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Hille M, Kühn S, Kempermann G, Bonhoeffer T, Lindenberger U. From animal models to human individuality: Integrative approaches to the study of brain plasticity. Neuron 2024; 112:3522-3541. [PMID: 39461332 DOI: 10.1016/j.neuron.2024.10.006] [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: 04/29/2024] [Revised: 06/02/2024] [Accepted: 10/04/2024] [Indexed: 10/29/2024]
Abstract
Plasticity allows organisms to form lasting adaptive changes in neural structures in response to interactions with the environment. It serves both species-general functions and individualized skill acquisition. To better understand human plasticity, we need to strengthen the dialogue between human research and animal models. Therefore, we propose to (1) enhance the interpretability of macroscopic methods used in human research by complementing molecular and fine-structural measures used in animals with such macroscopic methods, preferably applied to the same animals, to create macroscopic metrics common to both examined species; (2) launch dedicated cross-species research programs, using either well-controlled experimental paradigms, such as motor skill acquisition, or more naturalistic environments, where individuals of either species are observed in their habitats; and (3) develop conceptual and computational models linking molecular and fine-structural events to phenomena accessible by macroscopic methods. In concert, these three component strategies can foster new insights into the nature of plastic change.
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Affiliation(s)
- Maike Hille
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Center for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.
| | - Simone Kühn
- Center for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany; Clinic and Policlinic for Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Hamburg, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK
| | - Gerd Kempermann
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany; CRTD - Center for Regenerative Therapies Dresden, TU Dresden, Dresden, Germany
| | - Tobias Bonhoeffer
- Synapses-Circuits-Plasticity, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK.
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2
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Assimopoulos S, Warrington S, Bryant KL, Pszczolkowski S, Jbabdi S, Mars RB, Sotiropoulos SN. Generalising XTRACT tractography protocols across common macaque brain templates. Brain Struct Funct 2024; 229:1873-1888. [PMID: 38388696 PMCID: PMC11485040 DOI: 10.1007/s00429-024-02760-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/09/2024] [Indexed: 02/24/2024]
Abstract
Non-human primates are extensively used in neuroscience research as models of the human brain, with the rhesus macaque being a prominent example. We have previously introduced a set of tractography protocols (XTRACT) for reconstructing 42 corresponding white matter (WM) bundles in the human and the macaque brain and have shown cross-species comparisons using such bundles as WM landmarks. Our original XTRACT protocols were developed using the F99 macaque brain template. However, additional macaque template brains are becoming increasingly common. Here, we generalise the XTRACT tractography protocol definitions across five macaque brain templates, including the F99, D99, INIA, Yerkes and NMT. We demonstrate equivalence of such protocols in two ways: (a) Firstly by comparing the bodies of the tracts derived using protocols defined across the different templates considered, (b) Secondly by comparing the projection patterns of the reconstructed tracts across the different templates in two cross-species (human-macaque) comparison tasks. The results confirm similarity of all predictions regardless of the macaque brain template used, providing direct evidence for the generalisability of these tractography protocols across the five considered templates.
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Affiliation(s)
- Stephania Assimopoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Katherine L Bryant
- Laboratoire de Psychologie Cognitive, Aix-Marseille Université, Marseille, France
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stefan Pszczolkowski
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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3
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Mars RB, Palomero-Gallagher N. Towards multi-modal, multi-species brain atlases: part two. Brain Struct Funct 2024; 229:1769-1772. [PMID: 39343839 DOI: 10.1007/s00429-024-02858-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Affiliation(s)
- Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
- C. & O. Vogt Institute for Brain Research, Heinrich Heine University, Dusseldorf, Germany
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4
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van Hout ATB, van Heukelum S, Rushworth MFS, Grandjean J, Mars RB. Comparing mouse and human cingulate cortex organization using functional connectivity. Brain Struct Funct 2024; 229:1913-1925. [PMID: 38739155 PMCID: PMC11485145 DOI: 10.1007/s00429-024-02773-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/30/2024] [Indexed: 05/14/2024]
Abstract
The subdivisions of the extended cingulate cortex of the human brain are implicated in a number of high-level behaviors and affected by a range of neuropsychiatric disorders. Its anatomy, function, and response to therapeutics are often studied using non-human animals, including the mouse. However, the similarity of human and mouse frontal cortex, including cingulate areas, is still not fully understood. Some accounts emphasize resemblances between mouse cingulate cortex and human cingulate cortex while others emphasize similarities with human granular prefrontal cortex. We use comparative neuroimaging to study the connectivity of the cingulate cortex in the mouse and human, allowing comparisons between mouse 'gold standard' tracer and imaging data, and, in addition, comparison between the mouse and the human using comparable imaging data. We find overall similarities in organization of the cingulate between species, including anterior and midcingulate areas and a retrosplenial area. However, human cingulate contains subareas with a more fine-grained organization than is apparent in the mouse and it has connections to prefrontal areas not present in the mouse. Results such as these help formally address between-species brain organization and aim to improve the translation from preclinical to human results.
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Affiliation(s)
- Aran T B van Hout
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Sabrina van Heukelum
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Joanes Grandjean
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
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5
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Royer J, Paquola C, Valk SL, Kirschner M, Hong SJ, Park BY, Bethlehem RAI, Leech R, Yeo BTT, Jefferies E, Smallwood J, Margulies D, Bernhardt BC. Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community. Neuroinformatics 2024; 22:623-634. [PMID: 38568476 DOI: 10.1007/s12021-024-09660-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2024] [Indexed: 11/21/2024]
Abstract
Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends - or gradients - in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization.
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Affiliation(s)
- Jessica Royer
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - Casey Paquola
- Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Matthias Kirschner
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Thonex, Switzerland
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Center for the Developing Brain, Child Mind Institute, New York, USA
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Data Science, Inha University, Incheon, South Korea
- Department of Statistics and Data Science, Inha University, Incheon, South Korea
| | | | - Robert Leech
- Centre for Neuroimaging Science, King's College London, London, UK
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Daniel Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | - Boris C Bernhardt
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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6
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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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7
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Eichert N, DeKraker J, Howard AFD, Huszar IN, Zhu S, Sallet J, Miller KL, Mars RB, Jbabdi S, Bernhardt BC. Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain. Nat Commun 2024; 15:5963. [PMID: 39013855 PMCID: PMC11252401 DOI: 10.1038/s41467-024-49823-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 06/17/2024] [Indexed: 07/18/2024] Open
Abstract
While the hippocampus is key for human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, which shows anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species followed an anterior-posterior axis, we observed a marked reconfiguration in the latter across species, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Here we show that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding within heteromodal association networks.
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Affiliation(s)
- Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
| | - Jordan DeKraker
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Silei Zhu
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- INSERM U1208 Stem Cell and Brain Research Institute, Univ Lyon, Bron, France
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Boris C Bernhardt
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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8
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Luppi AI, Rosas FE, Mediano PAM, Demertzi A, Menon DK, Stamatakis EA. Unravelling consciousness and brain function through the lens of time, space, and information. Trends Neurosci 2024; 47:551-568. [PMID: 38824075 DOI: 10.1016/j.tins.2024.05.007] [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: 02/15/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; St John's College, University of Cambridge, Cambridge, UK; Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK.
| | - Fernando E Rosas
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK; Center for Psychedelic Research, Imperial College London, London, UK
| | | | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liège, Liège 4000, Belgium; Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège 4000, Belgium; National Fund for Scientific Research (FNRS), Brussels 1000, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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9
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Wang Y, Cheng L, Li D, Lu Y, Wang C, Wang Y, Gao C, Wang H, Vanduffel W, Hopkins WD, Sherwood CC, Jiang T, Chu C, Fan L. Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597252. [PMID: 38895242 PMCID: PMC11185649 DOI: 10.1101/2024.06.03.597252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.
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Affiliation(s)
- Yufan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Luqi Cheng
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, 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 100190, China
| | - 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 100190, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yaping Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Chaohong Gao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Department of Neurosciences, Laboratory of Neuro- and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium
| | - Wim Vanduffel
- Department of Neurosciences, Laboratory of Neuro- and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium
- Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
| | - William D. Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX 78602, USA
| | - Chet C. Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC 20052, USA
| | - 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 100190, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, 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 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266000, China
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10
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Lazari A, Tachrount M, Valverde JM, Papp D, Beauchamp A, McCarthy P, Ellegood J, Grandjean J, Johansen-Berg H, Zerbi V, Lerch JP, Mars RB. The mouse motor system contains multiple premotor areas and partially follows human organizational principles. Cell Rep 2024; 43:114191. [PMID: 38717901 DOI: 10.1016/j.celrep.2024.114191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/10/2023] [Accepted: 04/17/2024] [Indexed: 06/01/2024] Open
Abstract
While humans are known to have several premotor cortical areas, secondary motor cortex (M2) is often considered to be the only higher-order motor area of the mouse brain and is thought to combine properties of various human premotor cortices. Here, we show that axonal tracer, functional connectivity, myelin mapping, gene expression, and optogenetics data contradict this notion. Our analyses reveal three premotor areas in the mouse, anterior-lateral motor cortex (ALM), anterior-lateral M2 (aM2), and posterior-medial M2 (pM2), with distinct structural, functional, and behavioral properties. By using the same techniques across mice and humans, we show that ALM has strikingly similar functional and microstructural properties to human anterior ventral premotor areas and that aM2 and pM2 amalgamate properties of human pre-SMA and cingulate cortex. These results provide evidence for the existence of multiple premotor areas in the mouse and chart a comparative map between the motor systems of humans and mice.
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Affiliation(s)
- Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Mohamed Tachrount
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Juan Miguel Valverde
- DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70150 Kuopio, Finland
| | - Daniel Papp
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Antoine Beauchamp
- Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Joanes Grandjean
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Heidi Johansen-Berg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering (STI), EPFL, 1015 Lausanne, Switzerland; CIBM Center for Biomedical Imaging, 1015 Lausanne, Switzerland
| | - Jason P Lerch
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
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11
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Pagani M, Gutierrez-Barragan D, de Guzman AE, Xu T, Gozzi A. Mapping and comparing fMRI connectivity networks across species. Commun Biol 2023; 6:1238. [PMID: 38062107 PMCID: PMC10703935 DOI: 10.1038/s42003-023-05629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.
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Affiliation(s)
- Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Autism Center, Child Mind Institute, New York, NY, USA
- IMT School for Advanced Studies, Lucca, Italy
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - A Elizabeth de Guzman
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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12
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Magielse N, Heuer K, Toro R, Schutter DJLG, Valk SL. A Comparative Perspective on the Cerebello-Cerebral System and Its Link to Cognition. CEREBELLUM (LONDON, ENGLAND) 2023; 22:1293-1307. [PMID: 36417091 PMCID: PMC10657313 DOI: 10.1007/s12311-022-01495-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/11/2022] [Indexed: 11/24/2022]
Abstract
The longstanding idea that the cerebral cortex is the main neural correlate of human cognition can be elaborated by comparative analyses along the vertebrate phylogenetic tree that support the view that the cerebello-cerebral system is suited to support non-motor functions more generally. In humans, diverse accounts have illustrated cerebellar involvement in cognitive functions. Although the neocortex, and its transmodal association cortices such as the prefrontal cortex, have become disproportionately large over primate evolution specifically, human neocortical volume does not appear to be exceptional relative to the variability within primates. Rather, several lines of evidence indicate that the exceptional volumetric increase of the lateral cerebellum in conjunction with its connectivity with the cerebral cortical system may be linked to non-motor functions and mental operation in primates. This idea is supported by diverging cerebello-cerebral adaptations that potentially coevolve with cognitive abilities across other vertebrates such as dolphins, parrots, and elephants. Modular adaptations upon the vertebrate cerebello-cerebral system may thus help better understand the neuroevolutionary trajectory of the primate brain and its relation to cognition in humans. Lateral cerebellar lobules crura I-II and their reciprocal connections to the cerebral cortical association areas appear to have substantially expanded in great apes, and humans. This, along with the notable increase in the ventral portions of the dentate nucleus and a shift to increased relative prefrontal-cerebellar connectivity, suggests that modular cerebellar adaptations support cognitive functions in humans. In sum, we show how comparative neuroscience provides new avenues to broaden our understanding of cerebellar and cerebello-cerebral functions in the context of cognition.
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Affiliation(s)
- Neville Magielse
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Center Jülich, Jülich, Germany
- Otto Hahn Cognitive Neurogenetics Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany
| | - Katja Heuer
- Institute Pasteur, Unité de Neuroanatomie Appliquée et Théorique, Université Paris Cité, Paris, France
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roberto Toro
- Institute Pasteur, Unité de Neuroanatomie Appliquée et Théorique, Université Paris Cité, Paris, France
| | - Dennis J L G Schutter
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Center Jülich, Jülich, Germany.
- Otto Hahn Cognitive Neurogenetics Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany.
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13
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Wang Y, Shojaie A, Randolph T, Knight P, Ma J. GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA. Ann Appl Stat 2023; 17:2944-2969. [PMID: 38149262 PMCID: PMC10751029 DOI: 10.1214/23-aoas1746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR) to two-way structured data, but unlike PCR, GMDR selects the components that are most predictive of the outcome, leading to more accurate prediction. For inference on regression coefficients of individual variables, we propose the generalized matrix decomposition inference (GMDI), a general high-dimensional inferential framework for a large family of estimators that include the proposed GMDR estimator. GMDI provides more flexibility for incorporating relevant auxiliary row and column structures. As a result, GMDI does not require the true regression coefficients to be sparse, but constrains the coordinate system representing the regression coefficients according to the column structure. GMDI also allows dependent and heteroscedastic observations. We study the theoretical properties of GMDI in terms of both the type-I error rate and power and demonstrate the effectiveness of GMDR and GMDI in simulation studies and an application to human microbiome data.
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Affiliation(s)
- Yue Wang
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus
| | - Ali Shojaie
- Department of Biostatistics, University of Washington
| | | | | | - Jing Ma
- Public Health Sciences Division, Fred Hutchinson Cancer Center
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14
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Karadachka K, Assem M, Mitchell DJ, Duncan J, Medendorp WP, Mars RB. Structural connectivity of the multiple demand network in humans and comparison to the macaque brain. Cereb Cortex 2023; 33:10959-10971. [PMID: 37798142 PMCID: PMC10646692 DOI: 10.1093/cercor/bhad314] [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: 05/01/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 10/07/2023] Open
Abstract
Fluid intelligence encompasses a wide range of abilities such as working memory, problem-solving, and relational reasoning. In the human brain, these abilities are associated with the Multiple Demand Network, traditionally thought to involve combined activity of specific regions predominantly in the prefrontal and parietal cortices. However, the structural basis of the interactions between areas in the Multiple Demand Network, as well as their evolutionary basis among primates, remains largely unexplored. Here, we exploit diffusion MRI to elucidate the major white matter pathways connecting areas of the human core and extended Multiple Demand Network. We then investigate whether similar pathways can be identified in the putative homologous areas of the Multiple Demand Network in the macaque monkey. Finally, we contrast human and monkey networks using a recently proposed approach to compare different species' brains within a common organizational space. Our results indicate that the core Multiple Demand Network relies mostly on dorsal longitudinal connections and, although present in the macaque, these connections are more pronounced in the human brain. The extended Multiple Demand Network relies on distinct pathways and communicates with the core Multiple Demand Network through connections that also appear enhanced in the human compared with the macaque.
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Affiliation(s)
- Katrin Karadachka
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - Daniel J Mitchell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - W Pieter Medendorp
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
- Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford OX3 9DU, United Kingdom
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15
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Wang Q, Zhao S, Liu T, Han J, Liu C. Temporal fingerprints of cortical gyrification in marmosets and humans. Cereb Cortex 2023; 33:9802-9814. [PMID: 37434368 PMCID: PMC10656951 DOI: 10.1093/cercor/bhad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/13/2023] Open
Abstract
Recent neuroimaging studies in humans have reported distinct temporal dynamics of gyri and sulci, which may be associated with putative functions of cortical gyrification. However, the complex folding patterns of the human cortex make it difficult to explain temporal patterns of gyrification. In this study, we used the common marmoset as a simplified model to examine the temporal characteristics and compare them with the complex gyrification of humans. Using a brain-inspired deep neural network, we obtained reliable temporal-frequency fingerprints of gyri and sulci from the awake rs-fMRI data of marmosets and humans. Notably, the temporal fingerprints of one region successfully classified the gyrus/sulcus of another region in both marmosets and humans. Additionally, the temporal-frequency fingerprints were remarkably similar in both species. We then analyzed the resulting fingerprints in several domains and adopted the Wavelet Transform Coherence approach to characterize the gyro-sulcal coupling patterns. In both humans and marmosets, sulci exhibited higher frequency bands than gyri, and the two were temporally coupled within the same range of phase angles. This study supports the notion that gyri and sulci possess unique and evolutionarily conserved features that are consistent across functional areas, and advances our understanding of the functional role of cortical gyrification.
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Affiliation(s)
- Qiyu Wang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, United States
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Cirong Liu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
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16
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Howard AFD, Huszar IN, Smart A, Cottaar M, Daubney G, Hanayik T, Khrapitchev AA, Mars RB, Mollink J, Scott C, Sibson NR, Sallet J, Jbabdi S, Miller KL. An open resource combining multi-contrast MRI and microscopy in the macaque brain. Nat Commun 2023; 14:4320. [PMID: 37468455 PMCID: PMC10356772 DOI: 10.1038/s41467-023-39916-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 07/03/2023] [Indexed: 07/21/2023] Open
Abstract
Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.
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Affiliation(s)
- Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Greg Daubney
- Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Connor Scott
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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17
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Xia X, Zeng X, Gao F, Yuan Z. Mapping cross-species connectome atlas of human and macaque striatum. Cereb Cortex 2023; 33:7518-7530. [PMID: 36928317 PMCID: PMC10267647 DOI: 10.1093/cercor/bhad057] [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: 12/23/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/18/2023] Open
Abstract
Cross-species connectome atlas (CCA) that can provide connectionally homogeneous and homologous brain nodes is essential and customized for cross-species neuroscience. However, existing CCAs were flawed in design and coarse-grained in results. In this study, a normative mapping framework of CCA was proposed and applied on human and macaque striatum. Specifically, all striatal voxels in the 2 species were mixed together and classified based on their represented and characterized feature of within-striatum resting-state functional connectivity, which was shared between the species. Six pairs of striatal parcels in these species were delineated in both hemispheres. Furthermore, this striatal parcellation was demonstrated by the best-matched whole-brain functional and structural connectivity between interspecies corresponding subregions. Besides, detailed interspecies differences in whole-brain multimodal connectivities and involved brain functions of these subregions were described to flesh out this CCA of striatum. In particular, this flexible and scalable mapping framework enables reliable construction of CCA of the whole brain, which would enable reliable findings in future cross-species research and advance our understandings into how the human brain works.
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Affiliation(s)
- Xiaoluan Xia
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Xinglin Zeng
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Fei Gao
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
| | - Zhen Yuan
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
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18
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Mars RB, Palomero-Gallagher N. Towards multi-modal, multi-species brain atlases: part one. Brain Struct Funct 2023; 228:1041-1044. [PMID: 37227517 PMCID: PMC10250418 DOI: 10.1007/s00429-023-02656-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, AJ 6525, Nijmegen, The Netherlands.
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany.
- C. & O. Vogt Institute for Brain Research, Heinrich-Heine-University, 40225, Dusseldorf, Germany.
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19
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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: 0] [Impact Index Per Article: 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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20
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Bedini M, Olivetti E, Avesani P, Baldauf D. Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis. Brain Struct Funct 2023; 228:997-1017. [PMID: 37093304 PMCID: PMC10147761 DOI: 10.1007/s00429-023-02641-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023]
Abstract
The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.
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Affiliation(s)
- Marco Bedini
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy.
- Department of Psychology, University of California, San Diego, McGill Hall 9500 Gilman Dr, La Jolla, CA, 92093-0109, USA.
| | - Emanuele Olivetti
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Paolo Avesani
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Daniel Baldauf
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
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21
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Schwartz E, Nenning KH, Heuer K, Jeffery N, Bertrand OC, Toro R, Kasprian G, Prayer D, Langs G. Evolution of cortical geometry and its link to function, behaviour and ecology. Nat Commun 2023; 14:2252. [PMID: 37080952 PMCID: PMC10119184 DOI: 10.1038/s41467-023-37574-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 03/22/2023] [Indexed: 04/22/2023] Open
Abstract
Studies in comparative neuroanatomy and of the fossil record demonstrate the influence of socio-ecological niches on the morphology of the cerebral cortex, but have led to oftentimes conflicting theories about its evolution. Here, we study the relationship between the shape of the cerebral cortex and the topography of its function. We establish a joint geometric representation of the cerebral cortices of ninety species of extant Euarchontoglires, including commonly used experimental model organisms. We show that variability in surface geometry relates to species' ecology and behaviour, independent of overall brain size. Notably, ancestral shape reconstruction of the cortical surface and its change during evolution enables us to trace the evolutionary history of localised cortical expansions, modal segregation of brain function, and their association to behaviour and cognition. We find that individual cortical regions follow different sequences of area increase during evolutionary adaptations to dynamic socio-ecological niches. Anatomical correlates of this sequence of events are still observable in extant species, and relate to their current behaviour and ecology. We decompose the deep evolutionary history of the shape of the human cortical surface into spatially and temporally conscribed components with highly interpretable functional associations, highlighting the importance of considering the evolutionary history of cortical regions when studying their anatomy and function.
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Affiliation(s)
- Ernst Schwartz
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Karl-Heinz Nenning
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Katja Heuer
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | - Nathan Jeffery
- Institute of Life Course & Medical Sciences, University of Liverpool, Liverpool, England
| | - Ornella C Bertrand
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona Edifici ICTA-ICP, c/ Columnes s/n, Campus de la UAB, 08193 Cerdanyola del Vallès., Barcelona, Spain
- School of GeoSciences, University of Edinburgh, Grant Institute, Edinburgh, Scotland, EH9 3FE, United Kingdom
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria.
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
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22
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Ji JL, Demšar J, Fonteneau C, Tamayo Z, Pan L, Kraljič A, Matkovič A, Purg N, Helmer M, Warrington S, Winkler A, Zerbi V, Coalson TS, Glasser MF, Harms MP, Sotiropoulos SN, Murray JD, Anticevic A, Repovš G. QuNex-An integrative platform for reproducible neuroimaging analytics. Front Neuroinform 2023; 17:1104508. [PMID: 37090033 PMCID: PMC10113546 DOI: 10.3389/fninf.2023.1104508] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 04/08/2023] Open
Abstract
Introduction Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Jure Demšar
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Lining Pan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Aleksij Kraljič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Nina Purg
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Markus Helmer
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Anderson Winkler
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Valerio Zerbi
- Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland
- Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland
| | - Timothy S. Coalson
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Matthew F. Glasser
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Michael P. Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham NIHR Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Physics, Yale University, New Haven, CT, United States
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Psychology, Yale University School of Medicine, New Haven, CT, United States
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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23
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Banerjee A, Wang BA, Teutsch J, Helmchen F, Pleger B. Analogous cognitive strategies for tactile learning in the rodent and human brain. Prog Neurobiol 2023; 222:102401. [PMID: 36608783 DOI: 10.1016/j.pneurobio.2023.102401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 12/21/2022] [Accepted: 01/02/2023] [Indexed: 01/05/2023]
Abstract
Evolution has molded individual species' sensory capacities and abilities. In rodents, who mostly inhabit dark tunnels and burrows, the whisker-based somatosensory system has developed as the dominant sensory modality, essential for environmental exploration and spatial navigation. In contrast, humans rely more on visual and auditory inputs when collecting information from their surrounding sensory space in everyday life. As a result of such species-specific differences in sensory dominance, cognitive relevance and capacities, the evidence for analogous sensory-cognitive mechanisms across species remains sparse. However, recent research in rodents and humans yielded surprisingly comparable processing rules for detecting tactile stimuli, integrating touch information into percepts, and goal-directed rule learning. Here, we review how the brain, across species, harnesses such processing rules to establish decision-making during tactile learning, following canonical circuits from the thalamus and the primary somatosensory cortex up to the frontal cortex. We discuss concordances between empirical and computational evidence from micro- and mesoscopic circuit studies in rodents to findings from macroscopic imaging in humans. Furthermore, we discuss the relevance and challenges for future cross-species research in addressing mutual context-dependent evaluation processes underpinning perceptual learning.
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Affiliation(s)
- Abhishek Banerjee
- Adaptive Decisions Lab, Biosciences Institute, Newcastle University, United Kingdom.
| | - Bin A Wang
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr University Bochum, Germany; Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr University Bochum, Germany.
| | - Jasper Teutsch
- Adaptive Decisions Lab, Biosciences Institute, Newcastle University, United Kingdom
| | - Fritjof Helmchen
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zürich, Switzerland
| | - Burkhard Pleger
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr University Bochum, Germany; Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr University Bochum, Germany
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24
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Guma E, Beauchamp A, Liu S, Levitis E, Clasen LS, Torres E, Blumenthal J, Lalonde F, Qiu LR, Hrncir H, MacKenzie-Graham A, Yang X, Arnold AP, Lerch JP, Raznahan A. A Cross-Species Neuroimaging Study of Sex Chromosome Dosage Effects on Human and Mouse Brain Anatomy. J Neurosci 2023; 43:1321-1333. [PMID: 36631267 PMCID: PMC9987571 DOI: 10.1523/jneurosci.1761-22.2022] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
All eutherian mammals show chromosomal sex determination with contrasting sex chromosome dosages (SCDs) between males (XY) and females (XX). Studies in transgenic mice and humans with sex chromosome trisomy (SCT) have revealed direct SCD effects on regional mammalian brain anatomy, but we lack a formal test for cross-species conservation of these effects. Here, we develop a harmonized framework for comparative structural neuroimaging and apply this to systematically profile SCD effects on regional brain anatomy in both humans and mice by contrasting groups with SCT (XXY and XYY) versus XY controls. Total brain size was substantially altered by SCT in humans (significantly decreased by XXY and increased by XYY), but not in mice. Robust and spatially convergent effects of XXY and XYY on regional brain volume were observed in humans, but not mice, when controlling for global volume differences. However, mice do show subtle effects of XXY and XYY on regional volume, although there is not a general spatial convergence in these effects within mice or between species. Notwithstanding this general lack of conservation in SCT effects, we detect several brain regions that show overlapping effects of XXY and XYY both within and between species (cerebellar, parietal, and orbitofrontal cortex), thereby nominating high priority targets for future translational dissection of SCD effects on the mammalian brain. Our study introduces a generalizable framework for comparative neuroimaging in humans and mice and applies this to achieve a cross-species comparison of SCD effects on the mammalian brain through the lens of SCT.SIGNIFICANCE STATEMENT Sex chromosome dosage (SCD) affects neuroanatomy and risk for psychopathology in humans. Performing mechanistic studies in the human brain is challenging but possible in mouse models. Here, we develop a framework for cross-species neuroimaging analysis and use this to show that an added X- or Y-chromosome significantly alters human brain anatomy but has muted effects in the mouse brain. However, we do find evidence for conserved cross-species impact of an added chromosome in the fronto-parietal cortices and cerebellum, which point to regions for future mechanistic dissection of sex chromosome dosage effects on brain development.
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Affiliation(s)
- Elisa Guma
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Antoine Beauchamp
- Mouse Imaging Centre, Toronto, Ontario M5T 3H7, Canada
- The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Elizabeth Levitis
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Liv S. Clasen
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Erin Torres
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Jonathan Blumenthal
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Francois Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Lily R. Qiu
- Mouse Imaging Centre, Toronto, Ontario M5T 3H7, Canada
- The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | - Haley Hrncir
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California 90095
| | - Allan MacKenzie-Graham
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California 90095
| | - Arthur P. Arnold
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California 90095
| | - Jason P. Lerch
- Mouse Imaging Centre, Toronto, Ontario M5T 3H7, Canada
- The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
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25
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Suarez LE, Yovel Y, van den Heuvel MP, Sporns O, Assaf Y, Lajoie G, Misic B. A connectomics-based taxonomy of mammals. eLife 2022; 11:e78635. [PMID: 36342363 PMCID: PMC9681214 DOI: 10.7554/elife.78635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle to the comparison of neural architectures has been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here, we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyse the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion MRI scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.
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Affiliation(s)
- Laura E Suarez
- Montréal Neurological Institute, McGill UniversityMontrealCanada
- Mila - Quebec Artificial Intelligence InstituteMontrealCanada
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | - Martijn P van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana UniversityBloomingtonUnited States
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | | | - Bratislav Misic
- Montréal Neurological Institute, McGill UniversityMontrealCanada
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26
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Cruces RR, Royer J, Herholz P, Larivière S, Vos de Wael R, Paquola C, Benkarim O, Park BY, Degré-Pelletier J, Nelson MC, DeKraker J, Leppert IR, Tardif C, Poline JB, Concha L, Bernhardt BC. Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. Neuroimage 2022; 263:119612. [PMID: 36070839 PMCID: PMC10697132 DOI: 10.1016/j.neuroimage.2022.119612] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 11/25/2022] Open
Abstract
Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.
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Affiliation(s)
- Raúl R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Peer Herholz
- NeuroDataScience - ORIGAMI lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Department of Data Science, Inha University, Incheon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Janie Degré-Pelletier
- Labo IDEA, Département de Psychologie, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Mark C Nelson
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Christine Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
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27
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Warrington S, Thompson E, Bastiani M, Dubois J, Baxter L, Slater R, Jbabdi S, Mars RB, Sotiropoulos SN. Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications. SCIENCE ADVANCES 2022; 8:eabq2022. [PMID: 36260675 PMCID: PMC9581484 DOI: 10.1126/sciadv.abq2022] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
Developmental and evolutionary effects on brain organization are complex, yet linked, as evidenced by the correspondence in cortical area expansion across these vastly different time scales. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric measurements. Here, we propose a novel framework that allows the integration of structural connectivity maps from humans (adults and neonates) and nonhuman primates (macaques) onto a common space. We use white matter bundles to anchor the common space and use the uniqueness of cortical connection patterns to these bundles to probe area specialization. This enabled us to quantitatively study divergences and similarities in connectivity over evolutionary and developmental scales, to reveal brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains. Our findings open new avenues for an integrative approach to imaging neuroanatomy.
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Affiliation(s)
- Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Elinor Thompson
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jessica Dubois
- Université Paris Cité, Inserm, NeuroDiderot Unit, Paris, France
- University Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
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28
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Comparing human and chimpanzee temporal lobe neuroanatomy reveals modifications to human language hubs beyond the frontotemporal arcuate fasciculus. Proc Natl Acad Sci U S A 2022; 119:e2118295119. [PMID: 35787056 PMCID: PMC9282369 DOI: 10.1073/pnas.2118295119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The biological foundation for the language-ready brain in the human lineage remains a debated subject. In humans, the arcuate fasciculus (AF) white matter and the posterior portions of the middle temporal gyrus are crucial for language. Compared with other primates, the human AF has been shown to dramatically extend into the posterior temporal lobe, which forms the basis of a number of models of the structural connectivity basis of language. Recent advances in both language research and comparative neuroimaging invite a reassessment of the anatomical differences in language streams between humans and our closest relatives. Here, we show that posterior temporal connectivity via the AF in humans compared with chimpanzees is expanded in terms of its connectivity not just to the ventral frontal cortex but also to the parietal cortex. At the same time, posterior temporal regions connect more strongly to the ventral white matter in chimpanzees as opposed to humans. This pattern is present in both brain hemispheres. Additionally, we show that the anterior temporal lobe harbors a combination of connections present in both species through the inferior fronto-occipital fascicle and human-unique expansions through the uncinate and middle and inferior longitudinal fascicles. These findings elucidate structural changes that are unique to humans and may underlie the anatomical foundations for full-fledged language capacity.
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29
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Valk SL, Xu T, Paquola C, Park BY, Bethlehem RAI, Vos de Wael R, Royer J, Masouleh SK, Bayrak Ş, Kochunov P, Yeo BTT, Margulies D, Smallwood J, Eickhoff SB, Bernhardt BC. Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex. Nat Commun 2022; 13:2341. [PMID: 35534454 PMCID: PMC9085871 DOI: 10.1038/s41467-022-29886-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 04/01/2022] [Indexed: 12/15/2022] Open
Abstract
Brain structure scaffolds intrinsic function, supporting cognition and ultimately behavioral flexibility. However, it remains unclear how a static, genetically controlled architecture supports flexible cognition and behavior. Here, we synthesize genetic, phylogenetic and cognitive analyses to understand how the macroscale organization of structure-function coupling across the cortex can inform its role in cognition. In humans, structure-function coupling was highest in regions of unimodal cortex and lowest in transmodal cortex, a pattern that was mirrored by a reduced alignment with heritable connectivity profiles. Structure-function uncoupling in macaques had a similar spatial distribution, but we observed an increased coupling between structure and function in association cortices relative to humans. Meta-analysis suggested regions with the least genetic control (low heritable correspondence and different across primates) are linked to social-cognition and autobiographical memory. Our findings suggest that genetic and evolutionary uncoupling of structure and function in different transmodal systems may support the emergence of complex forms of cognition.
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Affiliation(s)
- Sofie L. Valk
- grid.419524.f0000 0001 0041 5028Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany ,grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Ting Xu
- grid.428122.f0000 0004 7592 9033Center for the Developing Brain, Child Mind Institute, New York, NY USA
| | - Casey Paquola
- grid.14709.3b0000 0004 1936 8649Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC Canada ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Structural and functional organisation of the brain (INM-1), Research Centre Jülich, Jülich, Germany, FZ Jülich, Jülich, Germany
| | - Bo-yong Park
- grid.14709.3b0000 0004 1936 8649Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC Canada ,grid.202119.90000 0001 2364 8385Department of Data Science, Inha University, Incheon, South Korea ,grid.410720.00000 0004 1784 4496Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | | | - Reinder Vos de Wael
- grid.14709.3b0000 0004 1936 8649Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC Canada
| | - Jessica Royer
- grid.14709.3b0000 0004 1936 8649Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC Canada
| | - Shahrzad Kharabian Masouleh
- grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany ,grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Şeyma Bayrak
- grid.419524.f0000 0001 0041 5028Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Peter Kochunov
- grid.411024.20000 0001 2175 4264Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD USA
| | - B. T. Thomas Yeo
- grid.4280.e0000 0001 2180 6431Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore ,grid.32224.350000 0004 0386 9924Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA USA ,grid.4280.e0000 0001 2180 6431Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Daniel Margulies
- grid.425274.20000 0004 0620 5939Neuroanatomy and Connectivity Lab, Institut de Cerveau et de la Moelle epiniere, Paris, France
| | - Jonathan Smallwood
- grid.410356.50000 0004 1936 8331Department of Psychology, Queen’s University, Kingston, ON Canada
| | - Simon B. Eickhoff
- grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany ,grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Boris C. Bernhardt
- grid.14709.3b0000 0004 1936 8649Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC Canada
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30
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Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
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Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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31
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Tendler BC, Hanayik T, Ansorge O, Bangerter-Christensen S, Berns GS, Bertelsen MF, Bryant KL, Foxley S, van den Heuvel MP, Howard AFD, Huszar IN, Khrapitchev AA, Leonte A, Manger PR, Menke RAL, Mollink J, Mortimer D, Pallebage-Gamarallage M, Roumazeilles L, Sallet J, Scholtens LH, Scott C, Smart A, Turner MR, Wang C, Jbabdi S, Mars RB, Miller KL. The Digital Brain Bank, an open access platform for post-mortem imaging datasets. eLife 2022; 11:e73153. [PMID: 35297760 PMCID: PMC9042233 DOI: 10.7554/elife.73153] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Post-mortem magnetic resonance imaging (MRI) provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes-Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; and Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank data release includes 21 distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen nonhuman primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab's investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.
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Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Olaf Ansorge
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Sarah Bangerter-Christensen
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | | | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen ZooFrederiksbergDenmark
| | - Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Department of Radiology, University of ChicagoChicagoUnited States
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Department of Child Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Amy FD Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Alexandre A Khrapitchev
- Medical Research Council Oxford Institute for Radiation Oncology, University of OxfordOxfordUnited Kingdom
| | - Anna Leonte
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the WitwatersrandJohannesburgSouth Africa
| | - Ricarda AL Menke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Duncan Mortimer
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Menuka Pallebage-Gamarallage
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Stem Cell and Brain Research Institute, Université Lyon 1, INSERMBronFrance
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Connor Scott
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Martin R Turner
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Donders Institute for Brain, Cognition and Behaviour, Radboud University NijmegenNijmegenNetherlands
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
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32
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Haber SN, Liu H, Seidlitz J, Bullmore E. Prefrontal connectomics: from anatomy to human imaging. Neuropsychopharmacology 2022; 47:20-40. [PMID: 34584210 PMCID: PMC8617085 DOI: 10.1038/s41386-021-01156-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/23/2021] [Accepted: 08/02/2021] [Indexed: 12/22/2022]
Abstract
The fundamental importance of prefrontal cortical connectivity to information processing and, therefore, disorders of cognition, emotion, and behavior has been recognized for decades. Anatomic tracing studies in animals have formed the basis for delineating the direct monosynaptic connectivity, from cells of origin, through axon trajectories, to synaptic terminals. Advances in neuroimaging combined with network science have taken the lead in developing complex wiring diagrams or connectomes of the human brain. A key question is how well these magnetic resonance imaging (MRI)-derived networks and hubs reflect the anatomic "hard wiring" first proposed to underlie the distribution of information for large-scale network interactions. In this review, we address this challenge by focusing on what is known about monosynaptic prefrontal cortical connections in non-human primates and how this compares to MRI-derived measurements of network organization in humans. First, we outline the anatomic cortical connections and pathways for each prefrontal cortex (PFC) region. We then review the available MRI-based techniques for indirectly measuring structural and functional connectivity, and introduce graph theoretical methods for analysis of hubs, modules, and topologically integrative features of the connectome. Finally, we bring these two approaches together, using specific examples, to demonstrate how monosynaptic connections, demonstrated by tract-tracing studies, can directly inform understanding of the composition of PFC nodes and hubs, and the edges or pathways that connect PFC to cortical and subcortical areas.
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Affiliation(s)
- Suzanne N. Haber
- grid.412750.50000 0004 1936 9166Department of Pharmacology and Physiology, University of Rochester School of Medicine & Dentistry, Rochester, NY 14642 USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | - Hesheng Liu
- grid.259828.c0000 0001 2189 3475Department of Neuroscience, Medical University of South Carolina, Charleston, SC USA ,grid.38142.3c000000041936754XDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Jakob Seidlitz
- grid.25879.310000 0004 1936 8972Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ed Bullmore
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain and Mind Sciences, Cambridge Biomedical Campus, Cambridge, CB2 0SZ UK
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33
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Rudebeck PH, Izquierdo A. Foraging with the frontal cortex: A cross-species evaluation of reward-guided behavior. Neuropsychopharmacology 2022; 47:134-146. [PMID: 34408279 PMCID: PMC8617092 DOI: 10.1038/s41386-021-01140-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/30/2021] [Accepted: 07/30/2021] [Indexed: 02/07/2023]
Abstract
Efficient foraging is essential to survival and depends on frontal cortex in mammals. Because of its role in psychiatric disorders, frontal cortex and its contributions to reward procurement have been studied extensively in both rodents and non-human primates. How frontal cortex of these animal models compares is a source of intense debate. Here we argue that translating findings from rodents to non-human primates requires an appreciation of both the niche in which each animal forages as well as the similarities in frontal cortex anatomy and function. Consequently, we highlight similarities and differences in behavior and anatomy, before focusing on points of convergence in how parts of frontal cortex contribute to distinct aspects of foraging in rats and macaques, more specifically. In doing so, our aim is to emphasize where translation of frontal cortex function between species is clearer, where there is divergence, and where future work should focus. We finish by highlighting aspects of foraging for which have received less attention but we believe are critical to uncovering how frontal cortex promotes survival in each species.
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Affiliation(s)
| | - Alicia Izquierdo
- Department of Psychology, UCLA, Los Angeles, CA, USA.
- The Brain Research Institute, UCLA, Los Angeles, CA, USA.
- Integrative Center for Learning and Memory, UCLA, Los Angeles, CA, USA.
- Integrative Center for Addictions, UCLA, Los Angeles, CA, USA.
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34
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Becker Y, Loh KK, Coulon O, Meguerditchian A. The Arcuate Fasciculus and language origins: Disentangling existing conceptions that influence evolutionary accounts. Neurosci Biobehav Rev 2021; 134:104490. [PMID: 34914937 DOI: 10.1016/j.neubiorev.2021.12.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 11/30/2021] [Accepted: 12/08/2021] [Indexed: 12/11/2022]
Abstract
The Arcuate Fasciculus (AF) is of considerable interdisciplinary interest, because of its major implication in language processing. Theories about language brain evolution are based on anatomical differences in the AF across primates. However, changing methodologies and nomenclatures have resulted in conflicting findings regarding interspecies AF differences: Historical knowledge about the AF originated from human blunt dissections and later from monkey tract-tracing studies. Contemporary tractography studies reinvestigate the fasciculus' morphology, but remain heavily bound to unclear anatomical priors and methodological limitations. First, we aim to disentangle the influences of these three epistemological steps on existing AF conceptions, and to propose a contemporary model to guide future work. Second, considering the influence of various AF conceptions, we discuss four key evolutionary changes that propagated current views about language evolution: 1) frontal terminations, 2) temporal terminations, 3) greater Dorsal- versus Ventral Pathway expansion, 4) lateralisation. We conclude that new data point towards a more shared AF anatomy across primates than previously described. Language evolution theories should incorporate this continuous AF evolution across primates.
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Affiliation(s)
- Yannick Becker
- Laboratoire de Psychologie Cognitive, Aix-Marseille Univ, CNRS UMR 7290, Marseille, France; Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France.
| | - Kep Kee Loh
- Laboratoire de Psychologie Cognitive, Aix-Marseille Univ, CNRS UMR 7290, Marseille, France; Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille Univ, Marseille, France
| | - Olivier Coulon
- Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille Univ, Marseille, France
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, Aix-Marseille Univ, CNRS UMR 7290, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille Univ, Marseille, France; Station de Primatologie CNRS, Rousset, France
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35
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Species and individual differences and connectional asymmetry of Broca's area in humans and macaques. Neuroimage 2021; 244:118583. [PMID: 34562577 DOI: 10.1016/j.neuroimage.2021.118583] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/11/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023] Open
Abstract
To reveal the connectional specialization of the Broca's area (or its homologue), voxel-wise inter-species and individual differences, and inter-hemispheric asymmetry were respectively inspected in humans and macaques at both whole-brain connectivity and single tract levels. It was discovered that the developed connectivity blueprint approach is able to localize connectionally comparable voxels between the two species in Broca's area, whereas the quantitative differences between blueprints of locationally or connectionally corresponding voxels enable us to generate inter-hemispheric, inter-subject, and inter-species connectional variabilities, respectively. More importantly, the inter-species and inter-subject variabilities exhibited positive correlation in both two primates, and relatively higher variabilities were detected in the anatomically defined pars triangularis. By contrast, negative relationship was identified between the inter-species variability and hemispheric asymmetry in human brain. In particular, relatively higher asymmetry was revealed in the anatomically defined pars opercularis. Therefore, our novel findings demonstrated that pars triangularis, as compared to pars opercularis, might be a more active area during primate evolution, in which the brain connectivity and possible functions of pars triangularis show relatively higher degree in species specialization, yet lower in hemispheric specialization. Meanwhile, brain connectivity and possible functions of pars opercularis manifested an opposite pattern. At the tract level, functional roles related to the ventral stream in speech comprehension were relatively conservative and bilaterally organized, while those related to the dorsal stream in speech production show relatively higher species and hemispheric specializations.
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36
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Bryant KL, Ardesch DJ, Roumazeilles L, Scholtens LH, Khrapitchev AA, Tendler BC, Wu W, Miller KL, Sallet J, van den Heuvel MP, Mars RB. Diffusion MRI data, sulcal anatomy, and tractography for eight species from the Primate Brain Bank. Brain Struct Funct 2021; 226:2497-2509. [PMID: 34264391 PMCID: PMC8608778 DOI: 10.1007/s00429-021-02268-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/26/2021] [Indexed: 12/16/2022]
Abstract
Large-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal-frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species. We discuss the results in the context of known primate specializations and present hypotheses for further research. All data and results presented here are available online as a resource for the scientific community.
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Affiliation(s)
- Katherine L Bryant
- Wellcome 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
| | - Dirk Jan Ardesch
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alexandre A Khrapitchev
- Department of Oncology, University of Oxford, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, Oxford, UK
| | - Benjamin C Tendler
- Wellcome 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
| | - Wenchuan Wu
- Wellcome 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
| | - Karla L Miller
- Wellcome 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
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500, Bron, France
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Rogier B Mars
- Wellcome 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.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
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Roumazeilles L, Lange FJ, Benn RA, Andersson JLR, Bertelsen MF, Manger PR, Flach E, Khrapitchev AA, Bryant KL, Sallet J, Mars RB. Cortical Morphology and White Matter Tractography of Three Phylogenetically Distant Primates: Evidence for a Simian Elaboration. Cereb Cortex 2021; 32:1608-1624. [PMID: 34518890 PMCID: PMC9016287 DOI: 10.1093/cercor/bhab285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/20/2022] Open
Abstract
Comparative neuroimaging has been used to identify changes in white matter architecture across primate species phylogenetically close to humans, but few have compared the phylogenetically distant species. Here, we acquired postmortem diffusion imaging data from ring-tailed lemurs (Lemur catta), black-capped squirrel monkeys (Saimiri boliviensis), and rhesus macaques (Macaca mulatta). We were able to establish templates and surfaces allowing us to investigate sulcal, cortical, and white matter anatomy. The results demonstrate an expansion of the frontal projections of the superior longitudinal fasciculus complex in squirrel monkeys and rhesus macaques compared to ring-tailed lemurs, which correlates with sulcal anatomy and the lemur’s smaller prefrontal granular cortex. The connectivity of the ventral pathway in the parietal region is also comparatively reduced in ring-tailed lemurs, with the posterior projections of the inferior longitudinal fasciculus not extending toward parietal cortical areas as in the other species. In the squirrel monkeys we note a very specific occipito-parietal anatomy that is apparent in their surface anatomy and the expansion of the posterior projections of the optical radiation. Our study supports the hypothesis that the connectivity of the prefrontal-parietal regions became relatively elaborated in the simian lineage after divergence from the prosimian lineage.
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Affiliation(s)
- Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX13TA, UK
| | - Frederik J Lange
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK
| | - R Austin Benn
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid 28029, Spain
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK
| | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg 2000, Denmark
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
| | - Edmund Flach
- Wildlife Health Services, Zoological Society of London, London NW14RY, UK (now retired)
| | - Alexandre A Khrapitchev
- MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford OX37DQ, UK
| | - Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX13TA, UK.,Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron 69500, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK.,Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen 6525 HR, The Netherlands
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