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Kang JU, Mooshagian E, Snyder LH. Functional organization of posterior parietal cortex circuitry based on inferred information flow. Cell Rep 2024; 43:114028. [PMID: 38581681 PMCID: PMC11090617 DOI: 10.1016/j.celrep.2024.114028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 02/09/2024] [Accepted: 03/15/2024] [Indexed: 04/08/2024] Open
Abstract
Many studies infer the role of neurons by asking what information can be decoded from their activity or by observing the consequences of perturbing their activity. An alternative approach is to consider information flow between neurons. We applied this approach to the parietal reach region (PRR) and the lateral intraparietal area (LIP) in posterior parietal cortex. Two complementary methods imply that across a range of reaching tasks, information flows primarily from PRR to LIP. This indicates that during a coordinated reach task, LIP has minimal influence on PRR and rules out the idea that LIP forms a general purpose spatial processing hub for action and cognition. Instead, we conclude that PRR and LIP operate in parallel to plan arm and eye movements, respectively, with asymmetric interactions that likely support eye-hand coordination. Similar methods can be applied to other areas to infer their functional relationships based on inferred information flow.
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Affiliation(s)
- Jung Uk Kang
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Eric Mooshagian
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lawrence H Snyder
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
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2
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Lai D, Wan Z, Lin J, Pan L, Ren F, Zhu J, Zhang J, Wang Y, Hao Y, Xu K. Neuronal representation of bimanual arm motor imagery in the motor cortex of a tetraplegia human, a pilot study. Front Neurosci 2023; 17:1133928. [PMID: 36937679 PMCID: PMC10014804 DOI: 10.3389/fnins.2023.1133928] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/13/2023] [Indexed: 03/05/2023] Open
Abstract
Introduction How the human brain coordinates bimanual movements is not well-established. Methods Here, we recorded neural signals from a paralyzed individual's left motor cortex during both unimanual and bimanual motor imagery tasks and quantified the representational interaction between arms by analyzing the tuning parameters of each neuron. Results We found a similar proportion of neurons preferring each arm during unimanual movements, however, when switching to bimanual movements, the proportion of contralateral preference increased to 71.8%, indicating contralateral lateralization. We also observed a decorrelation process for each arm's representation across the unimanual and bimanual tasks. We further confined that these changes in bilateral relationships are mainly caused by the alteration of tuning parameters, such as the increased bilateral preferred direction (PD) shifts and the significant suppression in bilateral modulation depths (MDs), especially the ipsilateral side. Discussion These results contribute to the knowledge of bimanual coordination and thus the design of cutting-edge bimanual brain-computer interfaces.
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Affiliation(s)
- Dongrong Lai
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Zijun Wan
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Jiafan Lin
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Li Pan
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Feixiao Ren
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Junming Zhu
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
| | - Jianmin Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
| | - Yueming Wang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
| | - Yaoyao Hao
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
- *Correspondence: Yaoyao Hao,
| | - Kedi Xu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
- Kedi Xu,
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3
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Blohm G, Cheyne DO, Crawford JD. Parietofrontal oscillations show hand-specific interactions with top-down movement plans. J Neurophysiol 2022; 128:1518-1533. [PMID: 36321728 DOI: 10.1152/jn.00240.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
To generate a hand-specific reach plan, the brain must integrate hand-specific signals with the desired movement strategy. Although various neurophysiology/imaging studies have investigated hand-target interactions in simple reach-to-target tasks, the whole brain timing and distribution of this process remain unclear, especially for more complex, instruction-dependent motor strategies. Previously, we showed that a pro/anti pointing instruction influences magnetoencephalographic (MEG) signals in frontal cortex that then propagate recurrently through parietal cortex (Blohm G, Alikhanian H, Gaetz W, Goltz HC, DeSouza JF, Cheyne DO, Crawford JD. NeuroImage 197: 306-319, 2019). Here, we contrasted left versus right hand pointing in the same task to investigate 1) which cortical regions of interest show hand specificity and 2) which of those areas interact with the instructed motor plan. Eight bilateral areas, the parietooccipital junction (POJ), superior parietooccipital cortex (SPOC), supramarginal gyrus (SMG), medial/anterior interparietal sulcus (mIPS/aIPS), primary somatosensory/motor cortex (S1/M1), and dorsal premotor cortex (PMd), showed hand-specific changes in beta band power, with four of these (M1, S1, SMG, aIPS) showing robust activation before movement onset. M1, SMG, SPOC, and aIPS showed significant interactions between contralateral hand specificity and the instructed motor plan but not with bottom-up target signals. Separate hand/motor signals emerged relatively early and lasted through execution, whereas hand-motor interactions only occurred close to movement onset. Taken together with our previous results, these findings show that instruction-dependent motor plans emerge in frontal cortex and interact recurrently with hand-specific parietofrontal signals before movement onset to produce hand-specific motor behaviors.NEW & NOTEWORTHY The brain must generate different motor signals depending on which hand is used. The distribution and timing of hand use/instructed motor plan integration are not understood at the whole brain level. Using MEG we show that different action planning subnetworks code for hand usage and integrating hand use into a hand-specific motor plan. The timing indicates that frontal cortex first creates a general motor plan and then integrates hand specificity to produce a hand-specific motor plan.
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Affiliation(s)
- Gunnar Blohm
- Centre of Neuroscience Studies, Departments of Biomedical & Molecular Sciences, Mathematics & Statistics, and Psychology and School of Computing, Queen's University, Kingston, Ontario, Canada.,Centre for Vision Research, York University, Toronto, Ontario, Canada.,Canadian Action and Perception Network (CAPnet), Montreal, Quebec, Canada.,Vision: Science to Applications (VISTA) program, Departments of Psychology, Biology, and Kinesiology and Health Sciences and Neuroscience Graduate Diploma Program, York University, Toronto, Ontario, Canada
| | - Douglas O Cheyne
- Program in Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - J Douglas Crawford
- Centre for Vision Research, York University, Toronto, Ontario, Canada.,Canadian Action and Perception Network (CAPnet), Montreal, Quebec, Canada.,Vision: Science to Applications (VISTA) program, Departments of Psychology, Biology, and Kinesiology and Health Sciences and Neuroscience Graduate Diploma Program, York University, Toronto, Ontario, Canada
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4
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Goldring AB, Cooke DF, Pineda CR, Recanzone GH, Krubitzer LA. Functional characterization of the fronto-parietal reaching and grasping network: reversible deactivation of M1 and areas 2, 5, and 7b in awake behaving monkeys. J Neurophysiol 2022; 127:1363-1387. [PMID: 35417261 PMCID: PMC9109808 DOI: 10.1152/jn.00279.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 04/04/2022] [Accepted: 04/04/2022] [Indexed: 11/22/2022] Open
Abstract
In the present investigation, we examined the role of different cortical fields in the fronto-parietal reaching and grasping network in awake, behaving macaque monkeys. This network is greatly expanded in primates compared to other mammals and coevolved with glabrous hands with opposable thumbs and the extraordinary dexterous behaviors employed by a number of primates, including humans. To examine this, we reversibly deactivated the primary motor area (M1), anterior parietal area 2, and posterior parietal areas 5L and 7b individually while monkeys were performing two types of reaching and grasping tasks. Reversible deactivation was accomplished with small microfluidic thermal regulators abutting specifically targeted cortical areas. Placement of these devices in the different cortical fields was confirmed post hoc in histologically processed tissue. Our results indicate that the different areas examined form a complex network of motor control that is overlapping. However, several consistent themes emerged that suggest the independent roles that motor cortex, area 2, area 7b, and area 5L play in the motor planning and execution of reaching and grasping movements. Area 5L is involved in the early stages and area 7b the later stages of a reaching and grasping movement, motor cortex is involved in all aspects of the execution of the movement, and area 2 provides proprioceptive feedback throughout the movement. We discuss our results in the context of previous studies that explored the fronto-parietal network, the overlapping (but also independent) functions of different nodes of this network, and the rapid compensatory plasticity of this network.NEW & NOTEWORTHY This is the first study to directly compare the results of cooling different portions of the fronto-parietal reaching and grasping network (motor cortex, anterior and posterior parietal cortex) in the same animals and the first to employ a complex, bimanual reaching and grasping task that is ethologically relevant. Whereas cooling area 7b or area 5L evoked deficits at distinct task phases, cooling M1 evoked a general set of deficits and cooling area 2 evoked proprioceptive deficits.
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Affiliation(s)
- Adam B Goldring
- Department of Psychology, University of California, Davis, California
- Center for Neuroscience, University of California, Davis, California
| | - Dylan F Cooke
- Center for Neuroscience, University of California, Davis, California
- Department of Biomedical Physiology and Kinesiology (BPK), Simon Fraser University, Burnaby, British Columbia, Canada
| | - Carlos R Pineda
- Department of Psychology, University of California, Davis, California
- Center for Neuroscience, University of California, Davis, California
| | - Gregg H Recanzone
- Center for Neuroscience, University of California, Davis, California
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, California
| | - Leah A Krubitzer
- Department of Psychology, University of California, Davis, California
- Center for Neuroscience, University of California, Davis, California
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5
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Choi H, Lim S, Min K, Ahn KH, Lee KM, Jang DP. Non-human primate epidural ECoG analysis using explainable deep learning technology. J Neural Eng 2021; 18. [PMID: 34695809 DOI: 10.1088/1741-2552/ac3314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 10/25/2021] [Indexed: 11/12/2022]
Abstract
Objective.With the development in the field of neural networks,explainable AI(XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results.Approach. We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment.Main results. The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements.Significance.As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.
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Affiliation(s)
- Hoseok Choi
- Department of Neurology, University of California, San Francisco, CA, United States of America.,Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Seokbeen Lim
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Kyeongran Min
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea.,Samsung SDS Artificial Intelligence Research Center, Seoul, Republic of Korea
| | - Kyoung-Ha Ahn
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kyoung-Min Lee
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dong Pyo Jang
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
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6
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O'Connor DH, Krubitzer L, Bensmaia S. Of mice and monkeys: Somatosensory processing in two prominent animal models. Prog Neurobiol 2021; 201:102008. [PMID: 33587956 PMCID: PMC8096687 DOI: 10.1016/j.pneurobio.2021.102008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/26/2020] [Accepted: 02/07/2021] [Indexed: 11/20/2022]
Abstract
Our understanding of the neural basis of somatosensation is based largely on studies of the whisker system of mice and rats and the hands of macaque monkeys. Results across these animal models are often interpreted as providing direct insight into human somatosensation. Work on these systems has proceeded in parallel, capitalizing on the strengths of each model, but has rarely been considered as a whole. This lack of integration promotes a piecemeal understanding of somatosensation. Here, we examine the functions and morphologies of whiskers of mice and rats, the hands of macaque monkeys, and the somatosensory neuraxes of these three species. We then discuss how somatosensory information is encoded in their respective nervous systems, highlighting similarities and differences. We reflect on the limitations of these models of human somatosensation and consider key gaps in our understanding of the neural basis of somatosensation.
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Affiliation(s)
- Daniel H O'Connor
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, United States; Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, United States
| | - Leah Krubitzer
- Department of Psychology and Center for Neuroscience, University of California at Davis, United States
| | - Sliman Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, United States; Committee on Computational Neuroscience, University of Chicago, United States; Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, United States.
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7
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Local field potentials in the parietal reach region reveal mechanisms of bimanual coordination. Nat Commun 2021; 12:2514. [PMID: 33947840 PMCID: PMC8096826 DOI: 10.1038/s41467-021-22701-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 03/25/2021] [Indexed: 02/02/2023] Open
Abstract
Primates use their arms in complex ways that frequently require coordination between the two arms. Yet the planning of bimanual movements has not been well-studied. We recorded spikes and local field potentials (LFP) from the parietal reach region (PRR) in both hemispheres simultaneously while monkeys planned and executed unimanual and bimanual reaches. From analyses of interhemispheric LFP-LFP and spike-LFP coherence, we found that task-specific information is shared across hemispheres in a frequency-specific manner. This shared information could arise from common input or from direct communication. The population average unit activity in PRR, representing PRR output, encodes only planned contralateral arm movements while beta-band LFP power, a putative PRR input, reflects the pattern of planned bimanual movement. A parsimonious interpretation of these data is that PRR integrates information about the movement of the left and right limbs, perhaps in service of bimanual coordination.
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8
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Peterson SM, Singh SH, Wang NXR, Rao RPN, Brunton BW. Behavioral and Neural Variability of Naturalistic Arm Movements. eNeuro 2021; 8:ENEURO.0007-21.2021. [PMID: 34031100 PMCID: PMC8225404 DOI: 10.1523/eneuro.0007-21.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/27/2021] [Accepted: 05/04/2021] [Indexed: 11/21/2022] Open
Abstract
Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed "in the wild." Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled interevent variability using 10 behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior.
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Affiliation(s)
- Steven M Peterson
- Department of Biology, University of Washington, Seattle, Washington 98195
- eScience Institute, University of Washington, Seattle, Washington 98195
| | - Satpreet H Singh
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195
| | - Nancy X R Wang
- IBM Research, San Jose, California 95120
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195
| | - Rajesh P N Rao
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195
- Center for Neurotechnology, University of Washington, Seattle, Washington 98195
| | - Bingni W Brunton
- Department of Biology, University of Washington, Seattle, Washington 98195
- eScience Institute, University of Washington, Seattle, Washington 98195
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9
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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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10
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Berger M, Agha NS, Gail A. Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex. eLife 2020; 9:e51322. [PMID: 32364495 PMCID: PMC7228770 DOI: 10.7554/elife.51322] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 05/02/2020] [Indexed: 11/25/2022] Open
Abstract
System neuroscience of motor cognition regarding the space beyond immediate reach mandates free, yet experimentally controlled movements. We present an experimental environment (Reach Cage) and a versatile visuo-haptic interaction system (MaCaQuE) for investigating goal-directed whole-body movements of unrestrained monkeys. Two rhesus monkeys conducted instructed walk-and-reach movements towards targets flexibly positioned in the cage. We tracked 3D multi-joint arm and head movements using markerless motion capture. Movements show small trial-to-trial variability despite being unrestrained. We wirelessly recorded 192 broad-band neural signals from three cortical sensorimotor areas simultaneously. Single unit activity is selective for different reach and walk-and-reach movements. Walk-and-reach targets could be decoded from premotor and parietal but not motor cortical activity during movement planning. The Reach Cage allows systems-level sensorimotor neuroscience studies with full-body movements in a configurable 3D spatial setting with unrestrained monkeys. We conclude that the primate frontoparietal network encodes reach goals beyond immediate reach during movement planning.
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Affiliation(s)
- Michael Berger
- Cognitive Neuroscience Laboratory, German Primate Center – Leibniz-Institute for Primate ResearchGoettingenGermany
- Faculty of Biology and Psychology, University of GoettingenGoettingenGermany
| | - Naubahar Shahryar Agha
- Cognitive Neuroscience Laboratory, German Primate Center – Leibniz-Institute for Primate ResearchGoettingenGermany
| | - Alexander Gail
- Cognitive Neuroscience Laboratory, German Primate Center – Leibniz-Institute for Primate ResearchGoettingenGermany
- Faculty of Biology and Psychology, University of GoettingenGoettingenGermany
- Leibniz-ScienceCampus Primate CognitionGoettingenGermany
- Bernstein Center for Computational NeuroscienceGoettingenGermany
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11
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Spatial eye-hand coordination during bimanual reaching is not systematically coded in either LIP or PRR. Proc Natl Acad Sci U S A 2018; 115:E3817-E3826. [PMID: 29610356 PMCID: PMC5910835 DOI: 10.1073/pnas.1718267115] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
When we reach for something, we also look at it. If we reach for two objects at once, one with each hand, we look first at one and then the other. It is not known which brain areas underlie this coordination. We studied two parietal areas known to be involved in eye and arm movements. Neither area was sensitive to the order in which the targets were looked at. This implies that coordinated saccades are driven by downstream areas and not by the parietal cortex as is commonly assumed. We often orient to where we are about to reach. Spatial and temporal correlations in eye and arm movements may depend on the posterior parietal cortex (PPC). Spatial representations of saccade and reach goals preferentially activate cells in the lateral intraparietal area (LIP) and the parietal reach region (PRR), respectively. With unimanual reaches, eye and arm movement patterns are highly stereotyped. This makes it difficult to study the neural circuits involved in coordination. Here, we employ bimanual reaching to two different targets. Animals naturally make a saccade first to one target and then the other, resulting in different patterns of limb–gaze coordination on different trials. Remarkably, neither LIP nor PRR cells code which target the eyes will move to first. These results suggest that the parietal cortex plays at best only a permissive role in some aspects of eye–hand coordination and makes the role of LIP in saccade generation unclear.
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