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Nie XP, Xu XS, Feng Z, Wang W, Ma C, Yang YX, Li JN, Zhou QX, Xu FQ, Luo MH, Zhou JN, Gong H, Xu L. Depicting Primate-Like Granular Dorsolateral Prefrontal Cortex in the Chinese Tree Shrew. eNeuro 2024; 11:ENEURO.0307-24.2024. [PMID: 39455280 PMCID: PMC11514722 DOI: 10.1523/eneuro.0307-24.2024] [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: 07/10/2024] [Revised: 09/04/2024] [Accepted: 09/24/2024] [Indexed: 10/28/2024] Open
Abstract
It remains unknown whether the Chinese tree shrew, regarded as the closest sister of primate, has evolved a dorsolateral prefrontal cortex (dlPFC) comparable with primates that is characterized by a fourth layer (L4) enriched with granular cells and reciprocal connections with the mediodorsal nucleus (MD). Here, we reported that following AAV-hSyn-EGFP expression in the MD neurons, the fluorescence micro-optical sectioning tomography revealed their projection trajectories and targeted brain areas, such as the hippocampus, the corpus striatum, and the dlPFC. Cre-dependent transsynaptic viral tracing identified the MD projection terminals that targeted the L4 of the dlPFC, in which the presence of granular cells was confirmed via cytoarchitectural studies by using the Nissl, Golgi, and vGlut2 stainings. Additionally, the L5/6 of the dlPFC projected back to the MD. These results suggest that the tree shrew has evolved a primate-like dlPFC which can serve as an alternative for studying cognition-related functions of the dlPFC.
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Affiliation(s)
- Xiu-Peng Nie
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
| | - Xiao-Shan Xu
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
| | - Zhao Feng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, China
| | - Wei Wang
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
| | - Chen Ma
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
| | - Yue-Xiong Yang
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
| | - Jin-Nan Li
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
| | - Qi-Xin Zhou
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
| | - Fu-Qiang Xu
- Shen Zhen Institute of Advanced Technology, Chinese Academy of Sciences, Xi Li Shen Zhen University Town, Shenzhen 518055, China
- Chinese Academy of Sciences Centre for Excellence in Brain Science and Intelligent Technology, Shanghai 200031, China
| | - Min-Hua Luo
- State Key Laboratory of Virology and Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China
| | - Jiang-Ning Zhou
- Chinese Academy of Sciences Centre for Excellence in Brain Science and Intelligent Technology, Shanghai 200031, China
- Institute of Brain Science, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
- Chinese Academy of Science Key Laboratory of Brain Function and Diseases, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, China
| | - Lin Xu
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Academy of Science Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Kunming 650223, China
- Chinese Academy of Sciences Centre for Excellence in Brain Science and Intelligent Technology, Shanghai 200031, China
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2
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Mateus JC, Sousa MM, Burrone J, Aguiar P. Beyond a Transmission Cable-New Technologies to Reveal the Richness in Axonal Electrophysiology. J Neurosci 2024; 44:e1446232023. [PMID: 38479812 PMCID: PMC10941245 DOI: 10.1523/jneurosci.1446-23.2023] [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: 08/07/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 03/17/2024] Open
Abstract
The axon is a neuronal structure capable of processing, encoding, and transmitting information. This assessment contrasts with a limiting, but deeply rooted, perspective where the axon functions solely as a transmission cable of somatodendritic activity, sending signals in the form of stereotypical action potentials. This perspective arose, at least partially, because of the technical difficulties in probing axons: their extreme length-to-diameter ratio and intricate growth paths preclude the study of their dynamics through traditional techniques. Recent findings are challenging this view and revealing a much larger repertoire of axonal computations. Axons display complex signaling processes and structure-function relationships, which can be modulated via diverse activity-dependent mechanisms. Additionally, axons can exhibit patterns of activity that are dramatically different from those of their corresponding soma. Not surprisingly, many of these recent discoveries have been driven by novel technology developments, which allow for in vitro axon electrophysiology with unprecedented spatiotemporal resolution and signal-to-noise ratio. In this review, we outline the state-of-the-art in vitro toolset for axonal electrophysiology and summarize the recent discoveries in axon function it has enabled. We also review the increasing repertoire of microtechnologies for controlling axon guidance which, in combination with the available cutting-edge electrophysiology and imaging approaches, have the potential for more controlled and high-throughput in vitro studies. We anticipate that a larger adoption of these new technologies by the neuroscience community will drive a new era of experimental opportunities in the study of axon physiology and consequently, neuronal function.
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Affiliation(s)
- J C Mateus
- i3S- Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal
| | - M M Sousa
- i3S- Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal
| | - J Burrone
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, United Kingdom
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, United Kingdom
| | - P Aguiar
- i3S- Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal
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Kourosh-Arami M, Komaki A, Gholami M, Marashi SH, Hejazi S. Heterosynaptic plasticity-induced modulation of synapses. J Physiol Sci 2023; 73:33. [PMID: 38057729 DOI: 10.1186/s12576-023-00893-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
Plasticity is a common feature of synapses that is stated in different ways and occurs through several mechanisms. The regular action of the brain needs to be balanced in several neuronal and synaptic features, one of which is synaptic plasticity. The different homeostatic processes, including the balance between excitation/inhibition or homeostasis of synaptic weights at the single-neuron level, may obtain this. Homosynaptic Hebbian-type plasticity causes associative alterations of synapses. Both homosynaptic and heterosynaptic plasticity characterize the corresponding aspects of adjustable synapses, and both are essential for the regular action of neural systems and their plastic synapses.In this review, we will compare homo- and heterosynaptic plasticity and the main factors affecting the direction of plastic changes. This review paper will also discuss the diverse functions of the different kinds of heterosynaptic plasticity and their properties. We argue that a complementary system of heterosynaptic plasticity demonstrates an essential cellular constituent for homeostatic modulation of synaptic weights and neuronal activity.
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Affiliation(s)
- Masoumeh Kourosh-Arami
- Department of Neuroscience, School of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Alireza Komaki
- Department of Neuroscience, School of Science and Advanced Technologies in Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Masoumeh Gholami
- Department of Physiology, Medical College, Arak University of Medical Sciences, Arak, Iran
| | | | - Sara Hejazi
- Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, USA
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4
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Rockland KS. Cellular and laminar architecture: A short history and commentary. J Comp Neurol 2023; 531:1926-1933. [PMID: 37941081 PMCID: PMC11406557 DOI: 10.1002/cne.25553] [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: 02/08/2023] [Revised: 08/11/2023] [Accepted: 10/13/2023] [Indexed: 11/10/2023]
Abstract
The feedforward/feedback classification, as originally stated in relation to early visual areas in the macaque monkey, has had a significant influence on ideas of laminar interactions, area reciprocity, and cortical hierarchical organization. In some contrast with this macroscale "laminar connectomics," a more cellular approach to cortical connections, as briefly surveyed here, points to a still underappreciated heterogeneity of neuronal subtypes and complex microcircuitries. From the perspective of heterogeneities, the question of how brain regions interact and influence each other quickly leads to discussions about concurrent hierarchical and nonhierarchical cortical features, brain organization as a multiscale system forming nested groups and hierarchies, connectomes annotated by multiple biological attributes, and interleaved and overlapping scales of organization.
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Affiliation(s)
- Kathleen S Rockland
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
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5
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Wang Y, Wang J, Zhang QF, Xiao KW, Wang L, Yu QP, Xie Q, Poo MM, Wen Y. Neural Mechanism Underlying Task-Specific Enhancement of Motor Learning by Concurrent Transcranial Direct Current Stimulation. Neurosci Bull 2023; 39:69-82. [PMID: 35908004 PMCID: PMC9849633 DOI: 10.1007/s12264-022-00901-1] [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: 01/23/2022] [Accepted: 04/10/2022] [Indexed: 01/22/2023] Open
Abstract
The optimal protocol for neuromodulation by transcranial direct current stimulation (tDCS) remains unclear. Using the rotarod paradigm, we found that mouse motor learning was enhanced by anodal tDCS (3.2 mA/cm2) during but not before or after the performance of a task. Dual-task experiments showed that motor learning enhancement was specific to the task accompanied by anodal tDCS. Studies using a mouse model of stroke induced by middle cerebral artery occlusion showed that concurrent anodal tDCS restored motor learning capability in a task-specific manner. Transcranial in vivo Ca2+ imaging further showed that anodal tDCS elevated and cathodal tDCS suppressed neuronal activity in the primary motor cortex (M1). Anodal tDCS specifically promoted the activity of task-related M1 neurons during task performance, suggesting that elevated Hebbian synaptic potentiation in task-activated circuits accounts for the motor learning enhancement. Thus, application of tDCS concurrent with the targeted behavioral dysfunction could be an effective approach to treating brain disorders.
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Affiliation(s)
- Ying Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, 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
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Lingang Laboratory, Shanghai, 201210, China
| | - Jixian Wang
- Department of Rehabilitation Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qing-Fang Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ke-Wei Xiao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Liang Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qing-Ping Yu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qing Xie
- Department of Rehabilitation Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Mu-Ming Poo
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, 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.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Lingang Laboratory, Shanghai, 201210, China.
| | - Yunqing Wen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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Resting-State Neuronal Activity and Functional Connectivity Changes in the Visual Cortex after High Altitude Exposure: A Longitudinal Study. Brain Sci 2022; 12:brainsci12060724. [PMID: 35741609 PMCID: PMC9221383 DOI: 10.3390/brainsci12060724] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/27/2022] [Accepted: 05/30/2022] [Indexed: 12/31/2022] Open
Abstract
Damage to the visual cortex structures after high altitude exposure has been well clarified. However, changes in the neuronal activity and functional connectivity (FC) of the visual cortex after hypoxia/reoxygenation remain unclear. Twenty-three sea-level college students, who took part in 30 days of teaching at high altitude (4300 m), underwent routine blood tests, visual behavior tests, and magnetic resonance imaging scans before they went to high altitude (Test 1), 7 days after they returned to sea level (Test 2), as well as 3 months (Test 3) after they returned to sea level. In this study, we investigated the hematological parameters, behavioral data, and spontaneous brain activity. There were significant differences among the tests in hematological parameters and spontaneous brain activity. The hematocrit, hemoglobin concentration, and red blood cell count were significantly increased in Test 2 as compared with Tests 1 and 3. As compared with Test 1, Test 3 increased amplitudes of low-frequency fluctuations (ALFF) in the right calcarine gyrus; Tests 2 and 3 increased ALFF in the right supplementary motor cortex, increased regional homogeneity (ReHo) in the left lingual gyrus, increased the voxel-mirrored homotopic connectivity (VMHC) value in the motor cortex, and decreased FC between the left lingual gyrus and left postcentral gyrus. The color accuracy in the visual task was positively correlated with ALFF and ReHo in Test 2. Hypoxia/reoxygenation increased functional connection between the neurons within the visual cortex and the motor cortex but decreased connection between the visual cortex and motor cortex.
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Broussard GJ, Petreanu L. Eavesdropping wires: Recording activity in axons using genetically encoded calcium indicators. J Neurosci Methods 2021; 360:109251. [PMID: 34119572 PMCID: PMC8363211 DOI: 10.1016/j.jneumeth.2021.109251] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/31/2021] [Accepted: 06/05/2021] [Indexed: 12/23/2022]
Abstract
Neurons broadcast electrical signals to distal brain regions through extensive axonal arbors. Genetically encoded calcium sensors permit the direct observation of action potential activity at axonal terminals, providing unique insights on the organization and function of neural projections. Here, we consider what information can be gleaned from axonal recordings made at scales ranging from the summed activity extracted from multi-cell axon projections to single boutons. In particular, we discuss the application of different recently developed multi photon and fiber photometry methods for recording neural activity in axons of rodents. We define experimental difficulties associated with imaging approaches in the axonal compartment and highlight the latest methodological advances for addressing these issues. Finally, we reflect on ways in which new technologies can be used in conjunction with axon calcium imaging to address current questions in neurobiology.
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Affiliation(s)
| | - Leopoldo Petreanu
- Champalimaud Research, Champalimaud Center for the Unknown, Lisbon, Portugal.
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8
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Young H, Belbut B, Baeta M, Petreanu L. Laminar-specific cortico-cortical loops in mouse visual cortex. eLife 2021; 10:e59551. [PMID: 33522479 PMCID: PMC7877907 DOI: 10.7554/elife.59551] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 01/29/2021] [Indexed: 11/13/2022] Open
Abstract
Many theories propose recurrent interactions across the cortical hierarchy, but it is unclear if cortical circuits are selectively wired to implement looped computations. Using subcellular channelrhodopsin-2-assisted circuit mapping in mouse visual cortex, we compared feedforward (FF) or feedback (FB) cortico-cortical (CC) synaptic input to cells projecting back to the input source (looped neurons) with cells projecting to a different cortical or subcortical area. FF and FB afferents showed similar cell-type selectivity, making stronger connections with looped neurons than with other projection types in layer (L)5 and L6, but not in L2/3, resulting in selective modulation of activity in looped neurons. In most cases, stronger connections in looped L5 neurons were located on their apical tufts, but not on their perisomatic dendrites. Our results reveal that CC connections are selectively wired to form monosynaptic excitatory loops and support a differential role of supragranular and infragranular neurons in hierarchical recurrent computations.
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Affiliation(s)
- Hedi Young
- Champalimaud Research, Champalimaud Center for the UnknownLisbonPortugal
| | - Beatriz Belbut
- Champalimaud Research, Champalimaud Center for the UnknownLisbonPortugal
| | - Margarida Baeta
- Champalimaud Research, Champalimaud Center for the UnknownLisbonPortugal
| | - Leopoldo Petreanu
- Champalimaud Research, Champalimaud Center for the UnknownLisbonPortugal
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9
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Abstract
The physiological response properties of neurons in the visual system are inherited mainly from feedforward inputs. Interestingly, feedback inputs often outnumber feedforward inputs. Although they are numerous, feedback connections are weaker, slower, and considered to be modulatory, in contrast to fast, high-efficacy feedforward connections. Accordingly, the functional role of feedback in visual processing has remained a fundamental mystery in vision science. At the core of this mystery are questions about whether feedback circuits regulate spatial receptive field properties versus temporal responses among target neurons, or whether feedback serves a more global role in arousal or attention. These proposed functions are not mutually exclusive, and there is compelling evidence to support multiple functional roles for feedback. In this review, the role of feedback in vision will be explored mainly from the perspective of corticothalamic feedback. Further generalized principles of feedback applicable to corticocortical connections will also be considered.
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Affiliation(s)
- Farran Briggs
- Departments of Neuroscience and Brain and Cognitive Sciences, Del Monte Institute for Neuroscience, and Center for Visual Science, University of Rochester, Rochester, New York 14642, USA;
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10
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Ju N, Li Y, Liu F, Jiang H, Macknik SL, Martinez-Conde S, Tang S. Spatiotemporal functional organization of excitatory synaptic inputs onto macaque V1 neurons. Nat Commun 2020; 11:697. [PMID: 32019929 PMCID: PMC7000673 DOI: 10.1038/s41467-020-14501-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 01/14/2020] [Indexed: 11/09/2022] Open
Abstract
The integration of synaptic inputs onto dendrites provides the basis for neuronal computation. Whereas recent studies have begun to outline the spatial organization of synaptic inputs on individual neurons, the underlying principles related to the specific neural functions are not well understood. Here we perform two-photon dendritic imaging with a genetically-encoded glutamate sensor in awake monkeys, and map the excitatory synaptic inputs on dendrites of individual V1 superficial layer neurons with high spatial and temporal resolution. We find a functional integration and trade-off between orientation-selective and color-selective inputs in basal dendrites of individual V1 neurons. Synaptic inputs on dendrites are spatially clustered by stimulus feature, but functionally scattered in multidimensional feature space, providing a potential substrate of local feature integration on dendritic branches. Furthermore, apical dendrite inputs have larger receptive fields and longer response latencies than basal dendrite inputs, suggesting a dominant role for apical dendrites in integrating feedback in visual information processing.
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Affiliation(s)
- Niansheng Ju
- Peking University School of Life Sciences, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, 100871, Beijing, China.,IDG/McGovern Institute for Brain Research at Peking University, 100871, Beijing, China.,Key Laboratory of Machine Perception (Ministry of Education), Peking University, 100871, Beijing, China
| | - Yang Li
- Peking University School of Life Sciences, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, 100871, Beijing, China.,IDG/McGovern Institute for Brain Research at Peking University, 100871, Beijing, China.,Key Laboratory of Machine Perception (Ministry of Education), Peking University, 100871, Beijing, China
| | - Fang Liu
- Peking University School of Life Sciences, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, 100871, Beijing, China.,IDG/McGovern Institute for Brain Research at Peking University, 100871, Beijing, China.,Key Laboratory of Machine Perception (Ministry of Education), Peking University, 100871, Beijing, China
| | - Hongfei Jiang
- Peking University School of Life Sciences, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, 100871, Beijing, China.,IDG/McGovern Institute for Brain Research at Peking University, 100871, Beijing, China.,Key Laboratory of Machine Perception (Ministry of Education), Peking University, 100871, Beijing, China
| | - Stephen L Macknik
- State University of New York, Downstate Health Sciences University, 11203, Brooklyn, NY, USA
| | - Susana Martinez-Conde
- State University of New York, Downstate Health Sciences University, 11203, Brooklyn, NY, USA
| | - Shiming Tang
- Peking University School of Life Sciences, 100871, Beijing, China. .,Peking-Tsinghua Center for Life Sciences, 100871, Beijing, China. .,IDG/McGovern Institute for Brain Research at Peking University, 100871, Beijing, China. .,Key Laboratory of Machine Perception (Ministry of Education), Peking University, 100871, Beijing, China.
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11
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Rockland KS. What we can learn from the complex architecture of single axons. Brain Struct Funct 2020; 225:1327-1347. [DOI: 10.1007/s00429-019-02023-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 12/30/2019] [Indexed: 12/22/2022]
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