neurobiology, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford, CA, USA; Bio-X Program, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Neurobiology, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford, CA, USA; Bio-X Program, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
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66
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Slutzky MW. Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations. Neuroscientist 2019; 25:139-154. [PMID: 29772957 PMCID: PMC6611552 DOI: 10.1177/1073858418775355] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Brain-machine interfaces (BMIs) have exploded in popularity in the past decade. BMIs, also called brain-computer interfaces, provide a direct link between the brain and a computer, usually to control an external device. BMIs have a wide array of potential clinical applications, ranging from restoring communication to people unable to speak due to amyotrophic lateral sclerosis or a stroke, to restoring movement to people with paralysis from spinal cord injury or motor neuron disease, to restoring memory to people with cognitive impairment. Because BMIs are controlled directly by the activity of prespecified neurons or cortical areas, they also provide a powerful paradigm with which to investigate fundamental questions about brain physiology, including neuronal behavior, learning, and the role of oscillations. This article reviews the clinical and neuroscientific applications of BMIs, with a primary focus on motor BMIs.
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Affiliation(s)
- Marc W Slutzky
- 1 Departments of Neurology, Physiology, and Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA
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67
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Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex. Neuron 2019; 102:694-705.e3. [PMID: 30853300 DOI: 10.1016/j.neuron.2019.02.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 11/05/2018] [Accepted: 02/06/2019] [Indexed: 11/22/2022]
Abstract
Although animal studies provided significant insights in understanding the neural basis of learning and adaptation, they often cannot dissociate between different learning mechanisms due to the lack of verbal communication. To overcome this limitation, we examined the mechanisms of learning and its limits in a human intracortical brain-machine interface (BMI) paradigm. A tetraplegic participant controlled a 2D computer cursor by modulating single-neuron activity in the anterior intraparietal area (AIP). By perturbing the neuron-to-movement mapping, the participant learned to modulate the activity of the recorded neurons to solve the perturbations by adopting a target re-aiming strategy. However, when no cognitive strategies were adequate to produce correct responses, AIP failed to adapt to perturbations. These findings suggest that learning is constrained by the pre-existing neuronal structure, although it is possible that AIP needs more training time to learn to generate novel activity patterns when cognitive re-adaptation fails to solve the perturbations.
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68
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Even-Chen N, Sheffer B, Vyas S, Ryu SI, Shenoy KV. Structure and variability of delay activity in premotor cortex. PLoS Comput Biol 2019; 15:e1006808. [PMID: 30794541 PMCID: PMC6402694 DOI: 10.1371/journal.pcbi.1006808] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 03/06/2019] [Accepted: 01/21/2019] [Indexed: 11/18/2022] Open
Abstract
Voluntary movements are widely considered to be planned before they are executed. Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an ‘initial condition’ which seeds the proceeding neural dynamics. Here, we studied these initial conditions in detail by investigating 1) the organization of neural states for different reaches and 2) the variance of these neural states from trial to trial. We examined population-level responses in macaque premotor cortex (PMd) during the preparatory stage of an instructed-delay center-out reaching task with dense target configurations. We found that after target onset the neural activity on single trials converges to neural states that have a clear low-dimensional structure which is organized by both the reach endpoint and maximum speed of the following reach. Further, we found that variability of the neural states during preparation resembles the spatial variability of reaches made in the absence of visual feedback: there is less variability in direction than distance in neural state space. We also used offline decoding to understand the implications of this neural population structure for brain-machine interfaces (BMIs). We found that decoding of angle between reaches is dependent on reach distance, while decoding of arc-length is independent. Thus, it might be more appropriate to quantify decoding performance for discrete BMIs by using arc-length between reach end-points rather than the angle between them. Lastly, we show that in contrast to the common notion that direction can better be decoded than distance, their decoding capabilities are comparable. These results provide new insights into the dynamical neural processes that underline motor control and can inform the design of BMIs. Early studies of premotor cortex explored how individual neurons directly encode aspects of an upcoming movement during preparation. Recent developments have proposed that the dynamics of populations of neurons underlie motor control, and that neural activity during preparation serves to set up these dynamics. While the dynamics of motor control have been studied extensively, several aspects of preparatory activity remain unresolved. Here, we ask how the patterns of neural activity during preparation for different reaches are related to one another. We found that the neural activity during preparation for reaches to different targets has a clear ‘structure’. Additionally, we found that the activity on a given trial was predictive of the initial trajectory of the reach. Lastly, we assessed the implications of our findings for predicting upcoming movements from neural activity, as in brain-machine interfaces.
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Affiliation(s)
- Nir Even-Chen
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- * E-mail:
| | - Blue Sheffer
- Department of Computer Science, Stanford University, Stanford, CA, United States of America
| | - Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
| | - Stephen I. Ryu
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, United States of America
| | - Krishna V. Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
- Department of Neurobiology, Stanford University, Stanford, CA, United States of America
- The Bio-X Program, Stanford University, Stanford, CA, United States of America
- The Stanford Neurosciences Institute, Stanford University, Stanford, CA, United States of America
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, United States of America
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69
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Zhou X, Tien RN, Ravikumar S, Chase SM. Distinct types of neural reorganization during long-term learning. J Neurophysiol 2019; 121:1329-1341. [PMID: 30726164 DOI: 10.1152/jn.00466.2018] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
What are the neural mechanisms of skill acquisition? Many studies find that long-term practice is associated with a functional reorganization of cortical neural activity. However, the link between these changes in neural activity and the behavioral improvements that occur is not well understood, especially for long-term learning that takes place over several weeks. To probe this link in detail, we leveraged a brain-computer interface (BCI) paradigm in which rhesus monkeys learned to master nonintuitive mappings between neural spiking in primary motor cortex and computer cursor movement. Critically, these BCI mappings were designed to disambiguate several different possible types of neural reorganization. We found that during the initial phase of learning, lasting minutes to hours, rapid changes in neural activity common to all neurons led to a fast suppression of motor error. In parallel, local changes to individual neurons gradually accrued over several weeks of training. This slower timescale cortical reorganization persisted long after the movement errors had decreased to asymptote and was associated with more efficient control of movement. We conclude that long-term practice evokes two distinct neural reorganization processes with vastly different timescales, leading to different aspects of improvement in motor behavior. NEW & NOTEWORTHY We leveraged a brain-computer interface learning paradigm to track the neural reorganization occurring throughout the full time course of motor skill learning lasting several weeks. We report on two distinct types of neural reorganization that mirror distinct phases of behavioral improvement: a fast phase, in which global reorganization of neural recruitment leads to a quick suppression of motor error, and a slow phase, in which local changes in individual tuning lead to improvements in movement efficiency.
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Affiliation(s)
- Xiao Zhou
- Department of Biomedical Engineering, Carnegie Mellon University , Pittsburgh, Pennsylvania.,Center for the Neural Basis of Cognition, Carnegie Mellon University , Pittsburgh, Pennsylvania
| | - Rex N Tien
- Center for the Neural Basis of Cognition, Carnegie Mellon University , Pittsburgh, Pennsylvania.,Department of Bioengineering, University of Pittsburgh , Pittsburgh, Pennsylvania
| | - Sadhana Ravikumar
- Department of Biomedical Engineering, Carnegie Mellon University , Pittsburgh, Pennsylvania
| | - Steven M Chase
- Department of Biomedical Engineering, Carnegie Mellon University , Pittsburgh, Pennsylvania.,Center for the Neural Basis of Cognition, Carnegie Mellon University , Pittsburgh, Pennsylvania
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70
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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. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 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] [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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71
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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] [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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72
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Pandarinath C, Ames KC, Russo AA, Farshchian A, Miller LE, Dyer EL, Kao JC. Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces. J Neurosci 2018; 38:9390-9401. [PMID: 30381431 PMCID: PMC6209846 DOI: 10.1523/jneurosci.1669-18.2018] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 09/24/2018] [Accepted: 09/25/2018] [Indexed: 01/07/2023] Open
Abstract
In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.
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Affiliation(s)
- Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322,
- Department of Neurosurgery, Emory University, Atlanta, Georgia 30322
| | - K Cora Ames
- Department of Neuroscience
- Center for Theoretical Neuroscience
- Grossman Center for the Statistics of Mind
- Zuckerman Institute, Columbia University, New York, New York 10027
| | - Abigail A Russo
- Department of Neuroscience
- Grossman Center for the Statistics of Mind
- Zuckerman Institute, Columbia University, New York, New York 10027
| | - Ali Farshchian
- Department of Physiology, Northwestern University, Chicago, Illinois 60611
| | - Lee E Miller
- Department of Physiology, Northwestern University, Chicago, Illinois 60611
| | - Eva L Dyer
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, and
- Neurosciences Program, University of California, Los Angeles, California 90095
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73
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López-Schier H. Neuroplasticity in the acoustic startle reflex in larval zebrafish. Curr Opin Neurobiol 2018; 54:134-139. [PMID: 30359930 DOI: 10.1016/j.conb.2018.10.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Accepted: 10/04/2018] [Indexed: 12/22/2022]
Abstract
Learning is essential for animal survival under changing environments. Even in its simplest form, learning involves interactions between a handful of neuronal circuits, hundreds of neurons and many thousand synapses. In this review I will focus on habituation - a form of non-associative learning during which organisms decrease their response to repetitions of identical sensory stimuli. I will discuss how recent studies of the acoustic startle reflex mediated by the Mauthner cell in the zebrafish larva are helping to understand the neuroplastic processes that underlie habituation. In addition to being a fascinating biological process, habituation is clinically relevant because it is affected in various neuropsychiatric disorders in humans, including autism, schizophrenia, Fragile-X and Tourette's syndromes.
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Affiliation(s)
- Hernán López-Schier
- Research Unit Sensory Biology & Organogenesis, Helmholtz Zentrum Munich, Neuherberg 85764, Germany.
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74
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Perich MG, Gallego JA, Miller LE. A Neural Population Mechanism for Rapid Learning. Neuron 2018; 100:964-976.e7. [PMID: 30344047 DOI: 10.1016/j.neuron.2018.09.030] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/16/2018] [Accepted: 09/21/2018] [Indexed: 12/18/2022]
Abstract
Long-term learning of language, mathematics, and motor skills likely requires cortical plasticity, but behavior often requires much faster changes, sometimes even after single errors. Here, we propose one neural mechanism to rapidly develop new motor output without altering the functional connectivity within or between cortical areas. We tested cortico-cortical models relating the activity of hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices throughout adaptation to reaching movement perturbations. We found a signature of learning in the "output-null" subspace of PMd with respect to M1 reflecting the ability of premotor cortex to alter preparatory activity without directly influencing M1. The output-null subspace planning activity evolved with adaptation, yet the "output-potent" mapping that captures information sent to M1 was preserved. Our results illustrate a population-level cortical mechanism to progressively adjust the output from one brain area to its downstream structures that could be exploited for rapid behavioral adaptation.
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Affiliation(s)
- Matthew G Perich
- Department of Biomedical Engineering, Northwestern University, Chicago, IL 60611, USA
| | - Juan A Gallego
- Department of Physiology, Northwestern University, Chicago, IL 60611, USA; Neural and Cognitive Engineering Group, Centre for Automation and Robotics, CSIC-UPM, 28500 Arganda del Rey, Madrid, Spain
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Chicago, IL 60611, USA; Department of Physiology, Northwestern University, Chicago, IL 60611, USA; Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA.
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75
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Gallego JA, Perich MG, Naufel SN, Ethier C, Solla SA, Miller LE. Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nat Commun 2018; 9:4233. [PMID: 30315158 PMCID: PMC6185944 DOI: 10.1038/s41467-018-06560-z] [Citation(s) in RCA: 137] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Accepted: 09/12/2018] [Indexed: 12/31/2022] Open
Abstract
Populations of cortical neurons flexibly perform different functions; for the primary motor cortex (M1) this means a rich repertoire of motor behaviors. We investigate the flexibility of M1 movement control by analyzing neural population activity during a variety of skilled wrist and reach-to-grasp tasks. We compare across tasks the neural modes that capture dominant neural covariance patterns during each task. While each task requires different patterns of muscle and single unit activity, we find unexpected similarities at the neural population level: the structure and activity of the neural modes is largely preserved across tasks. Furthermore, we find two sets of neural modes with task-independent activity that capture, respectively, generic temporal features of the set of tasks and a task-independent mapping onto muscle activity. This system of flexibly combined, well-preserved neural modes may underlie the ability of M1 to learn and generate a wide-ranging behavioral repertoire.
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Affiliation(s)
- Juan A Gallego
- Department of Physiology, Feinberg School of Medicine, Northwestern University, 303 E. Chicago Avenue, Chicago, IL, 60611, USA.
- Neural and Cognitive Engineering Group, Centre for Automation and Robotics CSIC-UPM, Ctra. Campo Real km 0.2 - La Poveda, 28500, Arganda del Rey, Spain.
| | - Matthew G Perich
- Department of Biomedical Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA
| | - Stephanie N Naufel
- Department of Biomedical Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA
| | - Christian Ethier
- Département de Psychiatrie et Neurosciences, Université Laval, CERVO Research Center, 2601 Ch. de la Canardière, Québec, QC, G1J 2G3, Canada
| | - Sara A Solla
- Department of Physiology, Feinberg School of Medicine, Northwestern University, 303 E. Chicago Avenue, Chicago, IL, 60611, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
| | - Lee E Miller
- Department of Physiology, Feinberg School of Medicine, Northwestern University, 303 E. Chicago Avenue, Chicago, IL, 60611, USA.
- Department of Biomedical Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA.
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA.
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76
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Imagery of movements immediately following performance allows learning of motor skills that interfere. Sci Rep 2018; 8:14330. [PMID: 30254381 PMCID: PMC6156339 DOI: 10.1038/s41598-018-32606-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 09/05/2018] [Indexed: 12/15/2022] Open
Abstract
Motor imagery, that is the mental rehearsal of a motor skill, can lead to improvements when performing the same skill. Here we show a powerful and complementary role, in which motor imagery of different movements after actually performing a skill allows learning that is not possible without imagery. We leverage a well-studied motor learning task in which subjects reach in the presence of a dynamic (force-field) perturbation. When two opposing perturbations are presented alternately for the same physical movement, there is substantial interference, preventing any learning. However, when the same physical movement is associated with follow-through movements that differ for each perturbation, both skills can be learned. Here we show that when subjects perform the skill and only imagine the follow-through, substantial learning occurs. In contrast, without such motor imagery there was no learning. Therefore, motor imagery can have a profound effect on skill acquisition even when the imagery is not of the skill itself. Our results suggest that motor imagery may evoke different neural states for the same physical state, thereby enhancing learning.
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77
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Hennig JA, Golub MD, Lund PJ, Sadtler PT, Oby ER, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Yu BM, Chase SM. Constraints on neural redundancy. eLife 2018; 7:36774. [PMID: 30109848 PMCID: PMC6130976 DOI: 10.7554/elife.36774] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 08/06/2018] [Indexed: 12/24/2022] Open
Abstract
Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation. When you swing a tennis racket, muscles in your arm contract in a specific sequence. For this to happen, millions of neurons in your brain and spinal cord must fire to make those muscles contract. If you swing the racket a second time, the same muscles in your arm will contract again. But the firing pattern of the underlying neurons will probably be different. This phenomenon, in which different patterns of neural activity generate the same outcome, is called neural redundancy. Neural redundancy allows a set of neurons to perform multiple tasks at once. For example, the same neurons may drive an arm movement while simultaneously planning the next activity. But does performing a given task constrain how often different patterns of neural activity can be produced? If so, this would limit whether other tasks could be carried out at the same time. To address this, Hennig et al. trained macaque monkeys to use a brain-computer interface (BCI). This is a device that reads out electrical brain activity and converts it into signals that can be used to control another device. The key advantage of a BCI is that the redundant activity patterns are precisely known. The monkeys learned to use their brain activity, via the BCI, to move a cursor on a computer screen in different directions. The results revealed that monkeys could only produce a limited number of different patterns of brain activity for a given BCI cursor movement. This suggests that the ability of a group of neurons to multitask is restricted. For example, if the same set of neurons is involved in both planning and performing movements, then an animal’s ability to plan a future movement will depend on the one it is currently performing. BCIs can help patients who have suffered stroke or paralysis. They enable patients to use their brain activity to control a computer or even robotic limbs. Understanding how the brain controls BCIs will help us improve their performance and deepen our knowledge of how the brain plans and performs movements. This might include designing BCIs that allow users to multitask more effectively.
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Affiliation(s)
- Jay A Hennig
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Machine Learning Department, Carnegie Mellon University, Pittsburgh, United States
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, United States
| | - Peter J Lund
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Machine Learning Department, Carnegie Mellon University, Pittsburgh, United States.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, United States
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States
| | - Stephen I Ryu
- Department of Neurosurgery, Palo Alto Medical Foundation, California, United States.,Department of Electrical Engineering, Stanford University, California, United States
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, United States
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States
| | - Byron M Yu
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, United States.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, United States
| | - Steven M Chase
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, United States
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78
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Williams AH, Kim TH, Wang F, Vyas S, Ryu SI, Shenoy KV, Schnitzer M, Kolda TG, Ganguli S. Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis. Neuron 2018; 98:1099-1115.e8. [PMID: 29887338 DOI: 10.1016/j.neuron.2018.05.015] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/18/2018] [Accepted: 05/08/2018] [Indexed: 01/19/2023]
Abstract
Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.
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Affiliation(s)
- Alex H Williams
- Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA.
| | - Tony Hyun Kim
- Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA
| | - Forea Wang
- Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Saurabh Vyas
- Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering 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
- Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA; Neurobiology Department, 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
| | - Mark Schnitzer
- Applied Physics Department, Stanford University, Stanford, CA 94305, USA; Biology Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; CNC Program, Stanford University, Stanford, CA 94305, USA
| | | | - Surya Ganguli
- Applied Physics Department, Stanford University, Stanford, CA 94305, USA; Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA.
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79
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Golub MD, Sadtler PT, Oby ER, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Chase SM, Yu BM. Learning by neural reassociation. Nat Neurosci 2018. [PMID: 29531364 PMCID: PMC5876156 DOI: 10.1038/s41593-018-0095-3] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of Reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.
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Affiliation(s)
- Matthew D Golub
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Elizabeth C Tyler-Kabara
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven M Chase
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA. .,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Byron M Yu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. .,Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA. .,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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80
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Abstract
Previous research has shown that mental rehearsal can improve performance. A new study by Vyas et al. (2018) reveals that direct modulation of neural dynamics using a brain-computer interface can also modify physical movements. The study further demonstrates that "mental practice" and physical movements share a common neural subspace.
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Affiliation(s)
- Nikhilesh Natraj
- Department of Neurology, University of California, San Francisco, San Francisco, CA; Department of Neurology, San Francisco VA Medical Center, San Francisco, CA; Center for Neural Engineering and Prosthesis, University of California, Berkeley and University of California, San Francisco, Berkeley and San Francisco, CA
| | - Karunesh Ganguly
- Department of Neurology, University of California, San Francisco, San Francisco, CA; Department of Neurology, San Francisco VA Medical Center, San Francisco, CA; Center for Neural Engineering and Prosthesis, University of California, Berkeley and University of California, San Francisco, Berkeley and San Francisco, CA; Kavli Institute for Fundamental Neuroscience, San Francisco, CA.
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