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Georgiadis M, Auf der Heiden F, Abbasi H, Ettema L, Nirschl J, Moein Taghavi H, Wakatsuki M, Liu A, Ho WHD, Carlson M, Doukas M, Koppes SA, Keereweer S, Sobel RA, Setsompop K, Liao C, Amunts K, Axer M, Zeineh M, Menzel M. Micron-resolution fiber mapping in histology independent of sample preparation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586745. [PMID: 38585744 PMCID: PMC10996646 DOI: 10.1101/2024.03.26.586745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Detailed knowledge of the brain's nerve fiber network is crucial for understanding its function in health and disease. However, mapping fibers with high resolution remains prohibitive in most histological sections because state-of-the-art techniques are incompatible with their preparation. Here, we present a micron-resolution light-scattering-based technique that reveals intricate fiber networks independent of sample preparation for extended fields of view. We uncover fiber structures in both label-free and stained, paraffin-embedded and deparaffinized, newly-prepared and archived, animal and human brain tissues - including whole-brain sections from the BigBrain atlas. We identify altered microstructures in demyelination and hippocampal neurodegeneration, and show key advantages over diffusion magnetic resonance imaging, polarization microscopy, and structure tensor analysis. We also reveal structures in non-brain tissues - including muscle, bone, and blood vessels. Our cost-effective, versatile technique enables studies of intricate fiber networks in any type of histological tissue section, offering a new dimension to neuroscientific and biomedical research.
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Yu L, Chen Z, Zhou X, Teng F, Bai QR, Li L, Li Y, Liu Y, Zeng Q, Wang Y, Wang M, Xu Y, Tang X, Wang X. KARS Mutations Impair Brain Myelination by Inducing Oligodendrocyte Deficiency: One Potential Mechanism and Improvement by Melatonin. J Pineal Res 2024; 76:e12998. [PMID: 39087379 DOI: 10.1111/jpi.12998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 07/08/2024] [Accepted: 07/19/2024] [Indexed: 08/02/2024]
Abstract
It is very crucial to investigate key molecules that are involved in myelination to gain an understanding of brain development and injury. We have reported for the first time that pathogenic variants p.R477H and p.P505S in KARS, which encodes lysyl-tRNA synthetase (LysRS), cause leukoencephalopathy with progressive cognitive impairment in humans. The role and action mechanisms of KARS in brain myelination during development are unknown. Here, we first generated Kars knock-in mouse models through the CRISPR-Cas9 system. Kars knock-in mice displayed significant cognitive deficits. These mice also showed significantly reduced myelin density and content, as well as significantly decreased myelin thickness during development. In addition, Kars mutations significantly induced oligodendrocyte differentiation arrest and reduction in the brain white matter of mice. Mechanically, oligodendrocytes' significantly imbalanced expression of differentiation regulators and increased capase-3-mediated apoptosis were observed in the brain white matter of Kars knock-in mice. Furthermore, Kars mutations significantly reduced the aminoacylation and steady-state level of mitochondrial tRNALys and decreased the protein expression of subunits of oxidative phosphorylation complexes in the brain white matter. Kars knock-in mice showed decreased activity of complex IV and significantly reduced ATP production and increased reactive oxygen species in the brain white matter. Significantly increased percentages of abnormal mitochondria and mitochondrion area were observed in the oligodendrocytes of Kars knock-in mouse brain. Finally, melatonin (a mitochondrion protectant) significantly attenuated mitochondrion and oligodendrocyte deficiency in the brain white matter of KarsR504H/P532S mice. The mice treated with melatonin also showed significantly restored myelination and cognitive function. Our study first establishes Kars knock-in mammal models of leukoencephalopathy and cognitive impairment and indicates important roles of KARS in the regulation of mitochondria, oligodendrocyte differentiation and survival, and myelination during brain development and application prospects of melatonin in KARS (or even aaRS)-related diseases.
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Affiliation(s)
- Lijia Yu
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zhilin Chen
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaolong Zhou
- State Key Laboratory of Molecular Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Fei Teng
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Qing-Ran Bai
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Lixi Li
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunhong Li
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ying Liu
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Qiyu Zeng
- State Key Laboratory of Molecular Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Yong Wang
- State Key Laboratory of Molecular Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Meihua Wang
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurosurgery & Neurocritical Care, Huashan Hospital Affiliated to Fudan University, Shanghai, China
| | - Yaling Xu
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaohui Tang
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xijin Wang
- Department of Neurology, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024; 69:2241-2259. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [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: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
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Affiliation(s)
- Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
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Oeschger JM, Tabelow K, Mohammadi S. Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates. Magn Reson Med 2024; 92:69-81. [PMID: 38308141 DOI: 10.1002/mrm.30034] [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: 06/07/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 02/04/2024]
Abstract
PURPOSE The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation. METHODS Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model. RESULTS The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects. CONCLUSION For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.
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Affiliation(s)
- Jan Malte Oeschger
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karsten Tabelow
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Sachsen, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
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Liu Z, Feng Z, Liu G, Li A, Gong H, Yang X, Li X. A complementary approach for neocortical cytoarchitecture inspection with cellular resolution imaging at whole brain scale. Front Neuroanat 2024; 18:1388084. [PMID: 38846539 PMCID: PMC11153794 DOI: 10.3389/fnana.2024.1388084] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/26/2024] [Indexed: 06/09/2024] Open
Abstract
Cytoarchitecture, the organization of cells within organs and tissues, serves as a crucial anatomical foundation for the delineation of various regions. It enables the segmentation of the cortex into distinct areas with unique structural and functional characteristics. While traditional 2D atlases have focused on cytoarchitectonic mapping of cortical regions through individual sections, the intricate cortical gyri and sulci demands a 3D perspective for unambiguous interpretation. In this study, we employed fluorescent micro-optical sectioning tomography to acquire architectural datasets of the entire macaque brain at a resolution of 0.65 μm × 0.65 μm × 3 μm. With these volumetric data, the cortical laminar textures were remarkably presented in appropriate view planes. Additionally, we established a stereo coordinate system to represent the cytoarchitectonic information as surface-based tomograms. Utilizing these cytoarchitectonic features, we were able to three-dimensionally parcel the macaque cortex into multiple regions exhibiting contrasting architectural patterns. The whole-brain analysis was also conducted on mice that clearly revealed the presence of barrel cortex and reflected biological reasonability of this method. Leveraging these high-resolution continuous datasets, our method offers a robust tool for exploring the organizational logic and pathological mechanisms of the brain's 3D anatomical structure.
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Affiliation(s)
- Zhixiang Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
| | - Zhao Feng
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Guangcai Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, 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, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 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, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Xiaoquan Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Xiangning Li
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
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Wang Y, Xiong Z, Zhang Q, Liu M, Zhang J, Qi X, Jiang X, Yu W. Acetyl-11-Keto-β-Boswellic Acid Accelerates the Repair of Spinal Cord Injury in Rats by Resisting Neuronal Pyroptosis with Nrf2. Int J Mol Sci 2023; 25:358. [PMID: 38203528 PMCID: PMC10779011 DOI: 10.3390/ijms25010358] [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: 10/29/2023] [Revised: 12/14/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
The primary aim of this study is to delve into the potential of Acetyl-11-keto-β-boswellic acid (AKBA) in ameliorating neuronal damage induced by acute spinal cord injury, as well as to unravel the intricate underlying mechanisms. A cohort of 40 Sprague-Dawley rats was meticulously categorized into four groups. Following a seven-day oral administration of AKBA, damaged spinal cord samples were meticulously procured for Nissl staining and electron microscopy to assess neuronal demise. Employing ELISA, immunofluorescence, Western blot (WB), and quantitative polymerase chain reaction (qPCR), the modulatory effects of AKBA within the context of spinal cord injury were comprehensively evaluated. Furthermore, employing an ex vivo extraction of spinal cord neurons, an ATP + LPS-induced pyroptotic injury model was established. The model was subsequently subjected to Nrf2 inhibition, followed by a battery of assessments involving ELISA, DCFH-DA staining, flow cytometry, immunofluorescence, and WB to decipher the effects of AKBA on the spinal cord neuron pyroptosis model. By engaging the Nrf2-ROS-NLRP3 pathway, AKBA exerted a repressive influence on the expression of the pyroptotic initiator protein Caspase-1, thereby mitigating the release of GSDMD and alleviating pyroptosis. Additionally, AKBA demonstrated the ability to attenuate the release of IL-18 and IL-1β, curbing neuronal loss and expediting the restorative processes within the context of spinal cord injury. Our study elucidates that AKBA can reduce spinal cord neuronal apoptosis, providing a basis for the development of AKBA as a clinical treatment for spinal cord injury.
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Affiliation(s)
- Yao Wang
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
| | - Zongliang Xiong
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
| | - Qiyuan Zhang
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
| | - Mengmeng Liu
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
| | - Jingjing Zhang
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
| | - Xinyue Qi
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
| | - Xiaowen Jiang
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
| | - Wenhui Yu
- Department of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; (Y.W.); (Z.X.); (Q.Z.); (M.L.); (J.Z.); (X.Q.)
- Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, Northeast Agricultural University, Harbin 150030, China
- Institute of Chinese Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China
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Jiang T, Wang J, Wang Y, Jiang J, Zhou J, Wang X, Zhang D, Xu J. Mitochondrial protein prohibitin promotes learning memory recovery in mice following intracerebral hemorrhage via CAMKII/CRMP signaling pathway. Neurochem Int 2023; 171:105637. [PMID: 37923298 DOI: 10.1016/j.neuint.2023.105637] [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: 08/24/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/07/2023]
Abstract
Prohibitin (PHB) is a mitochondrial inner membrane protein with neuroprotective, antioxidant, and apoptosis-reducing effects. This study aimed to explore the role of PHB in pathological symptoms, behavioral deficits, and cognitive impairment in a collagenase-IV-induced intracerebral hemorrhage (ICH) murine model. In this study, mice that received collagenase IV injection were pretreated with PHB or saline 21 days prior to modeling. The role of PHB in memory and learning ability was monitored using the Morris water maze, Y-maze, and rotarod, social, startle, and nest-building tests. The effect of PHB on depression-like symptoms was examined using the forced swimming, tail suspension, and sucrose preference tests. Subsequently, mouse samples were analyzed using immunohistochemistry, western blotting, Perls staining, Nissl staining, and gene sequencing. Results showed that collagenase IV significantly induced behavioral deficits, brain edema, cognitive impairment, and depressive symptoms. PHB overexpression effectively alleviated memory, learning, and motor deficits in mice with ICH. PHB markedly inhibited the number of terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling-positive cells and protein levels of ionized calcium-binding adapter molecule 1, glial fibrillary acidic protein, and interleukin-1β in the perihematomal region of ICH mice. PHB overexpression also remarkably promoted production of neurologin1 (NLGL1), and upregulated levels of Ca2+-calmodulin-dependent kinase II (CaMKII) and collapsin response mediator protein-1 (CRMP1) proteins. In conclusion, PHB overexpression can effectively alleviate the neurological deficits and neurodegeneration around the hematoma region. This may play a protective role by upregulating the expression of NLGL1 and promoting expression of CaMKII and CRMP1.
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Affiliation(s)
- Tianlin Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225009, China
| | - Jiahua Wang
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225009, China; Department of Anesthesia, Affiliated Hospital of Yangzhou University, Yangzhou, 225001, China
| | - Yanli Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiwei Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiawei Zhou
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225009, China; Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou University, YangZhou, 225001, China
| | - Xiaohong Wang
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225009, China; Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou University, YangZhou, 225001, China.
| | - Deke Zhang
- Department of Pharmacy, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia district, Jinan City, Shandong Province, China.
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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8
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Liu CJ, Ammon W, Jones RJ, Nolan JC, Gong D, Maffei C, Edlow BL, Augustinack JC, Magnain C, Yendiki A, Villiger M, Fischl B, Wang H. Quantitative imaging of three-dimensional fiber orientation in the human brain via two illumination angles using polarization-sensitive optical coherence tomography. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563298. [PMID: 37961162 PMCID: PMC10634685 DOI: 10.1101/2023.10.20.563298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Optical imaging methods such as polarization-sensitive optical coherence tomography (PS-OCT) provide important tools to directly quantify fiber orientation at micrometer resolution. However, brain imaging based on the optic axis by PS-OCT so far has been limited to two-dimensional in-plane orientation, preventing the comprehensive study of connectivity in 3D. In this work, we present a novel method to obtain the 3D fiber orientation in full angular space with only two illumination angles. We measure the optic axis orientation and the apparent birefringence by PS-OCT from a normal and a 15 deg tilted illumination, and then apply a computational method yielding the 3D optic axis orientation and true birefringence. We verify that our method accurately recovers a large range of through-plane orientations from -85 deg to 85 deg with a high angular precision. We further present 3D fiber orientation maps of entire coronal sections of human cerebrum and brainstem with 10 μm in-plane resolution, revealing unprecedented details of fiber configurations. We envision that further development of our method will open a promising avenue towards large-scale 3D fiber axis mapping in the human brain and other complex fibrous tissues at microscopic level.
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9
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Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
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Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
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10
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Gkotsoulias DG, Müller R, Jäger C, Schlumm T, Mildner T, Eichner C, Pampel A, Jaffe J, Gräßle T, Alsleben N, Chen J, Crockford C, Wittig R, Liu C, Möller HE. High angular resolution susceptibility imaging and estimation of fiber orientation distribution functions in primate brain. Neuroimage 2023; 276:120202. [PMID: 37247762 DOI: 10.1016/j.neuroimage.2023.120202] [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: 10/19/2022] [Revised: 05/21/2023] [Accepted: 05/27/2023] [Indexed: 05/31/2023] Open
Abstract
Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.
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Affiliation(s)
- Dimitrios G Gkotsoulias
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Roland Müller
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Carsten Jäger
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Torsten Schlumm
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Toralf Mildner
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Cornelius Eichner
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - André Pampel
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jennifer Jaffe
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany; Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Côte d'Ivoire
| | - Tobias Gräßle
- Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Côte d'Ivoire; Helmholtz Institute for One Health, Greifswald, Germany; Robert Koch Institute, Epidemiology of Highly Pathogenic Microorganisms, Berlin, Germany
| | - Niklas Alsleben
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jingjia Chen
- Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Catherine Crockford
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany; Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Côte d'Ivoire; Institute of Cognitive Sciences, CNRS UMR5229 University of Lyon, Bron, France
| | - Roman Wittig
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany; Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Côte d'Ivoire; Institute of Cognitive Sciences, CNRS UMR5229 University of Lyon, Bron, France
| | - Chunlei Liu
- Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Harald E Möller
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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11
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Ma G, Worthy KH, Liu C, Rosa MG, Atapour N. Parvalbumin as a neurochemical marker of the primate optic radiation. iScience 2023; 26:106608. [PMID: 37168578 PMCID: PMC10165026 DOI: 10.1016/j.isci.2023.106608] [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: 12/23/2022] [Revised: 03/20/2023] [Accepted: 03/20/2023] [Indexed: 05/13/2023] Open
Abstract
Parvalbumin (PV) is a calcium-binding protein that labels neuronal cell bodies in the magno and parvocellular layers of the primate lateral geniculate nucleus (LGN). Here we demonstrate that PV immunohistochemistry can also be used to trace the optic radiation (OR) of the marmoset monkey (Callithrix jacchus) from its LGN origin to its destinations in the primary visual cortex (V1), thus providing a high-resolution method for identification of the OR with single axon resolution. The emergence of fibers from LGN, their entire course and even the entry points to V1 were clearly defined in coronal, parasagittal, and horizontal sections of marmoset brain. In all cases, the trajectory revealed by PV staining paralleled that defined by high-resolution diffusion tensor imaging (DTI). We found that V1 was the exclusive target for the PV-containing fibers, with abrupt transitions in staining observed in the white matter at the border with area V2, and no evidence of PV-labeled axons feeding into other visual areas. Changes in the pattern of PV staining in the OR were detected following V1 lesions, demonstrating that this method can be used to assess the progress of retrograde degeneration of geniculocortical projections. These results suggest a technically simple approach to advance our understanding of a major white matter structure, which provides a cellular resolution suitable for the detection of microstructural variations during development, health and disease. Understanding the relationship between PV staining and DTI in non-human primates may also offer clues for improving the specificity and sensitivity of OR tractography for clinical purposes.
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Affiliation(s)
- Gaoyuan Ma
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
| | - Katrina H. Worthy
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
| | - Cirong Liu
- Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Marcello G.P. Rosa
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
| | - Nafiseh Atapour
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
- Corresponding author
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12
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Menzel M, Gräßel D, Rajkovic I, Zeineh MM, Georgiadis M. Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI. eLife 2023; 12:e84024. [PMID: 37166005 PMCID: PMC10259419 DOI: 10.7554/elife.84024] [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: 10/07/2022] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Disentangling human brain connectivity requires an accurate description of nerve fiber trajectories, unveiled via detailed mapping of axonal orientations. However, this is challenging because axons can cross one another on a micrometer scale. Diffusion magnetic resonance imaging (dMRI) can be used to infer axonal connectivity because it is sensitive to axonal alignment, but it has limited spatial resolution and specificity. Scattered light imaging (SLI) and small-angle X-ray scattering (SAXS) reveal axonal orientations with microscopic resolution and high specificity, respectively. Here, we apply both scattering techniques on the same samples and cross-validate them, laying the groundwork for ground-truth axonal orientation imaging and validating dMRI. We evaluate brain regions that include unidirectional and crossing fibers in human and vervet monkey brain sections. SLI and SAXS quantitatively agree regarding in-plane fiber orientations including crossings, while dMRI agrees in the majority of voxels with small discrepancies. We further use SAXS and dMRI to confirm theoretical predictions regarding SLI determination of through-plane fiber orientations. Scattered light and X-ray imaging can provide quantitative micrometer 3D fiber orientations with high resolution and specificity, facilitating detailed investigations of complex fiber architecture in the animal and human brain.
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Affiliation(s)
- Miriam Menzel
- Department of Imaging Physics, Faculty of Applied Sciences, Delft University of TechnologyDelftNetherlands
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHJülichGermany
| | - David Gräßel
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHJülichGermany
| | - Ivan Rajkovic
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator LaboratoryStandfordUnited States
| | - Michael M Zeineh
- Department of Radiology, Stanford School of MedicineStanfordUnited States
| | - Marios Georgiadis
- Department of Radiology, Stanford School of MedicineStanfordUnited States
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13
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Oishi H, Takemura H, Amano K. Macromolecular tissue volume mapping of lateral geniculate nucleus subdivisions in living human brains. Neuroimage 2023; 265:119777. [PMID: 36462730 DOI: 10.1016/j.neuroimage.2022.119777] [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: 03/08/2022] [Revised: 11/26/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022] Open
Abstract
The lateral geniculate nucleus (LGN) is a key thalamic nucleus in the visual system, which has an important function in relaying retinal visual input to the visual cortex. The human LGN is composed mainly of magnocellular (M) and parvocellular (P) subdivisions, each of which has different stimulus selectivity in neural response properties. Previous studies have discussed the potential relationship between LGN subdivisions and visual disorders based on psychophysical data on specific types of visual stimuli. However, these relationships remain speculative because non-invasive measurements of these subdivisions are difficult due to the small size of the LGN. Here we propose a method to identify these subdivisions by combining two structural MR measures: high-resolution proton-density weighted images and macromolecular tissue volume (MTV) maps. We defined the M and P subdivisions based on MTV fraction data and tested the validity of the definition by (1) comparing the data with that from human histological studies, (2) comparing the data with functional magnetic resonance imaging measurements on stimulus selectivity, and (3) analyzing the test-retest reliability. The findings demonstrated that the spatial organization of the M and P subdivisions was consistent across subjects and in line with LGN subdivisions observed in human histological data. Moreover, the difference in stimulus selectivity between the subdivisions identified using MTV was consistent with previous physiology literature. The definition of the subdivisions based on MTV was shown to be robust over measurements taken on different days. These results suggest that MTV mapping is a promising approach for evaluating the tissue properties of LGN subdivisions in living humans. This method potentially will enable neuroscientific and clinical hypotheses about the human LGN subdivisions to be tested.
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Affiliation(s)
- Hiroki Oishi
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita 565-0871, Japan; Department of Psychology, University of California, Berkeley, Berkeley, CA 94704, United States.
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita 565-0871, Japan; Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki 444-8585, Japan; Department of Physiological Sciences, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Hayama 240-0193, Japan.
| | - Kaoru Amano
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita 565-0871, Japan; Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
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14
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Reveley C, Ye FQ, Mars RB, Matrov D, Chudasama Y, Leopold DA. Diffusion MRI anisotropy in the cerebral cortex is determined by unmyelinated tissue features. Nat Commun 2022; 13:6702. [PMID: 36335105 PMCID: PMC9637141 DOI: 10.1038/s41467-022-34328-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/19/2022] [Indexed: 11/07/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is commonly used to assess the tissue and cellular substructure of the human brain. In the white matter, myelinated axons are the principal neural elements that shape dMRI through the restriction of water diffusion; however, in the gray matter the relative contributions of myelinated axons and other tissue features to dMRI are poorly understood. Here we investigate the determinants of diffusion in the cerebral cortex. Specifically, we ask whether myelinated axons significantly shape dMRI fractional anisotropy (dMRI-FA), a measure commonly used to characterize tissue properties in humans. We compared ultra-high resolution ex vivo dMRI data from the brain of a marmoset monkey with both myelin- and Nissl-stained histological sections obtained from the same brain after scanning. We found that the dMRI-FA did not match the spatial distribution of myelin in the gray matter. Instead dMRI-FA was more closely related to the anisotropy of stained tissue features, most prominently those revealed by Nissl staining and to a lesser extent those revealed by myelin staining. Our results suggest that unmyelinated neurites such as large caliber apical dendrites are the primary features shaping dMRI measures in the cerebral cortex.
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Affiliation(s)
- Colin Reveley
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.12082.390000 0004 1936 7590Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ UK
| | - Frank Q. Ye
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA
| | - Rogier B. Mars
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Denis Matrov
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Yogita Chudasama
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - David A. Leopold
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA ,grid.94365.3d0000 0001 2297 5165Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
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15
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Thiebaut de Schotten M, Forkel SJ. The emergent properties of the connected brain. Science 2022; 378:505-510. [DOI: 10.1126/science.abq2591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
There is more to brain connections than the mere transfer of signals between brain regions. Behavior and cognition emerge through cortical area interaction. This requires integration between local and distant areas orchestrated by densely connected networks. Brain connections determine the brain’s functional organization. The imaging of connections in the living brain has provided an opportunity to identify the driving factors behind the neurobiology of cognition. Connectivity differences between species and among humans have furthered the understanding of brain evolution and of diverging cognitive profiles. Brain pathologies amplify this variability through disconnections and, consequently, the disintegration of cognitive functions. The prediction of long-term symptoms is now preferentially based on brain disconnections. This paradigm shift will reshape our brain maps and challenge current brain models.
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Affiliation(s)
- Michel Thiebaut de Schotten
- Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
| | - Stephanie J. Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
- Donders Centre for Brain Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- Department of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
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16
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Axer M, Amunts K. Scale matters: The nested human connectome. Science 2022; 378:500-504. [DOI: 10.1126/science.abq2599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A comprehensive description of how neurons and entire brain regions are interconnected is fundamental for a mechanistic understanding of brain function and dysfunction. Neuroimaging has shaped the way to approaching the human brain’s connectivity on the basis of diffusion magnetic resonance imaging and tractography. At the same time, polarization, fluorescence, and electron microscopy became available, which pushed spatial resolution and sensitivity to the axonal or even to the synaptic level. New methods are mandatory to inform and constrain whole-brain tractography by regional, high-resolution connectivity data and local fiber geometry. Machine learning and simulation can provide predictions where experimental data are missing. Future interoperable atlases require new concepts, including high-resolution templates and directionality, to represent variants of tractography solutions and estimates of their accuracy.
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Affiliation(s)
- Markus Axer
- Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Department of Physics, School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany
| | - Katrin Amunts
- Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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17
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Fang S, Weng S, Li L, Guo Y, Fan X, Zhang Z, Jiang T, Wang Y. Association of homotopic areas in the right hemisphere with language deficits in the short term after tumor resection. J Neurosurg 2022; 138:1206-1215. [PMID: 36308477 DOI: 10.3171/2022.9.jns221475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/08/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE
It is important to identify language deficit and recovery in the week following a tumor resection procedure. The homotopic Broca’s area and the superior longitudinal fasciculus in the right hemisphere participate in language functional compensation. However, the nodes in these structures, as well as their contributions to language rehabilitation, remain unknown. In this study, the authors investigated the association of homotopic areas in the right hemisphere with language deficit.
METHODS
The authors retrospectively reviewed the records of 50 right-handed patients with left hemispheric lower-grade glioma that had been surgically treated between June 2020 and May 2022. The patients were divided into normal and aphasia groups based on their postoperative aphasia quotient (AQ) from the Western Aphasia Battery. Preoperative (within 24 hours before surgery) and postoperative (7 days after tumor resection) diffusion tensor images were used to reveal alterations of structural networks by using graphic theory analysis. The shortest distance between the glioma and the nodes belonging to the language network (SDTN) was quantitatively assessed. Pearson’s correlation and causal mediation analyses were used to identify correlations and mediator factors among SDTN, topological properties, and AQs.
RESULTS
Postoperative nodal local efficiency of the node dorsal Brodmann area (BA) 44 (A44d; p = 0.0330), nodal clustering coefficient of the nodes A44d (p = 0.0402) and dorsal lateral BA6 (A6dl; p = 0.0097), and nodal degree centrality (p = 0.0058) of the node medial BA7 (A7m) were higher in the normal group than in the aphasia group. SDTN was positively correlated with postoperative AQ (r = 0.457, p = 0.0009) and ΔAQ (r = 0.588, p < 0.0001). The nodal local efficiency of node A44d and the nodal efficiency, nodal betweenness centrality, and degree centrality of node A7m were mediators of SDTN and postoperative AQs.
CONCLUSIONS
The decreased ability of nodes A44d, A6dl, and A7m to convey information in the right hemisphere was associated with short-term language deficits after tumor resection. A smaller SDTN induced a worsened postoperative language deficit through a significant decrease in the ability to convey information from these three nodes.
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Affiliation(s)
- Shengyu Fang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing
- Beijing Neurosurgical Institute, Capital Medical University, Beijing; and
| | - Shimeng Weng
- Beijing Neurosurgical Institute, Capital Medical University, Beijing; and
| | - Lianwang Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing; and
| | - Yuhao Guo
- Beijing Neurosurgical Institute, Capital Medical University, Beijing; and
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing; and
| | - Zhong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing
- Research Unit of Accurate Diagnosis, Treatment, and Translational Medicine of Brain Tumors, Chinese Academy of Medical Sciences, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing
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18
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Alkemade A, Bazin PL, Balesar R, Pine K, Kirilina E, Möller HE, Trampel R, Kros JM, Keuken MC, Bleys RLAW, Swaab DF, Herrler A, Weiskopf N, Forstmann BU. A unified 3D map of microscopic architecture and MRI of the human brain. SCIENCE ADVANCES 2022; 8:eabj7892. [PMID: 35476433 PMCID: PMC9045605 DOI: 10.1126/sciadv.abj7892] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
We present the first three-dimensional (3D) concordance maps of cyto- and fiber architecture of the human brain, combining histology, immunohistochemistry, and 7-T quantitative magnetic resonance imaging (MRI), in two individual specimens. These 3D maps each integrate data from approximately 800 microscopy sections per brain, showing neuronal and glial cell bodies, nerve fibers, and interneuronal populations, as well as ultrahigh-field quantitative MRI, all coaligned at the 200-μm scale to the stacked blockface images obtained during sectioning. These unprecedented 3D multimodal datasets are shared without any restrictions and provide a unique resource for the joint study of cell and fiber architecture of the brain, detailed anatomical atlasing, or modeling of the microscopic underpinnings of MRI contrasts.
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Affiliation(s)
- Anneke Alkemade
- Integrative Model-Based Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
- Corresponding author. (A.A.); (B.U.F.)
| | - Pierre-Louis Bazin
- Integrative Model-Based Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Rawien Balesar
- Department of Neuropsychiatric disorders, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands
| | - Kerrin Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Neurocomputation and Neuroimaging Unit, Department of Psychology and Educational Science, Free University Berlin, Habelschwerdter Allee 45, Berlin 14195, Germany
| | - Harald E. Möller
- NMR Methods Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Johan M. Kros
- Department of Pathology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Max C. Keuken
- Integrative Model-Based Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
| | - Ronald L. A. W. Bleys
- Department of Anatomy, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Dick F. Swaab
- Department of Neuropsychiatric disorders, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands
| | - Andreas Herrler
- Department of Anatomy and Embryology, Maastricht University, Maastricht, Netherlands
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, Leipzig 04103, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Birte U. Forstmann
- Integrative Model-Based Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
- Corresponding author. (A.A.); (B.U.F.)
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19
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Kiwitz K, Brandstetter A, Schiffer C, Bludau S, Mohlberg H, Omidyeganeh M, Massicotte P, Amunts K. Cytoarchitectonic Maps of the Human Metathalamus in 3D Space. Front Neuroanat 2022; 16:837485. [PMID: 35350721 PMCID: PMC8957853 DOI: 10.3389/fnana.2022.837485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
The human metathalamus plays an important role in processing visual and auditory information. Understanding its layers and subdivisions is important to gain insights in its function as a subcortical relay station and involvement in various pathologies. Yet, detailed histological references of the microanatomy in 3D space are still missing. We therefore aim at providing cytoarchitectonic maps of the medial geniculate body (MGB) and its subdivisions in the BigBrain – a high-resolution 3D-reconstructed histological model of the human brain, as well as probabilistic cytoarchitectonic maps of the MGB and lateral geniculate body (LGB). Therefore, histological sections of ten postmortem brains were studied. Three MGB subdivisions (MGBv, MGBd, MGBm) were identified on every 5th BigBrain section, and a deep-learning based tool was applied to map them on every remaining section. The maps were 3D-reconstructed to show the shape and extent of the MGB and its subdivisions with cellular precision. The LGB and MGB were additionally identified in nine other postmortem brains. Probabilistic cytoarchitectonic maps in the MNI “Colin27” and MNI ICBM152 reference spaces were computed which reveal an overall low interindividual variability in topography and extent. The probabilistic maps were included into the Julich-Brain atlas, and are freely available. They can be linked to other 3D data of human brain organization and serve as an anatomical reference for diagnostic, prognostic and therapeutic neuroimaging studies of healthy brains and patients. Furthermore, the high-resolution MGB BigBrain maps provide a basis for data integration, brain modeling and simulation to bridge the larger scale involvement of thalamocortical and local subcortical circuits.
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Affiliation(s)
- Kai Kiwitz
- Cécile and Oskar Vogt Institute of Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstraße 1a, Leipzig, Germany
- *Correspondence: Kai Kiwitz,
| | - Andrea Brandstetter
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Christian Schiffer
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
- Helmholtz AI, Forschungszentrum Jülich, Jülich, Germany
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Mona Omidyeganeh
- McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- National Research Council of Canada, Ottawa, ON, Canada
| | - Philippe Massicotte
- McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Katrin Amunts
- Cécile and Oskar Vogt Institute of Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstraße 1a, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
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20
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Song M, Yang Z, Jiang T. Multimodal Brain Imaging Fusion for the White-Matter Fiber Architecture in the Human Brain. Neurosci Bull 2022; 38:561-564. [PMID: 35099675 PMCID: PMC9106764 DOI: 10.1007/s12264-022-00822-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 12/12/2021] [Indexed: 12/22/2022] Open
Affiliation(s)
- Ming Song
- National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190, China
- Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhengyi Yang
- National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190, China
- Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianzi Jiang
- National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190, China.
- Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190, China.
- University of the Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, 100190, China.
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China.
- The Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia.
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21
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Bonetto G, Belin D, Káradóttir RT. Myelin: A gatekeeper of activity-dependent circuit plasticity? Science 2021; 374:eaba6905. [PMID: 34618550 DOI: 10.1126/science.aba6905] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Giulia Bonetto
- Wellcome-Medical Research Council Cambridge Stem Cell Institute and Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - David Belin
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Ragnhildur Thóra Káradóttir
- Wellcome-Medical Research Council Cambridge Stem Cell Institute and Department of Veterinary Medicine, University of Cambridge, Cambridge, UK.,Department of Physiology, Biomedical Centre, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
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