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Guan C, Aflalo T, Kadlec K, Gámez de Leon J, Rosario ER, Bari A, Pouratian N, Andersen RA. Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex. J Neural Eng 2023; 20:036020. [PMID: 37160127 PMCID: PMC10209510 DOI: 10.1088/1741-2552/acd3b1] [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: 12/02/2022] [Revised: 03/24/2023] [Accepted: 05/09/2023] [Indexed: 05/11/2023]
Abstract
Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis.Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands.Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands.Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.
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Affiliation(s)
- Charles Guan
- California Institute of Technology, Pasadena, CA, United States of America
| | - Tyson Aflalo
- California Institute of Technology, Pasadena, CA, United States of America
- T&C Chen Brain-Machine Interface Center at Caltech, Pasadena, CA, United States of America
| | - Kelly Kadlec
- California Institute of Technology, Pasadena, CA, United States of America
| | | | - Emily R Rosario
- Casa Colina Hospital and Centers for Healthcare, Pomona, CA, United States of America
| | - Ausaf Bari
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States of America
| | - Nader Pouratian
- University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Richard A Andersen
- California Institute of Technology, Pasadena, CA, United States of America
- T&C Chen Brain-Machine Interface Center at Caltech, Pasadena, CA, United States of America
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Klaes C, Pilacinski A, Kellis S, Aflalo T, Liu C, Andersen R. Neural representations of economic decision variables in human posterior parietal cortex. bioRxiv 2023:2023.05.18.541297. [PMID: 37293079 PMCID: PMC10245787 DOI: 10.1101/2023.05.18.541297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Decision making has been intensively studied in the posterior parietal cortex in non-human primates on a single neuron level. In humans decision making has mainly been studied with psychophysical tools or with fMRI. Here, we investigated how single neurons from human posterior parietal cortex represent numeric values informing future decisions during a complex two-player game. The tetraplegic study participant was implanted with a Utah electrode array in the anterior intraparietal area (AIP). We played a simplified variant of Black Jack with the participant while neuronal data was recorded. During the game two players are presented with numbers which are added up. Each time a number is presented the player has to decide to proceed or to stop. Once the first player stops or the score reaches a limit the turn passes on to the second player who tries to beat the score of the first player. Whoever is closer to the limit (without overshooting) wins the game. We found that many AIP neurons selectively responded to the face value of the presented number. Other neurons tracked the cumulative score or were selectively active for the upcoming decision of the study participant. Interestingly, some cells also kept track of the opponent's score. Our findings show that parietal regions engaged in hand action control also represent numbers and their complex transformations. This is also the first demonstration of complex economic decisions being possible to track in single neuron activity in human AIP. Our findings show how tight are the links between parietal neural circuits underlying hand control, numerical cognition and complex decision-making.
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Guan C, Aflalo T, Zhang CY, Amoruso E, Rosario ER, Pouratian N, Andersen RA. Stability of motor representations after paralysis. eLife 2022; 11:74478. [PMID: 36125116 PMCID: PMC9555862 DOI: 10.7554/elife.74478] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 10/06/2021] [Accepted: 08/27/2022] [Indexed: 11/13/2022] Open
Abstract
Neural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in the posterior parietal cortex (PPC) of a tetraplegic adult as she controlled a virtual hand through a brain–computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers. Neural activity during finger movements exhibited robust representational structure similar to fMRI recordings of able-bodied individuals’ motor cortex, which is known to reflect able-bodied usage patterns. The finger representational structure was consistent throughout multiple sessions, even though the structure contributed to BCI decoding errors. Within individual BCI movements, the representational structure was dynamic, first resembling muscle activation patterns and then resembling the anticipated sensory consequences. Our results reveal that motor representations in PPC reflect able-bodied motor usage patterns even after paralysis, and BCIs can re-engage these stable representations to restore lost motor functions.
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Affiliation(s)
- Charles Guan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
| | - Tyson Aflalo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
| | - Carey Y Zhang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
| | - Elena Amoruso
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Emily R Rosario
- Casa Colina Hospital and Centers for Healthcare, Pomona, United States
| | - Nader Pouratian
- 5David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
| | - Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
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Andersen RA, Aflalo T. Preserved cortical somatotopic and motor representations in tetraplegic humans. Curr Opin Neurobiol 2022; 74:102547. [DOI: 10.1016/j.conb.2022.102547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/16/2022] [Accepted: 03/27/2022] [Indexed: 11/16/2022]
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Aflalo T, Zhang C, Revechkis B, Rosario E, Pouratian N, Andersen RA. Implicit mechanisms of intention. Curr Biol 2022; 32:2051-2060.e6. [PMID: 35390282 PMCID: PMC9090994 DOI: 10.1016/j.cub.2022.03.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [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: 08/06/2021] [Revised: 02/03/2022] [Accepted: 03/15/2022] [Indexed: 11/28/2022]
Abstract
High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions. Here, we study the timing of human posterior parietal cortex single-neuron activity recorded from implanted microelectrode arrays relative to the explicit urge to initiate movement. Participants were free to choose when to move, whether to move, and what to move, and they retrospectively reported the time they felt the urge to move. We replicate prior studies by showing that posterior parietal cortex (PPC) neural activity sharply rises hundreds of milliseconds before the reported urge. However, we find that this "preconscious" activity is part of a dynamic neural population response that initiates much earlier, when the participant first chooses to perform the task. Together with details of neural timing, our results suggest that PPC encodes an internal model of the motor planning network that transforms high-level task objectives into appropriate motor behavior. These new data challenge traditional interpretations of early neural activity and offer a more holistic perspective on the interplay between choice, behavior, and their neural underpinnings. Our results have important implications for translating BMIs into more complex real-world environments. We find that early neural dynamics are sufficient to drive BMI movements before the participant intends to initiate movement. Appropriate algorithms ensure that BMI movements align with the subject's awareness of choice.
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Affiliation(s)
- Tyson Aflalo
- California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd., Pasadena, CA 91125, USA; California Institute of Technology, Tianqiao and Chrissy Chen Brain-Machine Interface Center, 1200 E California Blvd., Pasadena, CA 91125, USA.
| | - Carey Zhang
- California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd., Pasadena, CA 91125, USA
| | - Boris Revechkis
- California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd., Pasadena, CA 91125, USA
| | - Emily Rosario
- Casa Colina Hospital and Centers for Rehabilitation, 255 E Bonita Ave, Pomona, CA 91767, USA
| | - Nader Pouratian
- University of California, Los Angeles, Geffen School of Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Richard A Andersen
- California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd., Pasadena, CA 91125, USA; California Institute of Technology, Tianqiao and Chrissy Chen Brain-Machine Interface Center, 1200 E California Blvd., Pasadena, CA 91125, USA
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6
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Abstract
Traditional brain-machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.
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Affiliation(s)
- Richard A Andersen
- Division of Biology and Biological Engineering and Tianqiao & Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, California 91125, USA;
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, California 90033, USA
| | - Tyson Aflalo
- Division of Biology and Biological Engineering and Tianqiao & Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, California 91125, USA;
| | - Luke Bashford
- Division of Biology and Biological Engineering and Tianqiao & Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, California 91125, USA;
| | - David Bjånes
- Division of Biology and Biological Engineering and Tianqiao & Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, California 91125, USA;
| | - Spencer Kellis
- Division of Biology and Biological Engineering and Tianqiao & Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, California 91125, USA;
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, California 90033, USA
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California 90033, USA
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7
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Chivukula S, Zhang CY, Aflalo T, Jafari M, Pejsa K, Pouratian N, Andersen RA. Neural encoding of actual and imagined touch within human posterior parietal cortex. eLife 2021; 10:61646. [PMID: 33647233 PMCID: PMC7924956 DOI: 10.7554/elife.61646] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 07/31/2020] [Accepted: 02/08/2021] [Indexed: 12/27/2022] Open
Abstract
In the human posterior parietal cortex (PPC), single units encode high-dimensional information with partially mixed representations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and perception. Here, we test whether a PPC neuronal population previously demonstrated to encode visual and motor information is similarly engaged in the somatosensory domain. We recorded neurons within the PPC of a human clinical trial participant during actual touch presentation and during a tactile imagery task. Neurons encoded actual touch at short latency with bilateral receptive fields, organized by body part, and covered all tested regions. The tactile imagery task evoked body part-specific responses that shared a neural substrate with actual touch. Our results are the first neuron-level evidence of touch encoding in human PPC and its cognitive engagement during a tactile imagery task, which may reflect semantic processing, attention, sensory anticipation, or imagined touch.
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Affiliation(s)
- Srinivas Chivukula
- Department of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of TechnologyPasadenaUnited States,Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Carey Y Zhang
- Department of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of TechnologyPasadenaUnited States
| | - Tyson Aflalo
- Department of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of TechnologyPasadenaUnited States
| | - Matiar Jafari
- Department of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of TechnologyPasadenaUnited States,Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Kelsie Pejsa
- Department of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of TechnologyPasadenaUnited States
| | - Nader Pouratian
- Department of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of TechnologyPasadenaUnited States,Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Richard A Andersen
- Department of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of TechnologyPasadenaUnited States
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Jafari M, Aflalo T, Chivukula S, Kellis SS, Salas MA, Norman SL, Pejsa K, Liu CY, Andersen RA. The human primary somatosensory cortex encodes imagined movement in the absence of sensory information. Commun Biol 2020; 3:757. [PMID: 33311578 PMCID: PMC7732821 DOI: 10.1038/s42003-020-01484-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [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: 02/26/2020] [Accepted: 11/15/2020] [Indexed: 02/07/2023] Open
Abstract
Classical systems neuroscience positions primary sensory areas as early feed-forward processing stations for refining incoming sensory information. This view may oversimplify their role given extensive bi-directional connectivity with multimodal cortical and subcortical regions. Here we show that single units in human primary somatosensory cortex encode imagined reaches in a cognitive motor task, but not other sensory–motor variables such as movement plans or imagined arm position. A population reference-frame analysis demonstrates coding relative to the cued starting hand location suggesting that imagined reaching movements are encoded relative to imagined limb position. These results imply a potential role for primary somatosensory cortex in cognitive imagery, engagement during motor production in the absence of sensation or expected sensation, and suggest that somatosensory cortex can provide control signals for future neural prosthetic systems. Matiar Jafari, Tyson Aflalo et al. show that the human primary somatosensory cortex is activated when subjects imagine reaches in a cognitive motor task, but not when they plan movement or imagine a static limb position. These results highlight a role for this region in cognitive imagery and motor control in the absence of sensory information.
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Affiliation(s)
- Matiar Jafari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA.,UCLA-Caltech Medical Scientist Training Program, Los Angeles, CA, USA
| | - Tyson Aflalo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. .,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA.
| | - Srinivas Chivukula
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,Department of Neurological Surgery, Los Angeles Medical Center, University of California, Los Angeles, Los Angeles, CA, USA
| | - Spencer Sterling Kellis
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA.,USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA.,Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, USA
| | | | - Sumner Lee Norman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Kelsie Pejsa
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
| | - Charles Yu Liu
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA.,Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, USA.,Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA
| | - Richard Alan Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, USA
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Aflalo T, Zhang CY, Rosario ER, Pouratian N, Orban GA, Andersen RA. A shared neural substrate for action verbs and observed actions in human posterior parietal cortex. Sci Adv 2020; 6:6/43/eabb3984. [PMID: 33097536 PMCID: PMC7608826 DOI: 10.1126/sciadv.abb3984] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 09/03/2020] [Indexed: 06/11/2023]
Abstract
High-level sensory and motor cortical areas are activated when processing the meaning of language, but it is unknown whether, and how, words share a neural substrate with corresponding sensorimotor representations. We recorded from single neurons in human posterior parietal cortex (PPC) while participants viewed action verbs and corresponding action videos from multiple views. We find that PPC neurons exhibit a common neural substrate for action verbs and observed actions. Further, videos were encoded with mixtures of invariant and idiosyncratic responses across views. Action verbs elicited selective responses from a fraction of these invariant and idiosyncratic neurons, without preference, thus associating with a statistical sampling of the diverse sensory representations related to the corresponding action concept. Controls indicated that the results are not the product of visual imagery or arbitrary learned associations. Our results suggest that language may activate the consolidated visual experience of the reader.
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Affiliation(s)
- T Aflalo
- California Institute of Technology, Division of Biology and Biological Engineering, Pasadena, CA, USA.
- Tianqiao and Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - C Y Zhang
- California Institute of Technology, Division of Biology and Biological Engineering, Pasadena, CA, USA
- Tianqiao and Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - E R Rosario
- Casa Colina Hospital and Centers for Healthcare, Pomona, CA, USA
| | - N Pouratian
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | - G A Orban
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - R A Andersen
- California Institute of Technology, Division of Biology and Biological Engineering, Pasadena, CA, USA
- Tianqiao and Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
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10
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Chivukula S, Jafari M, Aflalo T, Yong NA, Pouratian N. Cognition in Sensorimotor Control: Interfacing With the Posterior Parietal Cortex. Front Neurosci 2019; 13:140. [PMID: 30872993 PMCID: PMC6401528 DOI: 10.3389/fnins.2019.00140] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [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: 12/31/2017] [Accepted: 02/07/2019] [Indexed: 12/19/2022] Open
Abstract
Millions of people worldwide are afflicted with paralysis from a disruption of neural pathways between the brain and the muscles. Because their cortical architecture is often preserved, these patients are able to plan movements despite an inability to execute them. In such people, brain machine interfaces have great potential to restore lost function through neuroprosthetic devices, circumventing dysfunctional corticospinal circuitry. These devices have typically derived control signals from the motor cortex (M1) which provides information highly correlated with desired movement trajectories. However, sensorimotor control simultaneously engages multiple cognitive processes such as intent, state estimation, decision making, and the integration of multisensory feedback. As such, cortical association regions upstream of M1 such as the posterior parietal cortex (PPC) that are involved in higher order behaviors such as planning and learning, rather than in encoding movement itself, may enable enhanced, cognitive control of neuroprosthetics, termed cognitive neural prosthetics (CNPs). We illustrate in this review, through a small sampling, the cognitive functions encoded in the PPC and discuss their neural representation in the context of their relevance to motor neuroprosthetics. We aim to highlight through examples a role for cortical signals from the PPC in developing CNPs, and to inspire future avenues for exploration in their research and development.
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Affiliation(s)
- Srinivas Chivukula
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Matiar Jafari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Tyson Aflalo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Nicholas Au Yong
- Department of Neurological Surgery, Los Angeles Medical Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nader Pouratian
- Department of Neurological Surgery, Los Angeles Medical Center, University of California, Los Angeles, Los Angeles, CA, United States
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11
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Chivukula S, Aflalo T, Pouratian N. 136 Posterior Parietal Cortex Encodes Peripersonal Space Within the Framework of Object Identity and Action Perception. Neurosurgery 2018. [DOI: 10.1093/neuros/nyy303.136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Rutishauser U, Aflalo T, Rosario ER, Pouratian N, Andersen RA. Single-Neuron Representation of Memory Strength and Recognition Confidence in Left Human Posterior Parietal Cortex. Neuron 2017; 97:209-220.e3. [PMID: 29249283 DOI: 10.1016/j.neuron.2017.11.029] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [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: 07/24/2017] [Revised: 10/17/2017] [Accepted: 11/17/2017] [Indexed: 10/18/2022]
Abstract
The human posterior parietal cortex (PPC) is thought to contribute to memory retrieval, but little is known about its specific role. We recorded single PPC neurons of two human tetraplegic subjects implanted with microelectrode arrays, who performed a recognition memory task. We found two groups of neurons that signaled memory-based choices. Memory-selective neurons preferred either novel or familiar stimuli, scaled their response as a function of confidence, and signaled subjective choices regardless of truth. Confidence-selective neurons signaled confidence regardless of stimulus familiarity. Memory-selective signals appeared 553 ms after stimulus onset, but before action onset. Neurons also encoded spoken numbers, but these number-tuned neurons did not carry recognition signals. Together, this functional separation reveals action-independent coding of declarative memory-based familiarity and confidence of choices in human PPC. These data suggest that, in addition to sensory-motor integration, a function of human PPC is to utilize memory signals to make choices.
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Affiliation(s)
- Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Tyson Aflalo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - Emily R Rosario
- Casa Colina Hospital and Centers for Healthcare, Pomona, CA, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | - Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
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Zhang CY, Aflalo T, Revechkis B, Rosario ER, Ouellette D, Pouratian N, Andersen RA. Partially Mixed Selectivity in Human Posterior Parietal Association Cortex. Neuron 2017; 95:697-708.e4. [PMID: 28735750 DOI: 10.1016/j.neuron.2017.06.040] [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] [Received: 02/16/2017] [Revised: 06/05/2017] [Accepted: 06/24/2017] [Indexed: 01/09/2023]
Abstract
To clarify the organization of motor representations in posterior parietal cortex, we test how three motor variables (body side, body part, cognitive strategy) are coded in the human anterior intraparietal cortex. All tested movements were encoded, arguing against strict anatomical segregation of effectors. Single units coded for diverse conjunctions of variables, with different dimensions anatomically overlapping. Consistent with recent studies, neurons encoding body parts exhibited mixed selectivity. This mixed selectivity resulted in largely orthogonal coding of body parts, which "functionally segregate" the effector responses despite the high degree of anatomical overlap. Body side and strategy were not coded in a mixed manner as effector determined their organization. Mixed coding of some variables over others, what we term "partially mixed coding," argues that the type of functional encoding depends on the compared dimensions. This structure is advantageous for neuroprosthetics, allowing a single array to decode movements of a large extent of the body.
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Affiliation(s)
- Carey Y Zhang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Tyson Aflalo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Boris Revechkis
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Emily R Rosario
- Casa Colina Hospital and Centers for Healthcare, Pomona, CA 91767, USA
| | - Debra Ouellette
- Casa Colina Hospital and Centers for Healthcare, Pomona, CA 91767, USA
| | - Nader Pouratian
- Department of Neurosurgery, Interdepartmental Program in Neuroscience, and Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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Aflalo T, Kellis S, Klaes C, Lee B, Shi Y, Pejsa K, Shanfield K, Hayes-Jackson S, Aisen M, Heck C, Liu C, Andersen RA. Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 2015; 348:906-10. [PMID: 25999506 DOI: 10.1126/science.aaa5417] [Citation(s) in RCA: 390] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.
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Affiliation(s)
- Tyson Aflalo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Spencer Kellis
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Christian Klaes
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Brian Lee
- USC Neurorestoration Center and the Departments of Neurosurgery and Neurology, University of Southern California, Los Angeles, CA 90033, USA
| | - Ying Shi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Kelsie Pejsa
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | | | - Mindy Aisen
- Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, USA
| | - Christi Heck
- USC Neurorestoration Center and the Departments of Neurosurgery and Neurology, University of Southern California, Los Angeles, CA 90033, USA
| | - Charles Liu
- USC Neurorestoration Center and the Departments of Neurosurgery and Neurology, University of Southern California, Los Angeles, CA 90033, USA
| | - Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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Abstract
Brain-machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain-machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics.
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Affiliation(s)
- Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Mail Code 216-76, Pasadena, CA, 91125-7600, USA.
| | - Spencer Kellis
- Division of Biology and Biological Engineering, California Institute of Technology, Mail Code 216-76, Pasadena, CA, 91125-7600, USA
| | - Christian Klaes
- Division of Biology and Biological Engineering, California Institute of Technology, Mail Code 216-76, Pasadena, CA, 91125-7600, USA
| | - Tyson Aflalo
- Division of Biology and Biological Engineering, California Institute of Technology, Mail Code 216-76, Pasadena, CA, 91125-7600, USA
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