1
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Chang YT, Finkel EA, Xu D, O'Connor DH. Rule-based modulation of a sensorimotor transformation across cortical areas. eLife 2024; 12:RP92620. [PMID: 38842277 PMCID: PMC11156468 DOI: 10.7554/elife.92620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024] Open
Abstract
Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes and uses rule information to guide behavior. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task where they switched between two rules: licking in response to tactile stimuli while rejecting visual stimuli, or vice versa. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, single-neuron activity distinguished between the two rules both prior to and in response to the tactile stimulus. We hypothesized that neural populations in these areas would show rule-dependent preparatory states, which would shape the subsequent sensory processing and behavior. This hypothesis was supported for the motor cortical areas (MM and ALM) by findings that (1) the current task rule could be decoded from pre-stimulus population activity; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states impaired task performance. Our findings indicate that flexible action selection in response to sensory input can occur via configuration of preparatory states in the motor cortex.
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Affiliation(s)
- Yi-Ting Chang
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
| | - Eric A Finkel
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Duo Xu
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
| | - Daniel H O'Connor
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
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2
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Vickers ED, McCormick DA. Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice. eLife 2024; 13:RP94167. [PMID: 38808733 PMCID: PMC11136495 DOI: 10.7554/elife.94167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024] Open
Abstract
The flow of neural activity across the neocortex during active sensory discrimination is constrained by task-specific cognitive demands, movements, and internal states. During behavior, the brain appears to sample from a broad repertoire of activation motifs. Understanding how these patterns of local and global activity are selected in relation to both spontaneous and task-dependent behavior requires in-depth study of densely sampled activity at single neuron resolution across large regions of cortex. In a significant advance toward this goal, we developed procedures to record mesoscale 2-photon Ca2+ imaging data from two novel in vivo preparations that, between them, allow for simultaneous access to nearly all 0f the mouse dorsal and lateral neocortex. As a proof of principle, we aligned neural activity with both behavioral primitives and high-level motifs to reveal the existence of large populations of neurons that coordinated their activity across cortical areas with spontaneous changes in movement and/or arousal. The methods we detail here facilitate the identification and exploration of widespread, spatially heterogeneous neural ensembles whose activity is related to diverse aspects of behavior.
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Affiliation(s)
- Evan D Vickers
- Institute of Neuroscience, University of OregonEugeneUnited States
| | - David A McCormick
- Institute of Neuroscience, University of OregonEugeneUnited States
- Department of Biology, University of OregonEugeneUnited States
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3
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Shi L, Fu X, Gui S, Wan T, Zhuo J, Lu J, Li P. Global spatiotemporal synchronizing structures of spontaneous neural activities in different cell types. Nat Commun 2024; 15:2884. [PMID: 38570488 PMCID: PMC10991327 DOI: 10.1038/s41467-024-46975-5] [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: 08/11/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Increasing evidence has revealed the large-scale nonstationary synchronizations as traveling waves in spontaneous neural activity. However, the interplay of various cell types in fine-tuning these spatiotemporal patters remains unclear. Here, we performed comprehensive exploration of spatiotemporal synchronizing structures across different cell types, states (awake, anesthesia, motion) and developmental axis in male mice. We found traveling waves in glutamatergic neurons exhibited greater variety than those in GABAergic neurons. Moreover, the synchronizing structures of GABAergic neurons converged toward those of glutamatergic neurons during development, but the evolution of waves exhibited varying timelines for different sub-type interneurons. Functional connectivity arises from both standing and traveling waves, and negative connections can be elucidated by the spatial propagation of waves. In addition, some traveling waves were correlated with the spatial distribution of gene expression. Our findings offer further insights into the neural underpinnings of traveling waves, functional connectivity, and resting-state networks, with cell-type specificity and developmental perspectives.
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Affiliation(s)
- Liang Shi
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China
| | - Xiaoxi Fu
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China
| | - Shen Gui
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China
| | - Tong Wan
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572025, China
| | - Junjie Zhuo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572025, China
| | - Jinling Lu
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China.
| | - Pengcheng Li
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215100, China.
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572025, China.
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4
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Chang YT, Finkel EA, Xu D, O'Connor DH. Rule-based modulation of a sensorimotor transformation across cortical areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.21.554194. [PMID: 37662301 PMCID: PMC10473613 DOI: 10.1101/2023.08.21.554194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes rule information and uses this information to guide behavioral responses to sensory stimuli. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task in which they switched between two rules in different blocks of trials: licking in response to tactile stimuli applied to a whisker while rejecting visual stimuli, or licking to visual stimuli while rejecting the tactile stimuli. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, the single-trial activity of individual neurons distinguished between the two rules both prior to and in response to the tactile stimulus. Variable rule-dependent responses to identical stimuli could in principle occur via appropriate configuration of pre-stimulus preparatory states of a neural population, which would shape the subsequent response. We hypothesized that neural populations in S1, S2, MM and ALM would show preparatory activity states that were set in a rule-dependent manner to cause processing of sensory information according to the current rule. This hypothesis was supported for the motor cortical areas by findings that (1) the current task rule could be decoded from pre-stimulus population activity in ALM and MM; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states within ALM and MM impaired task performance. Our findings indicate that flexible selection of an appropriate action in response to a sensory input can occur via configuration of preparatory states in the motor cortex.
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5
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Taub DG, Jiang Q, Pietrafesa F, Su J, Carroll A, Greene C, Blanchard MR, Jain A, El-Rifai M, Callen A, Yager K, Chung C, He Z, Chen C, Woolf CJ. The secondary somatosensory cortex gates mechanical and heat sensitivity. Nat Commun 2024; 15:1289. [PMID: 38346995 PMCID: PMC10861531 DOI: 10.1038/s41467-024-45729-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 02/01/2024] [Indexed: 02/15/2024] Open
Abstract
The cerebral cortex is vital for the processing and perception of sensory stimuli. In the somatosensory axis, information is received primarily by two distinct regions, the primary (S1) and secondary (S2) somatosensory cortices. Top-down circuits stemming from S1 can modulate mechanical and cooling but not heat stimuli such that circuit inhibition causes blunted perception. This suggests that responsiveness to particular somatosensory stimuli occurs in a modality specific fashion and we sought to determine additional cortical substrates. In this work, we identify in a mouse model that inhibition of S2 output increases mechanical and heat, but not cooling sensitivity, in contrast to S1. Combining 2-photon anatomical reconstruction with chemogenetic inhibition of specific S2 circuits, we discover that S2 projections to the secondary motor cortex (M2) govern mechanical and heat sensitivity without affecting motor performance or anxiety. Taken together, we show that S2 is an essential cortical structure that governs mechanical and heat sensitivity.
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Affiliation(s)
- Daniel G Taub
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Qiufen Jiang
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Francesca Pietrafesa
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Junfeng Su
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Aloe Carroll
- College of Sciences, Northeastern University, Boston, MA, USA
| | - Caitlin Greene
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Aakanksha Jain
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Mahmoud El-Rifai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Alexis Callen
- Morrissey College of Arts and Sciences, Boston College, Chestnut Hill, MA, USA
| | - Katherine Yager
- Morrissey College of Arts and Sciences, Boston College, Chestnut Hill, MA, USA
| | - Clara Chung
- Department of Neuroscience, Boston University, Boston, MA, USA
| | - Zhigang He
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Chinfei Chen
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Clifford J Woolf
- F. M. Kirby Neurobiology Center and Department of Neurology, Boston Children's Hospital, Boston, MA, USA.
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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6
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Vickers ED, McCormick DA. Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.19.563159. [PMID: 37961229 PMCID: PMC10634705 DOI: 10.1101/2023.10.19.563159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The flow of neural activity across the neocortex during active sensory discrimination is constrained by task-specific cognitive demands, movements, and internal states. During behavior, the brain appears to sample from a broad repertoire of activation motifs. Understanding how these patterns of local and global activity are selected in relation to both spontaneous and task-dependent behavior requires in-depth study of densely sampled activity at single neuron resolution across large regions of cortex. In a significant advance toward this goal, we developed procedures to record mesoscale 2-photon Ca2+ imaging data from two novel in vivo preparations that, between them, allow simultaneous access to nearly all of the mouse dorsal and lateral neocortex. As a proof of principle, we aligned neural activity with both behavioral primitives and high-level motifs to reveal the existence of large populations of neurons that coordinated their activity across cortical areas with spontaneous changes in movement and/or arousal. The methods we detail here facilitate the identification and exploration of widespread, spatially heterogeneous neural ensembles whose activity is related to diverse aspects of behavior.
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Affiliation(s)
- Evan D Vickers
- Institute of Neuroscience, University of Oregon, Eugene, OR
| | - David A McCormick
- Institute of Neuroscience, University of Oregon, Eugene, OR
- Department of Biology
- Institute of Neuroscience
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7
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Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S. Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior. Cell Rep 2024; 43:113618. [PMID: 38150365 DOI: 10.1016/j.celrep.2023.113618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/27/2023] [Accepted: 12/08/2023] [Indexed: 12/29/2023] Open
Abstract
Goal-directed behaviors involve coordinated activity in many cortical areas, but whether the encoding of task variables is distributed across areas or is more specifically represented in distinct areas remains unclear. Here, we compared representations of sensory, motor, and decision information in the whisker primary somatosensory cortex, medial prefrontal cortex, and tongue-jaw primary motor cortex in mice trained to lick in response to a whisker stimulus with mice that were not taught this association. Irrespective of learning, properties of the sensory stimulus were best encoded in the sensory cortex, whereas fine movement kinematics were best represented in the motor cortex. However, movement initiation and the decision to lick in response to the whisker stimulus were represented in all three areas, with decision neurons in the medial prefrontal cortex being more selective, showing minimal sensory responses in miss trials and motor responses during spontaneous licks. Our results reconcile previous studies indicating highly specific vs. highly distributed sensorimotor processing.
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Affiliation(s)
- Anastasiia Oryshchuk
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Christos Sourmpis
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Julie Weverbergh
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Reza Asri
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Vahid Esmaeili
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Alireza Modirshanechi
- School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Wulfram Gerstner
- School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Carl C H Petersen
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Sylvain Crochet
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Institut National de la Santé et de la Recherche Médicale (INSERM), 6900 Lyon, France.
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8
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Ester E, Weese R. Temporally Dissociable Mechanisms of Spatial, Feature, and Motor Selection during Working Memory-guided Behavior. J Cogn Neurosci 2023; 35:2014-2027. [PMID: 37788302 DOI: 10.1162/jocn_a_02061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Working memory (WM) is a capacity- and duration-limited system that forms a temporal bridge between fleeting sensory phenomena and possible actions. But how are the contents of WM used to guide behavior? A recent high-profile study reported evidence for simultaneous access to WM content and linked motor plans during WM-guided behavior, challenging serial models where task-relevant WM content is first selected and then mapped on to a task-relevant motor response. However, the task used in that study was not optimized to distinguish the selection of spatial versus nonspatial visual information stored in memory, nor to distinguish whether or how the chronometry of selecting nonspatial visual information stored in memory might differ from the selection of linked motor plans. Here, we revisited the chronometry of spatial, feature, and motor selection during WM-guided behavior using a task optimized to disentangle these processes. Concurrent EEG and eye position recordings revealed clear evidence for temporally dissociable spatial, feature, and motor selection during this task. Thus, our data reveal the existence of multiple WM selection mechanisms that belie conceptualizations of WM-guided behavior based on purely serial or parallel visuomotor processing.
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9
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Chong HR, Ranjbar-Slamloo Y, Ho MZH, Ouyang X, Kamigaki T. Functional alterations of the prefrontal circuit underlying cognitive aging in mice. Nat Commun 2023; 14:7254. [PMID: 37945561 PMCID: PMC10636129 DOI: 10.1038/s41467-023-43142-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Executive function is susceptible to aging. How aging impacts the circuit-level computations underlying executive function remains unclear. Using calcium imaging and optogenetic manipulation during memory-guided behavior, we show that working-memory coding and the relevant recurrent connectivity in the mouse medial prefrontal cortex (mPFC) are altered as early as middle age. Population activity in the young adult mPFC exhibits dissociable yet overlapping patterns between tactile and auditory modalities, enabling crossmodal memory coding concurrent with modality-dependent coding. In middle age, however, crossmodal coding remarkably diminishes while modality-dependent coding persists, and both types of coding decay in advanced age. Resting-state functional connectivity, especially among memory-coding neurons, decreases already in middle age, suggesting deteriorated recurrent circuits for memory maintenance. Optogenetic inactivation reveals that the middle-aged mPFC exhibits heightened vulnerability to perturbations. These findings elucidate functional alterations of the prefrontal circuit that unfold in middle age and deteriorate further as a hallmark of cognitive aging.
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Affiliation(s)
- Huee Ru Chong
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Yadollah Ranjbar-Slamloo
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Malcolm Zheng Hao Ho
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore, 308232, Singapore
| | - Xuan Ouyang
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Tsukasa Kamigaki
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
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10
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Lande AS, Garvert AC, Ebbesen NC, Jordbræk SV, Vervaeke K. Representations of tactile object location in the retrosplenial cortex. Curr Biol 2023; 33:4599-4610.e7. [PMID: 37774708 DOI: 10.1016/j.cub.2023.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 07/23/2023] [Accepted: 09/06/2023] [Indexed: 10/01/2023]
Abstract
How animals use tactile sensation to detect important objects and remember their location in a world-based coordinate system is unclear. Here, we hypothesized that the retrosplenial cortex (RSC), a key network for contextual memory and spatial navigation, represents the location of objects based on tactile sensation. We studied mice palpating objects with their whiskers while navigating in a tactile virtual reality in darkness. Using two-photon Ca2+ imaging, we discovered that a population of neurons in the agranular RSC signal the location of objects. Responses to objects do not simply reflect the sensory stimulus. Instead, they are highly position, task, and context dependent and often predict the upcoming object before it is within reach. In addition, a large fraction of neurons encoding object location maintain a memory trace of the object's location. These data show that the RSC encodes the location and arrangement of tactile objects in a spatial reference frame.
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Affiliation(s)
- Andreas Sigstad Lande
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Anna Christina Garvert
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Nora Cecilie Ebbesen
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Sondre Valentin Jordbræk
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Koen Vervaeke
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway.
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11
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Marmor O, Pollak Y, Doron C, Helmchen F, Gilad A. History information emerges in the cortex during learning. eLife 2023; 12:e83702. [PMID: 37921842 PMCID: PMC10624423 DOI: 10.7554/elife.83702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 10/23/2023] [Indexed: 11/04/2023] Open
Abstract
We learn from our experience but the underlying neuronal mechanisms incorporating past information to facilitate learning is relatively unknown. Specifically, which cortical areas encode history-related information and how is this information modulated across learning? To study the relationship between history and learning, we continuously imaged cortex-wide calcium dynamics as mice learn to use their whiskers to discriminate between two different textures. We mainly focused on comparing the same trial type with different trial history, that is, a different preceding trial. We found trial history information in barrel cortex (BC) during stimulus presentation. Importantly, trial history in BC emerged only as the mouse learned the task. Next, we also found learning-dependent trial history information in rostrolateral (RL) association cortex that emerges before stimulus presentation, preceding activity in BC. Trial history was also encoded in other cortical areas and was not related to differences in body movements. Interestingly, a binary classifier could discriminate trial history at the single trial level just as well as current information both in BC and RL. These findings suggest that past experience emerges in the cortex around the time of learning, starting from higher-order association area RL and propagating down (i.e., top-down projection) to lower-order BC where it can be integrated with incoming sensory information. This integration between the past and present may facilitate learning.
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Affiliation(s)
- Odeya Marmor
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
| | - Yael Pollak
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
| | - Chen Doron
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
| | - Fritjof Helmchen
- Brain Research Institute, University of ZurichZurichSwitzerland
- Neuroscience Center ZurichZurichSwitzerland
| | - Ariel Gilad
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
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12
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Chia XW, Tan JK, Ang LF, Kamigaki T, Makino H. Emergence of cortical network motifs for short-term memory during learning. Nat Commun 2023; 14:6869. [PMID: 37898638 PMCID: PMC10613236 DOI: 10.1038/s41467-023-42609-4] [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: 10/28/2022] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
Learning of adaptive behaviors requires the refinement of coordinated activity across multiple brain regions. However, how neural communications develop during learning remains poorly understood. Here, using two-photon calcium imaging, we simultaneously recorded the activity of layer 2/3 excitatory neurons in eight regions of the mouse dorsal cortex during learning of a delayed-response task. Across learning, while global functional connectivity became sparser, there emerged a subnetwork comprising of neurons in the anterior lateral motor cortex (ALM) and posterior parietal cortex (PPC). Neurons in this subnetwork shared a similar choice code during action preparation and formed recurrent functional connectivity across learning. Suppression of PPC activity disrupted choice selectivity in ALM and impaired task performance. Recurrent neural networks reconstructed from ALM activity revealed that PPC-ALM interactions rendered choice-related attractor dynamics more stable. Thus, learning constructs cortical network motifs by recruiting specific inter-areal communication channels to promote efficient and robust sensorimotor transformation.
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Affiliation(s)
- Xin Wei Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Jian Kwang Tan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Lee Fang Ang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Tsukasa Kamigaki
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Hiroshi Makino
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
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13
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Maldonado PE, Concha-Miranda M, Schwalm M. Autogenous cerebral processes: an invitation to look at the brain from inside out. Front Neural Circuits 2023; 17:1253609. [PMID: 37941893 PMCID: PMC10629273 DOI: 10.3389/fncir.2023.1253609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/26/2023] [Indexed: 11/10/2023] Open
Abstract
While external stimulation can reliably trigger neuronal activity, cerebral processes can operate independently from the environment. In this study, we conceptualize autogenous cerebral processes (ACPs) as intrinsic operations of the brain that exist on multiple scales and can influence or shape stimulus responses, behavior, homeostasis, and the physiological state of an organism. We further propose that the field should consider exploring to what extent perception, arousal, behavior, or movement, as well as other cognitive functions previously investigated mainly regarding their stimulus-response dynamics, are ACP-driven.
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Affiliation(s)
- Pedro E. Maldonado
- Departamento de Neurociencia, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Biomedical Neuroscience Institute (BNI), Faculty of Medicine, University of Chile, Santiago, Chile
- National Center for Artificial Intelligence (CENIA), Santiago, Chile
| | - Miguel Concha-Miranda
- Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Miriam Schwalm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
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14
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Roland PE. How far neuroscience is from understanding brains. Front Syst Neurosci 2023; 17:1147896. [PMID: 37867627 PMCID: PMC10585277 DOI: 10.3389/fnsys.2023.1147896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/31/2023] [Indexed: 10/24/2023] Open
Abstract
The cellular biology of brains is relatively well-understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about the brain mechanisms, I attempt here to identify major obstacles to progress in neuroscientific understanding of brains and central nervous systems. Most of the obstacles to our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task-related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the independent variable are insufficient to account for central nervous system activities. Spatial dynamics may be a practical solution. The general hypothesis that measurements of changes in fundamental brain variables, action potentials, transmitter releases, post-synaptic transmembrane currents, etc., propagating in central nervous systems reveal how they work, carries no additional assumptions. Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Furthermore, the facts that pooling and averaging of data destroy their underlying dynamics imply that single-trial designs and statistics are necessary.
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Affiliation(s)
- Per E. Roland
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
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15
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Liu J, Liu D, Pu X, Zou K, Xie T, Li Y, Yao H. The Secondary Motor Cortex-striatum Circuit Contributes to Suppressing Inappropriate Responses in Perceptual Decision Behavior. Neurosci Bull 2023; 39:1544-1560. [PMID: 37253985 PMCID: PMC10533474 DOI: 10.1007/s12264-023-01073-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 03/08/2023] [Indexed: 06/01/2023] Open
Abstract
The secondary motor cortex (M2) encodes choice-related information and plays an important role in cue-guided actions. M2 neurons innervate the dorsal striatum (DS), which also contributes to decision-making behavior, yet how M2 modulates signals in the DS to influence perceptual decision-making is unclear. Using mice performing a visual Go/No-Go task, we showed that inactivating M2 projections to the DS impaired performance by increasing the false alarm (FA) rate to the reward-irrelevant No-Go stimulus. The choice signal of M2 neurons correlated with behavioral performance, and the inactivation of M2 neurons projecting to the DS reduced the choice signal in the DS. By measuring and manipulating the responses of direct or indirect pathway striatal neurons defined by M2 inputs, we found that the indirect pathway neurons exhibited a shorter response latency to the No-Go stimulus, and inactivating their early responses increased the FA rate. These results demonstrate that the M2-to-DS pathway is crucial for suppressing inappropriate responses in perceptual decision behavior.
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Affiliation(s)
- Jing Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Dechen Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xiaotian Pu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kexin Zou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Taorong Xie
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yaping Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Haishan Yao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, 201210, China.
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16
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Greene AS, Horien C, Barson D, Scheinost D, Constable RT. Why is everyone talking about brain state? Trends Neurosci 2023; 46:508-524. [PMID: 37164869 PMCID: PMC10330476 DOI: 10.1016/j.tins.2023.04.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/17/2023] [Accepted: 04/07/2023] [Indexed: 05/12/2023]
Abstract
The rapid and coordinated propagation of neural activity across the brain provides the foundation for complex behavior and cognition. Technical advances across neuroscience subfields have advanced understanding of these dynamics, but points of convergence are often obscured by semantic differences, creating silos of subfield-specific findings. In this review we describe how a parsimonious conceptualization of brain state as the fundamental building block of whole-brain activity offers a common framework to relate findings across scales and species. We present examples of the diverse techniques commonly used to study brain states associated with physiology and higher-order cognitive processes, and discuss how integration across them will enable a more comprehensive and mechanistic characterization of the neural dynamics that are crucial to survival but are disrupted in disease.
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Affiliation(s)
- Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, USA; MD/PhD program, Yale School of Medicine, New Haven, CT 06520, USA.
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, USA; MD/PhD program, Yale School of Medicine, New Haven, CT 06520, USA.
| | - Daniel Barson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, USA; MD/PhD program, Yale School of Medicine, New Haven, CT 06520, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA.
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06520, USA; Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA; Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06520, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
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17
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Shahsavarani S, Thibodeaux DN, Xu W, Kim SH, Lodgher F, Nwokeabia C, Cambareri M, Yagielski AJ, Zhao HT, Handwerker DA, Gonzalez-Castillo J, Bandettini PA, Hillman EMC. Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity. Cell Rep 2023; 42:112527. [PMID: 37243588 PMCID: PMC10592480 DOI: 10.1016/j.celrep.2023.112527] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/14/2023] [Accepted: 05/01/2023] [Indexed: 05/29/2023] Open
Abstract
Although resting-state functional magnetic resonance imaging (fMRI) studies have observed dynamically changing brain-wide networks of correlated activity, fMRI's dependence on hemodynamic signals makes results challenging to interpret. Meanwhile, emerging techniques for real-time recording of large populations of neurons have revealed compelling fluctuations in neuronal activity across the brain that are obscured by traditional trial averaging. To reconcile these observations, we use wide-field optical mapping to simultaneously record pan-cortical neuronal and hemodynamic activity in awake, spontaneously behaving mice. Some components of observed neuronal activity clearly represent sensory and motor function. However, particularly during quiet rest, strongly fluctuating patterns of activity across diverse brain regions contribute greatly to interregional correlations. Dynamic changes in these correlations coincide with changes in arousal state. Simultaneously acquired hemodynamics depict similar brain-state-dependent correlation shifts. These results support a neural basis for dynamic resting-state fMRI, while highlighting the importance of brain-wide neuronal fluctuations in the study of brain state.
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Affiliation(s)
- Somayeh Shahsavarani
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA; Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - David N Thibodeaux
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Weihao Xu
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Sharon H Kim
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Fatema Lodgher
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Chinwendu Nwokeabia
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Morgan Cambareri
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Alexis J Yagielski
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Hanzhi T Zhao
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth M C Hillman
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
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18
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Mo C, McKinnon C, Sherman SM. A transthalamic pathway crucial for perception. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.30.533323. [PMID: 37034798 PMCID: PMC10081228 DOI: 10.1101/2023.03.30.533323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Perception arises from activity between cortical areas, first primary cortex and then higher order cortices. This communication is served in part by transthalamic (cortico-thalamo-cortical) pathways, which ubiquitously parallel direct corticocortical pathways, but their role in sensory processing has largely remained unexplored. Here, we show that the transthalamic pathway linking somatosensory cortices propagates task-relevant information required for correct sensory decisions. Using optogenetics, we specifically inhibited the pathway at its synapse in higher order somatosensory thalamus of mice performing a texture-based discrimination task. We concurrently monitored the cellular effects of inhibition in primary or secondary cortex using two-photon calcium imaging. Inhibition severely impaired performance despite intact direct corticocortical projections, thus challenging the purely corticocentric map of perception. Interestingly, the inhibition did not reduce overall cell responsiveness to texture stimulation in somatosensory cortex, but rather disrupted the texture selectivity of cells, a discriminability that develops over task learning. This discriminability was more disrupted in the secondary than primary somatosensory cortex, emphasizing the feedforward influence of the transthalamic route. Transthalamic pathways thus appear critical in delivering performance-relevant information to higher order cortex and are critical hierarchical pathways in perceptual decision-making.
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19
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Taub DG, Jiang Q, Pietrafesa F, Su J, Greene C, Blanchard MR, Jain A, El-Rifai M, Callen A, Yager K, Chung C, He Z, Chen C, Woolf CJ. The Secondary Somatosensory Cortex Gates Mechanical and Thermal Sensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.19.541449. [PMID: 37293011 PMCID: PMC10245795 DOI: 10.1101/2023.05.19.541449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The cerebral cortex is vital for the perception and processing of sensory stimuli. In the somatosensory axis, information is received by two distinct regions, the primary (S1) and secondary (S2) somatosensory cortices. Top-down circuits stemming from S1 can modulate mechanical and cooling but not heat stimuli such that circuit inhibition causes blunted mechanical and cooling perception. Using optogenetics and chemogenetics, we find that in contrast to S1, an inhibition of S2 output increases mechanical and heat, but not cooling sensitivity. Combining 2-photon anatomical reconstruction with chemogenetic inhibition of specific S2 circuits, we discover that S2 projections to the secondary motor cortex (M2) govern mechanical and thermal sensitivity without affecting motor or cognitive function. This suggests that while S2, like S1, encodes specific sensory information, that S2 operates through quite distinct neural substrates to modulate responsiveness to particular somatosensory stimuli and that somatosensory cortical encoding occurs in a largely parallel fashion.
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20
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Nakai N, Sato M, Yamashita O, Sekine Y, Fu X, Nakai J, Zalesky A, Takumi T. Virtual reality-based real-time imaging reveals abnormal cortical dynamics during behavioral transitions in a mouse model of autism. Cell Rep 2023; 42:112258. [PMID: 36990094 DOI: 10.1016/j.celrep.2023.112258] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/16/2023] [Accepted: 02/28/2023] [Indexed: 03/30/2023] Open
Abstract
Functional connectivity (FC) can provide insight into cortical circuit dysfunction in neuropsychiatric disorders. However, dynamic changes in FC related to locomotion with sensory feedback remain to be elucidated. To investigate FC dynamics in locomoting mice, we develop mesoscopic Ca2+ imaging with a virtual reality (VR) environment. We find rapid reorganization of cortical FC in response to changing behavioral states. By using machine learning classification, behavioral states are accurately decoded. We then use our VR-based imaging system to study cortical FC in a mouse model of autism and find that locomotion states are associated with altered FC dynamics. Furthermore, we identify FC patterns involving the motor area as the most distinguishing features of the autism mice from wild-type mice during behavioral transitions, which might correlate with motor clumsiness in individuals with autism. Our VR-based real-time imaging system provides crucial information to understand FC dynamics linked to behavioral abnormality of neuropsychiatric disorders.
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Affiliation(s)
- Nobuhiro Nakai
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, Kobe 650-0017, Japan
| | - Masaaki Sato
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Neuropharmacology, Hokkaido University Graduate School of Medicine, Kita, Sapporo 060-8638, Japan.
| | - Okito Yamashita
- RIKEN Center for Advanced Intelligence Project, Chuo, Tokyo 103-0027, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, Seika, Kyoto 619-0288, Japan
| | - Yukiko Sekine
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Xiaochen Fu
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Junichi Nakai
- Division of Oral Physiology, Department of Disease Management Dentistry, Tohoku University Graduate School of Dentistry, Aoba, Sendai 980-8575, Japan
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Toru Takumi
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, Kobe 650-0017, Japan; RIKEN Center for Biosystems Dynamics Research, Chuo, Kobe 650-0047, Japan.
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21
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Ester EF, Pytel P. Changes in behavioral priority influence the accessibility of working memory content. Neuroimage 2023; 272:120055. [PMID: 37001833 DOI: 10.1016/j.neuroimage.2023.120055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/21/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023] Open
Abstract
Evolving behavioral goals require the existence of selection mechanisms that prioritize task-relevant working memory (WM) content for action. Selecting an item stored in WM is known to blunt and/or reverse information loss in stimulus-specific representations of that item reconstructed from human brain activity, but extant studies have focused on all-or-none circumstances that allow or disallow an agent to select one of several items stored in WM. Conversely, behavioral studies suggest that humans can flexibly assign different levels of priority to different items stored in WM, but how doing so influences neural representations of WM content is unclear. One possibility is that assigning different levels of priority to items in WM influences the quality of those representations, resulting in more robust neural representations of high- vs. low-priority WM content. A second - and non-exclusive - possibility is that asymmetries in behavioral priority influence how rapidly neural representations of high- vs. low-priority WM content can be selected and reported. We tested these possibilities in two experiments by decoding high- and low-priority WM content from EEG recordings obtained while human volunteers performed a retrospectively cued WM task. Probabilistic changes in the behavioral relevance of a remembered item had no effect on our ability to decode it from EEG signals; instead, these changes influenced the latency at which above-chance decoding performance was reached. Thus, our results indicate that probabilistic changes in the behavioral relevance of WM content influence the ease with which memories can be selected independently of their strength.
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22
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Musall S, Sun XR, Mohan H, An X, Gluf S, Li SJ, Drewes R, Cravo E, Lenzi I, Yin C, Kampa BM, Churchland AK. Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making. Nat Neurosci 2023; 26:495-505. [PMID: 36690900 PMCID: PMC9991922 DOI: 10.1038/s41593-022-01245-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 12/06/2022] [Indexed: 01/25/2023]
Abstract
Understanding how cortical circuits generate complex behavior requires investigating the cell types that comprise them. Functional differences across pyramidal neuron (PyN) types have been observed within cortical areas, but it is not known whether these local differences extend throughout the cortex, nor whether additional differences emerge when larger-scale dynamics are considered. We used genetic and retrograde labeling to target pyramidal tract, intratelencephalic and corticostriatal projection neurons and measured their cortex-wide activity. Each PyN type drove unique neural dynamics, both at the local and cortex-wide scales. Cortical activity and optogenetic inactivation during an auditory decision task revealed distinct functional roles. All PyNs in parietal cortex were recruited during perception of the auditory stimulus, but, surprisingly, pyramidal tract neurons had the largest causal role. In frontal cortex, all PyNs were required for accurate choices but showed distinct choice tuning. Our results reveal that rich, cell-type-specific cortical dynamics shape perceptual decisions.
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Affiliation(s)
- Simon Musall
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich, Jülich, Germany.
- Department of Systems Neurophysiology, Institute for Zoology, RWTH Aachen University, Aachen, Germany.
| | - Xiaonan R Sun
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, New York, NY, USA
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Hemanth Mohan
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - Steven Gluf
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, New York, NY, USA
| | - Shu-Jing Li
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, New York, NY, USA
| | - Rhonda Drewes
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, New York, NY, USA
| | - Emma Cravo
- Department of Systems Neurophysiology, Institute for Zoology, RWTH Aachen University, Aachen, Germany
| | - Irene Lenzi
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich, Jülich, Germany
- Department of Systems Neurophysiology, Institute for Zoology, RWTH Aachen University, Aachen, Germany
| | - Chaoqun Yin
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Björn M Kampa
- Department of Systems Neurophysiology, Institute for Zoology, RWTH Aachen University, Aachen, Germany
- JARA Brain, Institute for Neuroscience and Medicine (INM-10), Forschungszentrum Jülich, Jülich, Germany
| | - Anne K Churchland
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, New York, NY, USA.
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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23
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Higley MJ, Cardin JA. Spatiotemporal dynamics in large-scale cortical networks. Curr Opin Neurobiol 2022; 77:102627. [PMID: 36115252 PMCID: PMC10618958 DOI: 10.1016/j.conb.2022.102627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/27/2022] [Accepted: 08/04/2022] [Indexed: 01/10/2023]
Abstract
Investigating links between nervous system function and behavior requires monitoring neuronal activity at a range of spatial and temporal scales. Here, we summarize recent progress in applying two distinct but complementary approaches to the study of network dynamics in the neocortex. Mesoscopic calcium imaging allows simultaneous monitoring of activity across most of the cortex at moderate spatiotemporal resolution. Electrophysiological recordings provide extremely high temporal resolution of neural signals at multiple targeted locations. A number of recent studies have used these tools to reveal novel patterns of activity across distributed cortical subnetworks. This growing body of work strongly supports the hypothesis that the dynamic coordination of spatially distinct regions is a fundamental aspect of cortical function that supports cognition and behavior.
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Affiliation(s)
- Michael J Higley
- Departments of Neuroscience and Psychiatry, Kavli Institute for Neuroscience, Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jessica A Cardin
- Departments of Neuroscience and Psychiatry, Kavli Institute for Neuroscience, Wu Tsai Institute, Yale University, New Haven, CT, USA.
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24
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Chen APF, Malgady JM, Chen L, Shi KW, Cheng E, Plotkin JL, Ge S, Xiong Q. Nigrostriatal dopamine pathway regulates auditory discrimination behavior. Nat Commun 2022; 13:5942. [PMID: 36209150 PMCID: PMC9547888 DOI: 10.1038/s41467-022-33747-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022] Open
Abstract
The auditory striatum, the tail portion of dorsal striatum in basal ganglia, is implicated in perceptual decision-making, transforming auditory stimuli to action outcomes. Despite its known connections to diverse neurological conditions, the dopaminergic modulation of sensory striatal neuronal activity and its behavioral influences remain unknown. We demonstrated that the optogenetic inhibition of dopaminergic projections from the substantia nigra pars compacta to the auditory striatum specifically impairs mouse choice performance but not movement in an auditory frequency discrimination task. In vivo dopamine and calcium imaging in freely behaving mice revealed that this dopaminergic projection modulates striatal tone representations, and tone-evoked striatal dopamine release inversely correlated with the evidence strength of tones. Optogenetic inhibition of D1-receptor expressing neurons and pharmacological inhibition of D1 receptors in the auditory striatum dampened choice performance accuracy. Our study uncovers a phasic mechanism within the nigrostriatal system that regulates auditory decisions by modulating ongoing auditory perception. The auditory striatum, the tail portion of dorsal striatum, is implicated in decision-making. This study uncovers a phasic mechanism within the nigrostriatal system that regulates auditory decisions by modulating ongoing auditory perception.
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Affiliation(s)
- Allen P F Chen
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA.,Medical Scientist Training Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794, USA
| | - Jeffrey M Malgady
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Lu Chen
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Kaiyo W Shi
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Eileen Cheng
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA.,Department of Physiology and Biophysics, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Joshua L Plotkin
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA.,Center for Nervous System Disorders, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Shaoyu Ge
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Qiaojie Xiong
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA.
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25
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Transition of distinct context-dependent ensembles from secondary to primary motor cortex in skilled motor performance. Cell Rep 2022; 41:111494. [DOI: 10.1016/j.celrep.2022.111494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/27/2022] [Accepted: 09/21/2022] [Indexed: 11/19/2022] Open
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26
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Fomins A, Sych Y, Helmchen F. Conservative significance testing of tripartite statistical relations in multivariate neural data. Netw Neurosci 2022; 6:1243-1274. [PMID: 38800452 PMCID: PMC11117094 DOI: 10.1162/netn_a_00259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 06/14/2022] [Indexed: 05/29/2024] Open
Abstract
An important goal in systems neuroscience is to understand the structure of neuronal interactions, frequently approached by studying functional relations between recorded neuronal signals. Commonly used pairwise measures (e.g., correlation coefficient) offer limited insight, neither addressing the specificity of estimated neuronal interactions nor potential synergistic coupling between neuronal signals. Tripartite measures, such as partial correlation, variance partitioning, and partial information decomposition, address these questions by disentangling functional relations into interpretable information atoms (unique, redundant, and synergistic). Here, we apply these tripartite measures to simulated neuronal recordings to investigate their sensitivity to noise. We find that the considered measures are mostly accurate and specific for signals with noiseless sources but experience significant bias for noisy sources.We show that permutation testing of such measures results in high false positive rates even for small noise fractions and large data sizes. We present a conservative null hypothesis for significance testing of tripartite measures, which significantly decreases false positive rate at a tolerable expense of increasing false negative rate. We hope our study raises awareness about the potential pitfalls of significance testing and of interpretation of functional relations, offering both conceptual and practical advice.
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Affiliation(s)
- Aleksejs Fomins
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Switzerland
| | - Yaroslav Sych
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Experimental Neurology Center, Department of Neurology, Inselspital University Hospital Bern, Bern, Switzerland
- Present address: Institute of Cellular and Integrative Neurosciences, University of Strasbourg and CNRS, Strasbourg, France
| | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Switzerland
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27
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Sych Y, Fomins A, Novelli L, Helmchen F. Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning. Cell Rep 2022; 40:111394. [PMID: 36130513 PMCID: PMC9513804 DOI: 10.1016/j.celrep.2022.111394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 05/31/2022] [Accepted: 08/29/2022] [Indexed: 11/19/2022] Open
Abstract
Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within such mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity in 12 or 48 brain regions of mice trained in a tactile discrimination task. With improving task performance, most regions shift their peak activity from the time of reward-related action to the reward-predicting stimulus. By estimating cross-regional interactions using transfer entropy, we reveal that functional networks encompassing basal ganglia, thalamus, neocortex, and hippocampus grow and stabilize upon learning, especially at stimulus presentation time. The internal globus pallidus, ventromedial thalamus, and several regions in the frontal cortex emerge as salient hub regions. Our results highlight the learning-related dynamic reorganization that brain networks undergo when task-appropriate mesoscale network dynamics are established for goal-oriented behavior. Multi-fiber photometry reveals brain network adaptations during learning Activity in most regions temporally shifts from reward to predictive stimulus Cross-regional interactions in the CBGTC network increase and stabilize with learning Internal pallidum, VM thalamus, and prefrontal cortex regions emerge as hubs
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Affiliation(s)
- Yaroslav Sych
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland.
| | - Aleksejs Fomins
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland; Neuroscience Center Zurich, 8057 Zurich, Switzerland
| | - Leonardo Novelli
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland
| | - Fritjof Helmchen
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland; Neuroscience Center Zurich, 8057 Zurich, Switzerland.
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28
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Yin X, Wang Y, Li J, Guo ZV. Lateralization of short-term memory in the frontal cortex. Cell Rep 2022; 40:111190. [PMID: 35977520 DOI: 10.1016/j.celrep.2022.111190] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 06/04/2022] [Accepted: 07/20/2022] [Indexed: 11/03/2022] Open
Abstract
Despite essentially symmetric structures in mammalian brains, the left and right hemispheres do not contribute equally to certain cognitive functions. How both hemispheres interact to cause this asymmetry remains unclear. Here, we study this question in the anterior lateral motor cortex (ALM) of mice performing five versions of a tactile-based decision-making task with a short-term memory (STM) component. Unilateral inhibition of ALM produces variable behavioral deficits across tasks, with the left, right, or both ALMs playing critical roles in STM. Neural activity and its encoding capability are similar across hemispheres, despite that only one hemisphere dominates in behavior. Inhibition of the dominant ALM disrupts encoding capability in the non-dominant ALM, but not vice versa. Variable behavioral deficits are predicted by the influence on contralateral activity across sessions, mice, and tasks. Together, these results reveal that the left and right ALM interact asymmetrically, leading to their differential contributions to STM.
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Affiliation(s)
- Xinxin Yin
- School of Medicine, Tsinghua University, 100084 Beijing, China; IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China; School of Life Sciences, Tsinghua University, 100084 Beijing, China
| | - Yu Wang
- IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China; School of Life Sciences, Tsinghua University, 100084 Beijing, China
| | - Jiejue Li
- IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China; School of Life Sciences, Tsinghua University, 100084 Beijing, China
| | - Zengcai V Guo
- School of Medicine, Tsinghua University, 100084 Beijing, China; IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China.
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29
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Wang XJ. Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition. Annu Rev Neurosci 2022; 45:533-560. [PMID: 35803587 DOI: 10.1146/annurev-neuro-110920-035434] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.
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Affiliation(s)
- Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA;
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30
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Fagerholm ED, Foulkes WMC, Gallero-Salas Y, Helmchen F, Moran RJ, Friston KJ, Leech R. Estimating anisotropy directly via neural timeseries. J Comput Neurosci 2022; 50:241-249. [PMID: 35182268 PMCID: PMC9035010 DOI: 10.1007/s10827-021-00810-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/14/2021] [Accepted: 12/06/2021] [Indexed: 11/30/2022]
Abstract
An isotropic dynamical system is one that looks the same in every direction, i.e., if we imagine standing somewhere within an isotropic system, we would not be able to differentiate between different lines of sight. Conversely, anisotropy is a measure of the extent to which a system deviates from perfect isotropy, with larger values indicating greater discrepancies between the structure of the system along its axes. Here, we derive the form of a generalised scalable (mechanically similar) discretized field theoretic Lagrangian that allows for levels of anisotropy to be directly estimated via timeseries of arbitrary dimensionality. We generate synthetic data for both isotropic and anisotropic systems and, by using Bayesian model inversion and reduction, show that we can discriminate between the two datasets - thereby demonstrating proof of principle. We then apply this methodology to murine calcium imaging data collected in rest and task states, showing that anisotropy can be estimated directly from different brain states and cortical regions in an empirical in vivo biological setting. We hope that this theoretical foundation, together with the methodology and publicly available MATLAB code, will provide an accessible way for researchers to obtain new insight into the structural organization of neural systems in terms of how scalable neural regions grow - both ontogenetically during the development of an individual organism, as well as phylogenetically across species.
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Affiliation(s)
- Erik D Fagerholm
- Department of Neuroimaging, King's College London, London, United Kingdom.
| | - W M C Foulkes
- Department of Physics, Imperial College London, London, United Kingdom
| | - Yasir Gallero-Salas
- Brain Research Institute, University of Zürich, Zürich, Switzerland
- Neuroscience Center Zürich, Zürich, Switzerland
| | - Fritjof Helmchen
- Brain Research Institute, University of Zürich, Zürich, Switzerland
- Neuroscience Center Zürich, Zürich, Switzerland
| | - Rosalyn J Moran
- Department of Neuroimaging, King's College London, London, United Kingdom
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Robert Leech
- Department of Neuroimaging, King's College London, London, United Kingdom
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31
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Abdelfattah AS, Ahuja S, Akkin T, Allu SR, Brake J, Boas DA, Buckley EM, Campbell RE, Chen AI, Cheng X, Čižmár T, Costantini I, De Vittorio M, Devor A, Doran PR, El Khatib M, Emiliani V, Fomin-Thunemann N, Fainman Y, Fernandez-Alfonso T, Ferri CGL, Gilad A, Han X, Harris A, Hillman EMC, Hochgeschwender U, Holt MG, Ji N, Kılıç K, Lake EMR, Li L, Li T, Mächler P, Miller EW, Mesquita RC, Nadella KMNS, Nägerl UV, Nasu Y, Nimmerjahn A, Ondráčková P, Pavone FS, Perez Campos C, Peterka DS, Pisano F, Pisanello F, Puppo F, Sabatini BL, Sadegh S, Sakadzic S, Shoham S, Shroff SN, Silver RA, Sims RR, Smith SL, Srinivasan VJ, Thunemann M, Tian L, Tian L, Troxler T, Valera A, Vaziri A, Vinogradov SA, Vitale F, Wang LV, Uhlířová H, Xu C, Yang C, Yang MH, Yellen G, Yizhar O, Zhao Y. Neurophotonic tools for microscopic measurements and manipulation: status report. NEUROPHOTONICS 2022; 9:013001. [PMID: 35493335 PMCID: PMC9047450 DOI: 10.1117/1.nph.9.s1.013001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Ahmed S. Abdelfattah
- Brown University, Department of Neuroscience, Providence, Rhode Island, United States
| | - Sapna Ahuja
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Taner Akkin
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Srinivasa Rao Allu
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University, Department of Pediatrics, Atlanta, Georgia, United States
| | - Robert E. Campbell
- University of Tokyo, Department of Chemistry, Tokyo, Japan
- University of Alberta, Department of Chemistry, Edmonton, Alberta, Canada
| | - Anderson I. Chen
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Xiaojun Cheng
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Tomáš Čižmár
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Irene Costantini
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Biology, Florence, Italy
- National Institute of Optics, National Research Council, Rome, Italy
| | - Massimo De Vittorio
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Anna Devor
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Patrick R. Doran
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Mirna El Khatib
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | | | - Natalie Fomin-Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Yeshaiahu Fainman
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Tomas Fernandez-Alfonso
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Christopher G. L. Ferri
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Ariel Gilad
- The Hebrew University of Jerusalem, Institute for Medical Research Israel–Canada, Department of Medical Neurobiology, Faculty of Medicine, Jerusalem, Israel
| | - Xue Han
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Andrew Harris
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | | | - Ute Hochgeschwender
- Central Michigan University, Department of Neuroscience, Mount Pleasant, Michigan, United States
| | - Matthew G. Holt
- University of Porto, Instituto de Investigação e Inovação em Saúde (i3S), Porto, Portugal
| | - Na Ji
- University of California Berkeley, Department of Physics, Berkeley, California, United States
| | - Kıvılcım Kılıç
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evelyn M. R. Lake
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, Connecticut, United States
| | - Lei Li
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Tianqi Li
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Philipp Mächler
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evan W. Miller
- University of California Berkeley, Departments of Chemistry and Molecular & Cell Biology and Helen Wills Neuroscience Institute, Berkeley, California, United States
| | | | | | - U. Valentin Nägerl
- Interdisciplinary Institute for Neuroscience University of Bordeaux & CNRS, Bordeaux, France
| | - Yusuke Nasu
- University of Tokyo, Department of Chemistry, Tokyo, Japan
| | - Axel Nimmerjahn
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, California, United States
| | - Petra Ondráčková
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Francesco S. Pavone
- National Institute of Optics, National Research Council, Rome, Italy
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Physics, Florence, Italy
| | - Citlali Perez Campos
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Filippo Pisano
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Ferruccio Pisanello
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Francesca Puppo
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Bernardo L. Sabatini
- Harvard Medical School, Howard Hughes Medical Institute, Department of Neurobiology, Boston, Massachusetts, United States
| | - Sanaz Sadegh
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Shy Shoham
- New York University Grossman School of Medicine, Tech4Health and Neuroscience Institutes, New York, New York, United States
| | - Sanaya N. Shroff
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - R. Angus Silver
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Ruth R. Sims
- Sorbonne University, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Spencer L. Smith
- University of California Santa Barbara, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
| | - Vivek J. Srinivasan
- New York University Langone Health, Departments of Ophthalmology and Radiology, New York, New York, United States
| | - Martin Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Lei Tian
- Boston University, Departments of Electrical Engineering and Biomedical Engineering, Boston, Massachusetts, United States
| | - Lin Tian
- University of California Davis, Department of Biochemistry and Molecular Medicine, Davis, California, United States
| | - Thomas Troxler
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Antoine Valera
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Alipasha Vaziri
- Rockefeller University, Laboratory of Neurotechnology and Biophysics, New York, New York, United States
- The Rockefeller University, The Kavli Neural Systems Institute, New York, New York, United States
| | - Sergei A. Vinogradov
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Flavia Vitale
- Center for Neuroengineering and Therapeutics, Departments of Neurology, Bioengineering, Physical Medicine and Rehabilitation, Philadelphia, Pennsylvania, United States
| | - Lihong V. Wang
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Hana Uhlířová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Chris Xu
- Cornell University, School of Applied and Engineering Physics, Ithaca, New York, United States
| | - Changhuei Yang
- California Institute of Technology, Departments of Electrical Engineering, Bioengineering and Medical Engineering, Pasadena, California, United States
| | - Mu-Han Yang
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Gary Yellen
- Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, United States
| | - Ofer Yizhar
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | - Yongxin Zhao
- Carnegie Mellon University, Department of Biological Sciences, Pittsburgh, Pennsylvania, United States
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32
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Ewall G, Parkins S, Lin A, Jaoui Y, Lee HK. Cortical and Subcortical Circuits for Cross-Modal Plasticity Induced by Loss of Vision. Front Neural Circuits 2021; 15:665009. [PMID: 34113240 PMCID: PMC8185208 DOI: 10.3389/fncir.2021.665009] [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: 02/06/2021] [Accepted: 04/14/2021] [Indexed: 11/29/2022] Open
Abstract
Cortical areas are highly interconnected both via cortical and subcortical pathways, and primary sensory cortices are not isolated from this general structure. In primary sensory cortical areas, these pre-existing functional connections serve to provide contextual information for sensory processing and can mediate adaptation when a sensory modality is lost. Cross-modal plasticity in broad terms refers to widespread plasticity across the brain in response to losing a sensory modality, and largely involves two distinct changes: cross-modal recruitment and compensatory plasticity. The former involves recruitment of the deprived sensory area, which includes the deprived primary sensory cortex, for processing the remaining senses. Compensatory plasticity refers to plasticity in the remaining sensory areas, including the spared primary sensory cortices, to enhance the processing of its own sensory inputs. Here, we will summarize potential cellular plasticity mechanisms involved in cross-modal recruitment and compensatory plasticity, and review cortical and subcortical circuits to the primary sensory cortices which can mediate cross-modal plasticity upon loss of vision.
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Affiliation(s)
- Gabrielle Ewall
- Solomon H. Snyder Department of Neuroscience, Zanvyl-Krieger Mind/Brain Institute, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Samuel Parkins
- Cell, Molecular, Developmental Biology and Biophysics (CMDB) Graduate Program, Johns Hopkins University, Baltimore, MD, United States
| | - Amy Lin
- Solomon H. Snyder Department of Neuroscience, Zanvyl-Krieger Mind/Brain Institute, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Yanis Jaoui
- Solomon H. Snyder Department of Neuroscience, Zanvyl-Krieger Mind/Brain Institute, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Hey-Kyoung Lee
- Solomon H. Snyder Department of Neuroscience, Zanvyl-Krieger Mind/Brain Institute, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Cell, Molecular, Developmental Biology and Biophysics (CMDB) Graduate Program, Johns Hopkins University, Baltimore, MD, United States.,Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, United States
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