1
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Pattadkal JJ, Priebe NJ. Neural Recording Chambers for Long-Term Access to Brain Tissue. J Neurosci 2025; 45:e1106242024. [PMID: 39725520 PMCID: PMC11823352 DOI: 10.1523/jneurosci.1106-24.2024] [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: 08/25/2024] [Revised: 10/10/2024] [Accepted: 11/01/2024] [Indexed: 12/28/2024] Open
Abstract
We describe a chamber system to perform imaging, electrophysiology, and optogenetic stimulation in awake and anesthetized marmosets. We developed this low-profile chamber design to be able to access the underlying tissue when needed or to leave it sealed for long periods. Such accessibility is useful to maintain chamber clarity as well as perform viral or drug injections at different time points. The chamber is flexible as either optical or electrophysiological recording can be performed in the same chamber by exchanging chamber inserts. The design provides an easy approach to day-to-day stable neural recordings that was developed for marmosets and can be extended to other species.
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Affiliation(s)
- Jagruti J Pattadkal
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712
| | - Nicholas J Priebe
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712
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2
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Li W, Chen W, Dai Z, Chai X, An S, Guan Z, Zhou W, Chen J, Gong H, Luo Q, Feng Z, Li A. Graph-based cell pattern recognition for merging the multi-modal optical microscopic image of neurons. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108392. [PMID: 39226842 DOI: 10.1016/j.cmpb.2024.108392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 08/06/2024] [Accepted: 08/22/2024] [Indexed: 09/05/2024]
Abstract
A deep understanding of neuron structure and function is crucial for elucidating brain mechanisms, diagnosing and treating diseases. Optical microscopy, pivotal in neuroscience, illuminates neuronal shapes, projections, and electrical activities. To explore the projection of specific functional neurons, scientists have been developing optical-based multimodal imaging strategies to simultaneously capture dynamic in vivo signals and static ex vivo structures from the same neuron. However, the original position of neurons is highly susceptible to displacement during ex vivo imaging, presenting a significant challenge for integrating multimodal information at the single-neuron level. This study introduces a graph-model-based approach for cell image matching, facilitating precise and automated pairing of sparsely labeled neurons across different optical microscopic images. It has been shown that utilizing neuron distribution as a matching feature can mitigate modal differences, the high-order graph model can address scale inconsistency, and the nonlinear iteration can resolve discrepancies in neuron density. This strategy was applied to the connectivity study of the mouse visual cortex, performing cell matching between the two-photon calcium image and the HD-fMOST brain-wide anatomical image sets. Experimental results demonstrate 96.67% precision, 85.29% recall rate, and 90.63% F1 Score, comparable to expert technicians. This study builds a bridge between functional and structural imaging, offering crucial technical support for neuron classification and circuitry analysis.
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Affiliation(s)
- Wenwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Wu Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Zimin Dai
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Xiaokang Chai
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Sile An
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Zhuang Guan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Wei Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Jianwei Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China; HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, PR China
| | - Qingming Luo
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, PR China
| | - Zhao Feng
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, PR China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, PR China; HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, PR China; Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, PR China.
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3
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Chen C, Song S. Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding. J Neurosci 2024; 44:e0159242024. [PMID: 39261006 PMCID: PMC11502229 DOI: 10.1523/jneurosci.0159-24.2024] [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: 01/23/2024] [Revised: 07/20/2024] [Accepted: 07/28/2024] [Indexed: 09/13/2024] Open
Abstract
Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.
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Affiliation(s)
- Chenggang Chen
- Tsinghua Laboratory of Brain and Intelligence and School of Biomedical Engineering, McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Sen Song
- Tsinghua Laboratory of Brain and Intelligence and School of Biomedical Engineering, McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
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4
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Lin L, Liu J, Pan Z, Pang W, Jiang X, Lei M, Gao J, Xiao Y, Li B, Hu F, Bao Z, Wei X, Wu W, Gu B. General Post-Regulation Strategy of AIEgens' Photophysical Properties for Intravital Two-Photon Fluorescence Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2404792. [PMID: 39119825 PMCID: PMC11481373 DOI: 10.1002/advs.202404792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Revised: 07/28/2024] [Indexed: 08/10/2024]
Abstract
Fluorogens with aggregation-induced emission (AIEgens) are promising agents for two-photon fluorescence (TPF) imaging. However, AIEgens' photophysical properties are fixed and unoptimizable once synthesized. Therefore, it is urgent and meaningful to explore an efficient post-regulation strategy to optimize AIEgens' photophysical properties. Herein, a general and efficient post-regulation strategy is reported. By simply tuning the ratio of inert AIEgens within binary nanoparticles (BNPs), the fluorescence quantum yield and two-photon absorption cross-section of functional AIEgens are enhanced by 8.7 and 5.4 times respectively, which are not achievable by conventional strategies, and the notorious phototoxicity is almost eliminated. The experimental results, theoretical simulation, and mechanism analysis demonstrated its feasibility and generality. The BNPs enabled deep cerebrovascular network imaging with ≈1.10 mm depth and metastatic cancer cell detection with single-cell resolution. Furthermore, the TPF imaging quality is improved by the self-supervised denoising algorithm. The proposed binary molecular post-regulation strategy opened a new avenue to efficiently boost the AIEgens' photophysical properties and consequently TPF imaging quality.
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Affiliation(s)
- Liyun Lin
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Jiaxin Liu
- Department of ChemistryInstitute of Molecular Aggregation ScienceTianjin UniversityTianjin300072China
| | - Zhengyuan Pan
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Wen Pang
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Xinyan Jiang
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Man Lei
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Jucai Gao
- Biomaterials Research CenterSchool of Biomedical EngineeringSouthern Medical UniversityGuangzhou510515China
| | - Yujie Xiao
- Department of NeurologyHuashan HospitalMOE Frontiers Center for Brain ScienceState Key Laboratory of Medical NeurobiologyInstitutes for Translational Brain ResearchFudan UniversityShanghai200437China
| | - Bo Li
- Department of NeurologyHuashan HospitalMOE Frontiers Center for Brain ScienceState Key Laboratory of Medical NeurobiologyInstitutes for Translational Brain ResearchFudan UniversityShanghai200437China
| | - Fang Hu
- Biomaterials Research CenterSchool of Biomedical EngineeringSouthern Medical UniversityGuangzhou510515China
| | - Zhouzhou Bao
- Shanghai Key Laboratory of Gynecologic OncologyRen Ji HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai200127China
| | - Xunbin Wei
- Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing)Peking University Cancer Hospital & InstituteBeijing100142China
- Biomedical Engineering Department and International Cancer InstitutePeking UniversityBeijing100191China
| | - Wenbo Wu
- Department of ChemistryInstitute of Molecular Aggregation ScienceTianjin UniversityTianjin300072China
| | - Bobo Gu
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
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5
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Chemerkouh MJHN, Zhou X, Yang Y, Wang S. Deep Learning Enhanced Label-Free Action Potential Detection Using Plasmonic-Based Electrochemical Impedance Microscopy. Anal Chem 2024; 96:11299-11308. [PMID: 38953225 PMCID: PMC11283340 DOI: 10.1021/acs.analchem.4c01179] [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: 07/03/2024]
Abstract
Measuring neuronal electrical activity, such as action potential propagation in cells, requires the sensitive detection of the weak electrical signal with high spatial and temporal resolution. None of the existing tools can fulfill this need. Recently, plasmonic-based electrochemical impedance microscopy (P-EIM) was demonstrated for the label-free mapping of the ignition and propagation of action potentials in neuron cells with subcellular resolution. However, limited by the signal-to-noise ratio in the high-speed P-EIM video, action potential mapping was achieved by averaging 90 cycles of signals. Such extensive averaging is not desired and may not always be feasible due to factors such as neuronal desensitization. In this study, we utilized advanced signal processing techniques to detect action potentials in P-EIM extracted signals with fewer averaged cycles. Matched filtering successfully detected action potential signals with as few as averaging five cycles of signals. Long short-term memory (LSTM) recurrent neural network achieved the best performance and was able to detect single-cycle stimulated action potential successfully [satisfactory area under the receiver operating characteristic curve (AUC) equal to 0.855]. Therefore, we show that deep learning-based signal processing can dramatically improve the usability of P-EIM mapping of neuronal electrical signals.
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Affiliation(s)
- Mohammad Javad Haji Najafi Chemerkouh
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - Xinyu Zhou
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - Yunze Yang
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA
| | - Shaopeng Wang
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
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6
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Song X, Guo Y, Chen C, Lee JH, Wang X. Tonotopic organization of auditory cortex in awake marmosets revealed by multi-modal wide-field optical imaging. CURRENT RESEARCH IN NEUROBIOLOGY 2024; 6:100132. [PMID: 38799765 PMCID: PMC11127206 DOI: 10.1016/j.crneur.2024.100132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 05/06/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024] Open
Abstract
Tonotopic organization of the auditory cortex has been extensively studied in many mammalian species using various methodologies and physiological preparations. Tonotopy mapping in primates, however, is more limited due to constraints such as cortical folding, use of anesthetized subjects, and mapping methodology. Here we applied a combination of through-skull and through-window intrinsic optical signal imaging, wide-field calcium imaging, and neural probe recording techniques in awake marmosets (Callithrix jacchus), a New World monkey with most of its auditory cortex located on a flat brain surface. Coarse tonotopic gradients, including a recently described rostral-temporal (RT) to parabelt gradient, were revealed by the through-skull imaging of intrinsic optical signals and were subsequently validated by single-unit recording. Furthermore, these tonotopic gradients were observed with more detail through chronically implanted cranial windows with additional verifications on the experimental design. Moreover, the tonotopy mapped by the intrinsic-signal imaging methods was verified by wide-field calcium imaging in an AAV-GCaMP labeled subject. After these validations and with further effort to expand the field of view more rostrally in both windowed and through-skull subjects, an additional putative tonotopic gradient was observed more rostrally to the area RT, which has not been previously described by the standard model of tonotopic organization of the primate auditory cortex. Together, these results provide the most comprehensive data of tonotopy mapping in an awake primate species with unprecedented coverage and details in the rostral proportion and support a caudal-rostrally arranged mesoscale organization of at least three repeats of functional gradients in the primate auditory cortex, similar to the ventral stream of primate visual cortex.
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Affiliation(s)
- Xindong Song
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, China
| | - Yueqi Guo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Chenggang Chen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jong Hoon Lee
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biological Sciences, KAIST, Daejeon, 34141, South Korea
| | - Xiaoqin Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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7
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Zhu X, Yan H, Zhan Y, Feng F, Wei C, Yao YG, Liu C. An anatomical and connectivity atlas of the marmoset cerebellum. Cell Rep 2023; 42:112480. [PMID: 37163375 DOI: 10.1016/j.celrep.2023.112480] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/01/2023] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
The cerebellum is essential for motor control and cognitive functioning, engaging in bidirectional communication with the cerebral cortex. The common marmoset, a small non-human primate, offers unique advantages for studying cerebello-cerebral circuits. However, the marmoset cerebellum is not well described in published resources. In this study, we present a comprehensive atlas of the marmoset cerebellum comprising (1) fine-detailed anatomical atlases and surface-analysis tools of the cerebellar cortex based on ultra-high-resolution ex vivo MRI, (2) functional connectivity and gradient patterns of the cerebellar cortex revealed by awake resting-state fMRI, and (3) structural-connectivity mapping of cerebellar nuclei using high-resolution diffusion MRI tractography. The atlas elucidates the anatomical details of the marmoset cerebellum, reveals distinct gradient patterns of intra-cerebellar and cerebello-cerebral functional connectivity, and maps the topological relationship of cerebellar nuclei in cerebello-cerebral circuits. As version 5 of the Marmoset Brain Mapping project, this atlas is publicly available at https://marmosetbrainmapping.org/MBMv5.html.
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Affiliation(s)
- Xiaojia Zhu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haotian Yan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yafeng Zhan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Furui Feng
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chuanyao Wei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Cirong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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8
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Chen C, Remington ED, Wang X. Sound localization acuity of the common marmoset (Callithrix jacchus). Hear Res 2023; 430:108722. [PMID: 36863289 DOI: 10.1016/j.heares.2023.108722] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 02/03/2023] [Accepted: 02/10/2023] [Indexed: 02/14/2023]
Abstract
The common marmoset (Callithrix jacchus) is a small arboreal New World primate which has emerged as a promising model in auditory neuroscience. One potentially useful application of this model system is in the study of the neural mechanism underlying spatial hearing in primate species, as the marmosets need to localize sounds to orient their head to events of interest and identify their vocalizing conspecifics that are not visible. However, interpretation of neurophysiological data on sound localization requires an understanding of perceptual abilities, and the sound localization behavior of marmosets has not been well studied. The present experiment measured sound localization acuity using an operant conditioning procedure in which marmosets were trained to discriminate changes in sound location in the horizontal (azimuth) or vertical (elevation) dimension. Our results showed that the minimum audible angle (MAA) for horizontal and vertical discrimination was 13.17° and 12.53°, respectively, for 2 to 32 kHz Gaussian noise. Removing the monaural spectral cues tended to increase the horizontal localization acuity (11.31°). Marmosets have larger horizontal MAA (15.54°) in the rear than the front. Removing the high-frequency (> 26 kHz) region of the head-related transfer function (HRTF) affected vertical acuity mildly (15.76°), but removing the first notch (12-26 kHz) region of HRTF substantially reduced the vertical acuity (89.01°). In summary, our findings indicate that marmosets' spatial acuity is on par with other species of similar head size and field of best vision, and they do not appear to use monaural spectral cues for horizontal discrimination but rely heavily on first notch region of HRTF for vertical discrimination.
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Affiliation(s)
- Chenggang Chen
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 720 Rutland Ave., Traylor 410, Baltimore, MD 21025, United States
| | - Evan D Remington
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 720 Rutland Ave., Traylor 410, Baltimore, MD 21025, United States
| | - Xiaoqin Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 720 Rutland Ave., Traylor 410, Baltimore, MD 21025, United States.
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9
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Terada SI, Matsuzaki M. Silent microscopy to explore a brain that hears butterflies' wings. LIGHT, SCIENCE & APPLICATIONS 2022; 11:140. [PMID: 35577797 PMCID: PMC9110353 DOI: 10.1038/s41377-022-00843-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A silent two-photon laser-scanning microscopy system, which eliminates mechanical vibrations in the audible range, has enabled the detection of auditory cortical neurons with responses at sound pressure levels as low as 5 dB in nonhuman primates.
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Affiliation(s)
- Shin-Ichiro Terada
- Department of Physiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masanori Matsuzaki
- Department of Physiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Brain Functional Dynamics Collaboration Laboratory, RIKEN Center for Brain Science, Saitama, Japan.
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, Tokyo, Japan.
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10
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Song X, Guo Y, Li H, Chen C, Lee JH, Zhang Y, Schmidt Z, Wang X. Mesoscopic landscape of cortical functions revealed by through-skull wide-field optical imaging in marmoset monkeys. Nat Commun 2022; 13:2238. [PMID: 35474064 PMCID: PMC9042927 DOI: 10.1038/s41467-022-29864-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/04/2022] [Indexed: 12/15/2022] Open
Abstract
The primate cerebral cortex is organized into specialized areas representing different modalities and functions along a continuous surface. The functional maps across the cortex, however, are often investigated a single modality at a time (e.g., audition or vision). To advance our understanding of the complex landscape of primate cortical functions, here we develop a polarization-gated wide-field optical imaging method for measuring cortical functions through the un-thinned intact skull in awake marmoset monkeys (Callithrix jacchus), a primate species featuring a smooth cortex. Using this method, adjacent auditory, visual, and somatosensory cortices are noninvasively parcellated in individual subjects with detailed tonotopy, retinotopy, and somatotopy. An additional pure-tone-responsive tonotopic gradient is discovered in auditory cortex and a face-patch sensitive to motion in the lower-center visual field is localized near an auditory region representing frequencies of conspecific vocalizations. This through-skull landscape-mapping approach provides new opportunities for understanding how the primate cortex is organized and coordinated to enable real-world behaviors.
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Affiliation(s)
- Xindong Song
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Yueqi Guo
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Hongbo Li
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Chenggang Chen
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Jong Hoon Lee
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Yang Zhang
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Zachary Schmidt
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Xiaoqin Wang
- grid.21107.350000 0001 2171 9311Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
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