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Autio JA, Kimura I, Ose T, Matsumoto Y, Ohno M, Urushibata Y, Ikeda T, Glasser MF, Van Essen DC, Hayashi T. Mapping vascular network architecture in primate brain using ferumoxytol-weighted laminar MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594068. [PMID: 38798334 PMCID: PMC11118324 DOI: 10.1101/2024.05.16.594068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Mapping the vascular organization of the brain is of great importance across various domains of basic neuroimaging research, diagnostic radiology, and neurology. However, the intricate task of precisely mapping vasculature across brain regions and cortical layers presents formidable challenges, resulting in a limited understanding of neurometabolic factors influencing the brain's microvasculature. Addressing this gap, our study investigates whole-brain vascular volume using ferumoxytol-weighted laminar-resolution multi-echo gradient-echo imaging in macaque monkeys. We validate the results with published data for vascular densities and compare them with cytoarchitecture, neuron and synaptic densities. The ferumoxytol-induced change in transverse relaxation rate (ΔR2*), an indirect proxy measure of cerebral blood volume (CBV), was mapped onto twelve equivolumetric laminar cortical surfaces. Our findings reveal that CBV varies 3-fold across the brain, with the highest vascular volume observed in the inferior colliculus and lowest in the corpus callosum. In the cerebral cortex, CBV is notably high in early primary sensory areas and low in association areas responsible for higher cognitive functions. Classification of CBV into distinct groups unveils extensive replication of translaminar vascular network motifs, suggesting distinct computational energy supply requirements in areas with varying cytoarchitecture types. Regionally, baseline R2* and CBV exhibit positive correlations with neuron density and negative correlations with receptor densities. Adjusting image resolution based on the critical sampling frequency of penetrating cortical vessels, allows us to delineate approximately 30% of the arterial-venous vessels. Collectively, these results mark significant methodological and conceptual advancements, contributing to the refinement of cerebrovascular MRI. Furthermore, our study establishes a linkage between neurometabolic factors and the vascular network architecture in the primate brain.
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Affiliation(s)
- Joonas A. Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Ikko Kimura
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Takayuki Ose
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Yuki Matsumoto
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Masahiro Ohno
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Takuro Ikeda
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Matthew F. Glasser
- Department of Radiology, Washington University Medical School, St. Louis, MO, United States
- Department of Neuroscience, Washington University Medical School, St. Louis, MO, United States
| | - David C. Van Essen
- Department of Neuroscience, Washington University Medical School, St. Louis, MO, United States
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
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2
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Froesel M, Gacoin M, Clavagnier S, Hauser M, Goudard Q, Ben Hamed S. Macaque claustrum, pulvinar and putative dorsolateral amygdala support the cross-modal association of social audio-visual stimuli based on meaning. Eur J Neurosci 2024; 59:3203-3223. [PMID: 38637993 DOI: 10.1111/ejn.16328] [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: 11/16/2022] [Revised: 02/14/2024] [Accepted: 03/07/2024] [Indexed: 04/20/2024]
Abstract
Social communication draws on several cognitive functions such as perception, emotion recognition and attention. The association of audio-visual information is essential to the processing of species-specific communication signals. In this study, we use functional magnetic resonance imaging in order to identify the subcortical areas involved in the cross-modal association of visual and auditory information based on their common social meaning. We identified three subcortical regions involved in audio-visual processing of species-specific communicative signals: the dorsolateral amygdala, the claustrum and the pulvinar. These regions responded to visual, auditory congruent and audio-visual stimulations. However, none of them was significantly activated when the auditory stimuli were semantically incongruent with the visual context, thus showing an influence of visual context on auditory processing. For example, positive vocalization (coos) activated the three subcortical regions when presented in the context of positive facial expression (lipsmacks) but not when presented in the context of negative facial expression (aggressive faces). In addition, the medial pulvinar and the amygdala presented multisensory integration such that audiovisual stimuli resulted in activations that were significantly higher than those observed for the highest unimodal response. Last, the pulvinar responded in a task-dependent manner, along a specific spatial sensory gradient. We propose that the dorsolateral amygdala, the claustrum and the pulvinar belong to a multisensory network that modulates the perception of visual socioemotional information and vocalizations as a function of the relevance of the stimuli in the social context. SIGNIFICANCE STATEMENT: Understanding and correctly associating socioemotional information across sensory modalities, such that happy faces predict laughter and escape scenes predict screams, is essential when living in complex social groups. With the use of functional magnetic imaging in the awake macaque, we identify three subcortical structures-dorsolateral amygdala, claustrum and pulvinar-that only respond to auditory information that matches the ongoing visual socioemotional context, such as hearing positively valenced coo calls and seeing positively valenced mutual grooming monkeys. We additionally describe task-dependent activations in the pulvinar, organizing along a specific spatial sensory gradient, supporting its role as a network regulator.
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Affiliation(s)
- Mathilda Froesel
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, Bron Cedex, France
| | - Maëva Gacoin
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, Bron Cedex, France
| | - Simon Clavagnier
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, Bron Cedex, France
| | - Marc Hauser
- Risk-Eraser, West Falmouth, Massachusetts, USA
| | - Quentin Goudard
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, Bron Cedex, France
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, Bron Cedex, France
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3
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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:S2095-9273(24)00187-7. [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] [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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4
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Hori Y, Mimura K, Nagai Y, Hori Y, Kumata K, Zhang MR, Suhara T, Higuchi M, Minamimoto T. Reduced serotonergic transmission alters sensitivity to cost and reward via 5-HT1A and 5-HT1B receptors in monkeys. PLoS Biol 2024; 22:e3002445. [PMID: 38163325 PMCID: PMC10758260 DOI: 10.1371/journal.pbio.3002445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Serotonin (5-HT) deficiency is a core biological pathology underlying depression and other psychiatric disorders whose key symptoms include decreased motivation. However, the exact role of 5-HT in motivation remains controversial and elusive. Here, we pharmacologically manipulated the 5-HT system in macaque monkeys and quantified the effects on motivation for goal-directed actions in terms of incentives and costs. Reversible inhibition of 5-HT synthesis increased errors and reaction times on goal-directed tasks, indicating reduced motivation. Analysis found incentive-dependent and cost-dependent components of this reduction. To identify the receptor subtypes that mediate cost and incentive, we systemically administered antagonists specific to 4 major 5-HT receptor subtypes: 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4. Positron emission tomography (PET) visualized the unique distribution of each subtype in limbic brain regions and determined the systemic dosage for antagonists that would achieve approximately 30% occupancy. Only blockade of 5-HT1A decreased motivation through changes in both expected cost and incentive; sensitivity to future workload and time delay to reward increased (cost) and reward value decreased (incentive). Blocking the 5-HT1B receptor also reduced motivation through decreased incentive, although it did not affect expected cost. These results suggest that 5-HT deficiency disrupts 2 processes, the subjective valuation of costs and rewards, via 5-HT1A and 5-HT1B receptors, thus leading to reduced motivation.
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Affiliation(s)
- Yukiko Hori
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Koki Mimura
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba, Japan
- Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, Japan
| | - Yuji Nagai
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Yuki Hori
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Katsushi Kumata
- Department of Advanced Nuclear Medicine Sciences, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Ming-Rong Zhang
- Department of Advanced Nuclear Medicine Sciences, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Tetsuya Suhara
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Makoto Higuchi
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Takafumi Minamimoto
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba, Japan
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5
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Demirci N, Hoffman ME, Holland MA. Systematic cortical thickness and curvature patterns in primates. Neuroimage 2023; 278:120283. [PMID: 37516374 PMCID: PMC10443624 DOI: 10.1016/j.neuroimage.2023.120283] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/31/2023] Open
Abstract
Humans are known to have significant and consistent differences in thickness throughout the cortex, with thick outer gyral folds and thin inner sulcal folds. Our previous work has suggested a mechanical basis for this thickness pattern, with the forces generated during cortical folding leading to thick gyri and thin sulci, and shown that cortical thickness varies along a gyral-sulcal spectrum in humans. While other primate species are expected to exhibit similar patterns of cortical thickness, it is currently unknown how these patterns scale across different sizes, forms, and foldedness. Among primates, brains vary enormously from roughly the size of a grape to the size of a grapefruit, and from nearly smooth to dramatically folded; of these, human brains are the largest and most folded. These variations in size and form make comparative neuroanatomy a rich resource for investigating common trends that transcend differences between species. In this study, we examine 12 primate species in order to cover a wide range of sizes and forms, and investigate the scaling of their cortical thickness relative to the surface geometry. The 12 species were selected due to the public availability of either reconstructed surfaces and/or population templates. After obtaining or reconstructing 3D surfaces from publicly available neuroimaging data, we used our surface-based computational pipeline (https://github.com/mholla/curveball) to analyze patterns of cortical thickness and folding with respect to size (total surface area), geometry (i.e. curvature, shape, and sulcal depth), and foldedness (gyrification). In all 12 species, we found consistent cortical thickness variations along a gyral-sulcal spectrum, with convex shapes thicker than concave shapes and saddle shapes in between. Furthermore, we saw an increasing thickness difference between gyri and sulci as brain size increases. Our results suggest a systematic folding mechanism relating local cortical thickness to geometry. Finally, all of our reconstructed surfaces and morphometry data are available for future research in comparative neuroanatomy.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Mia E Hoffman
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA; Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Maria A Holland
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA; Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
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6
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Rapan L, Froudist-Walsh S, Niu M, Xu T, Zhao L, Funck T, Wang XJ, Amunts K, Palomero-Gallagher N. Cytoarchitectonic, receptor distribution and functional connectivity analyses of the macaque frontal lobe. eLife 2023; 12:e82850. [PMID: 37578332 PMCID: PMC10425179 DOI: 10.7554/elife.82850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 06/14/2023] [Indexed: 08/15/2023] Open
Abstract
Based on quantitative cyto- and receptor architectonic analyses, we identified 35 prefrontal areas, including novel subdivisions of Walker's areas 10, 9, 8B, and 46. Statistical analysis of receptor densities revealed regional differences in lateral and ventrolateral prefrontal cortex. Indeed, structural and functional organization of subdivisions encompassing areas 46 and 12 demonstrated significant differences in the interareal levels of α2 receptors. Furthermore, multivariate analysis included receptor fingerprints of previously identified 16 motor areas in the same macaque brains and revealed 5 clusters encompassing frontal lobe areas. We used the MRI datasets from the non-human primate data sharing consortium PRIME-DE to perform functional connectivity analyses using the resulting frontal maps as seed regions. In general, rostrally located frontal areas were characterized by bigger fingerprints, that is, higher receptor densities, and stronger regional interconnections. Whereas more caudal areas had smaller fingerprints, but showed a widespread connectivity pattern with distant cortical regions. Taken together, this study provides a comprehensive insight into the molecular structure underlying the functional organization of the cortex and, thus, reconcile the discrepancies between the structural and functional hierarchical organization of the primate frontal lobe. Finally, our data are publicly available via the EBRAINS and BALSA repositories for the entire scientific community.
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Affiliation(s)
- Lucija Rapan
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Sean Froudist-Walsh
- Center for Neural Science, New York UniversityNew YorkUnited States
- Bristol Computational Neuroscience Unit, Faculty of Engineering, University of BristolBristolUnited Kingdom
| | - Meiqi Niu
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Ting Xu
- Center for the Developing Brain, Child Mind InstituteNew YorkUnited States
| | - Ling Zhao
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Thomas Funck
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Xiao-Jing Wang
- Center for Neural Science, New York UniversityNew YorkUnited States
| | - Katrin Amunts
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, Heinrich-Heine-UniversityDüsseldorfGermany
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7
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Valcourt Caron A, Shmuel A, Hao Z, Descoteaux M. versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database. Front Neuroinform 2023; 17:1191200. [PMID: 37637471 PMCID: PMC10449583 DOI: 10.3389/fninf.2023.1191200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/27/2023] [Indexed: 08/29/2023] Open
Abstract
The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.
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Affiliation(s)
- Alex Valcourt Caron
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Amir Shmuel
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Ziqi Hao
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
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8
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Sebenius I, Seidlitz J, Warrier V, Bethlehem RAI, Alexander-Bloch A, Mallard TT, Garcia RR, Bullmore ET, Morgan SE. Robust estimation of cortical similarity networks from brain MRI. Nat Neurosci 2023; 26:1461-1471. [PMID: 37460809 PMCID: PMC10400419 DOI: 10.1038/s41593-023-01376-7] [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: 10/11/2022] [Accepted: 06/08/2023] [Indexed: 08/05/2023]
Abstract
Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.
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Affiliation(s)
- Isaac Sebenius
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Varun Warrier
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Travis T Mallard
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Rafael Romero Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Barcelona, Spain
| | | | - Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
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9
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Lou F, Tang X, Quan Z, Qian M, Wang J, Qu S, Gao Y, Wang Y, Pan G, Lai HY, Roe AW, Zhang X. A 16-channel loop array for in vivo macaque whole-brain imaging at 7 T. Magn Reson Imaging 2023:S0730-725X(23)00110-8. [PMID: 37356599 DOI: 10.1016/j.mri.2023.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/20/2023] [Accepted: 06/22/2023] [Indexed: 06/27/2023]
Abstract
Combining multimodal approaches with functional magnetic resonance imaging (fMRI) has catapulted the research on brain circuitries of non-human primates (NHPs) into a new era. However, many studies are constrained by a lack of appropriate RF coils. In this study,a single loop transmit and 16-channel receive array coil was constructed for brain imaging of macaques at 7 Tesla (7 T). The 16 receive channels were mounted on a 3D-printed helmet-shaped form closely fiting the macaque head, with fourteen openings arranged for multimodal devices around the cortical regions. Coil performance was evaluated by quantifying and comparing signal-to-noise ratio (SNR) maps, noise correlations, g-factor maps and flip-angle maps with a 28-channel commercial knee coil. The in vivo results suggested that the macaque coil has higher SNR in cortical regions and better acceleration ability in parallel imaging, which may benefit revealing mesoscale organizations in the brain.
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Affiliation(s)
- Feiyang Lou
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China
| | - Xiaocui Tang
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China
| | - Zhiyan Quan
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Meizhen Qian
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China; Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jianbao Wang
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China
| | - Shuxian Qu
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yang Gao
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yueming Wang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China; State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
| | - Gang Pan
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Hsin-Yi Lai
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Anna Wang Roe
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaotong Zhang
- The Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; College of Electrical Engineering, Zhejiang University, Hangzhou, China.
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10
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Hendrickson TJ, Reiners P, Moore LA, Perrone AJ, Alexopoulos D, Lee EG, Styner M, Kardan O, Chamberlain TA, Mummaneni A, Caldas HA, Bower B, Stoyell S, Martin T, Sung S, Fair E, Uriarte-Lopez J, Rueter AR, Yacoub E, Rosenberg MD, Smyser CD, Elison JT, Graham A, Fair DA, Feczko E. BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533696. [PMID: 36993540 PMCID: PMC10055337 DOI: 10.1101/2023.03.22.533696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Objectives Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.
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Affiliation(s)
- Timothy J Hendrickson
- Minnesota Supercomputing Institute, University of Minnesota
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Paul Reiners
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota
| | | | - Erik G Lee
- Minnesota Supercomputing Institute, University of Minnesota
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill
| | - Omid Kardan
- Department of Psychology, University of Chicago
- University of Michigan
| | | | | | | | | | - Sally Stoyell
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Tabitha Martin
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Sooyeon Sung
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Ermias Fair
- Masonic Institute for the Developing Brain, University of Minnesota
| | | | | | - Essa Yacoub
- Department of Radiology, University of Minnesota
- Center for Magnetic Resonance Research, University of Minnesota
| | | | | | - Jed T Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Department of Pediatrics, University of Minnesota
| | | | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Department of Pediatrics, University of Minnesota
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota
- Department of Pediatrics, University of Minnesota
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11
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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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12
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Fei H, Wang Q, Shang F, Xu W, Chen X, Chen Y, Li H. HC-Net: A hybrid convolutional network for non-human primate brain extraction. Front Comput Neurosci 2023; 17:1113381. [PMID: 36846727 PMCID: PMC9947775 DOI: 10.3389/fncom.2023.1113381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks.
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Affiliation(s)
- Hong Fei
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Qianshan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Fangxin Shang
- Country Intelligent Healthcare Unit, Baidu, Beijing, China
| | - Wenyi Xu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xiaofeng Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yifei Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Haifang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China,*Correspondence: Haifang Li,
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13
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Cortical adaptation of the night monkey to a nocturnal niche environment: a comparative non-invasive T1w/T2w myelin study. Brain Struct Funct 2022:10.1007/s00429-022-02591-x. [PMID: 36399210 DOI: 10.1007/s00429-022-02591-x] [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: 05/31/2022] [Accepted: 10/25/2022] [Indexed: 11/21/2022]
Abstract
Night monkeys (Aotus) are the only genus of monkeys within the Simian lineage that successfully occupy a nocturnal environmental niche. Their behavior is supported by their sensory organs' distinctive morphological features; however, little is known about their evolutionary adaptations in sensory regions of the cerebral cortex. Here, we investigate this question by exploring the cortical organization of night monkeys using high-resolution in-vivo brain MRI and comparative cortical-surface T1w/T2w myeloarchitectonic mapping. Our results show that the night monkey cerebral cortex has a qualitatively similar but quantitatively different pattern of cortical myelin compared to the diurnal macaque and marmoset monkeys. T1w/T2w myelin and its gradient allowed us to parcellate high myelin areas, including the middle temporal complex (MT +) and auditory cortex, and a low-myelin area, Brodmann area 7 (BA7) in the three species, despite species differences in cortical convolutions. Relative to the total cortical-surface area, those of MT + and the auditory cortex are significantly larger in night monkeys than diurnal monkeys, whereas area BA7 occupies a similar fraction of the cortical sheet in all three species. We propose that the selective expansion of sensory areas dedicated to visual motion and auditory processing in night monkeys may reflect cortical adaptations to a nocturnal environment.
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14
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Nakai R, Hamazaki Y, Ito H, Imamura M. Early neurogenic properties of iPSC-derived neurosphere formation in Japanese macaque monkeys. Differentiation 2022; 128:33-42. [DOI: 10.1016/j.diff.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/03/2022]
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15
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Muta K, Hata J, Kawaguchi N, Haga Y, Yoshimaru D, Hagiya K, Kaneko T, Miyabe-Nishiwaki T, Komaki Y, Seki F, Okano HJ, Okano H. Effect of sedatives or anesthetics on the measurement of resting brain function in common marmosets. Cereb Cortex 2022; 33:5148-5162. [PMID: 36222604 PMCID: PMC10151911 DOI: 10.1093/cercor/bhac406] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 11/13/2022] Open
Abstract
Common marmosets are promising laboratory animals for the study of higher brain functions. Although there are many opportunities to use sedatives and anesthetics in resting brain function measurements in marmosets, their effects on the resting-state network remain unclear. In this study, the effects of sedatives or anesthetics such as midazolam, dexmedetomidine, co-administration of isoflurane and dexmedetomidine, propofol, alfaxalone, isoflurane, and sevoflurane on the resting brain function in common marmosets were evaluated using independent component analysis, dual regression analysis, and graph-theoretic analysis; and the sedatives or anesthetics suitable for the evaluation of resting brain function were investigated. The results show that network preservation tendency under light sedative with midazolam and dexmedetomidine is similar regardless of the type of target receptor. Moreover, alfaxalone, isoflurane, and sevoflurane have similar effects on resting state brain function, but only propofol exhibits different tendencies, as resting brain function is more preserved than it is following the administration of the other anesthetics. Co-administration of isoflurane and dexmedetomidine shows middle effect between sedatives and anesthetics.
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Affiliation(s)
- Kanako Muta
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan.,Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan
| | - Junichi Hata
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan.,Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan.,Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
| | - Naoki Kawaguchi
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan
| | - Yawara Haga
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan.,Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Daisuke Yoshimaru
- Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan.,Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Kei Hagiya
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan
| | - Takaaki Kaneko
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Takako Miyabe-Nishiwaki
- Center for Model Human Evolution Research, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Yuji Komaki
- Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Fumiko Seki
- Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Hirotaka James Okano
- Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
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16
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Woo MK, DelaBarre L, Waks M, Lagore R, Radder J, Jungst S, Kang CK, Ugurbil K, Adriany G. A Monopole and Dipole Hybrid Antenna Array for Human Brain Imaging at 10.5 Tesla. IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2022; 21:1857-1861. [PMID: 37020750 PMCID: PMC10072856 DOI: 10.1109/lawp.2022.3183206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In this letter, we evaluate antenna designs for ultra-high frequency and field (UHF) human brain magnetic resonance imaging (MRI) at 10.5 tesla (T). Although MRI at such UHF is expected to provide major signal-to-noise gains, the frequency of interest, 447 MHz, presents us with challenges regarding improved B1 + efficiency, image homogeneity, specific absorption rate (SAR), and antenna element decoupling for array configurations. To address these challenges, we propose the use of both monopole and dipole antennas in a novel hybrid configuration, which we refer to as a mono-dipole hybrid antenna (MDH) array. Compared to an 8-channel dipole antenna array of the same dimensions, the 8-channel MDH array showed an improvement in decoupling between adjacent array channels, as well as ~18% higher B1 + and SAR efficiency near the central region of the phantom based on simulation and experiment. However, the performances of the MDH and dipole antenna arrays were overall similar when evaluating a human model in terms of peak B1 + efficiency, 10 g SAR, and SAR efficiency. Finally, the concept of an MDH array showed an advantage in improved decoupling, SAR, and B1 + near the superior region of the brain for human brain imaging.
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Affiliation(s)
- Myung Kyun Woo
- Department of Biomedical Engineering, School of Electrical Engineering, University of Ulsan, Ulsan 44005, South Korea
| | - Lance DelaBarre
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN 55455 USA
| | - Matt Waks
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN 55455 USA
| | - Russell Lagore
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN 55455 USA
| | - Jerahmie Radder
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN 55455 USA
| | - Steve Jungst
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN 55455 USA
| | - Chang-Ki Kang
- Department of Radiological Science, College of Health Science, Gachon University, Incheon 1342, South Korea
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455 USA
| | - Gregor Adriany
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN 55455 USA
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17
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Glasser MF, Coalson TS, Harms MP, Xu J, Baum GL, Autio JA, Auerbach EJ, Greve DN, Yacoub E, Van Essen DC, Bock NA, Hayashi T. Empirical transmit field bias correction of T1w/T2w myelin maps. Neuroimage 2022; 258:119360. [PMID: 35697132 PMCID: PMC9483036 DOI: 10.1016/j.neuroimage.2022.119360] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 06/01/2022] [Accepted: 06/04/2022] [Indexed: 12/30/2022] Open
Abstract
T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a 'pseudo-transmit' approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward.
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Affiliation(s)
| | | | - Michael P Harms
- Psychiatry, Washington University Medical School, St. Louis, MO, United States
| | - Junqian Xu
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States; Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, United States
| | - Graham L Baum
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Joonas A Autio
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Edward J Auerbach
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | | | - Nicholas A Bock
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Takuya Hayashi
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
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18
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Socially meaningful visual context either enhances or inhibits vocalisation processing in the macaque brain. Nat Commun 2022; 13:4886. [PMID: 35985995 PMCID: PMC9391382 DOI: 10.1038/s41467-022-32512-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/03/2022] [Indexed: 11/08/2022] Open
Abstract
Social interactions rely on the interpretation of semantic and emotional information, often from multiple sensory modalities. Nonhuman primates send and receive auditory and visual communicative signals. However, the neural mechanisms underlying the association of visual and auditory information based on their common social meaning are unknown. Using heart rate estimates and functional neuroimaging, we show that in the lateral and superior temporal sulcus of the macaque monkey, neural responses are enhanced in response to species-specific vocalisations paired with a matching visual context, or when vocalisations follow, in time, visual information, but inhibited when vocalisation are incongruent with the visual context. For example, responses to affiliative vocalisations are enhanced when paired with affiliative contexts but inhibited when paired with aggressive or escape contexts. Overall, we propose that the identified neural network represents social meaning irrespective of sensory modality. Social interaction involves processing semantic and emotional information. Here the authors show that in the macaque monkey lateral and superior temporal sulcus, cortical activity is enhanced in response to species-specific vocalisations predicted by matching face or social visual stimuli but inhibited when vocalisations are incongruent with the predictive visual context.
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19
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Oyama K, Hori Y, Mimura K, Nagai Y, Eldridge MAG, Saunders RC, Miyakawa N, Hirabayashi T, Hori Y, Inoue KI, Suhara T, Takada M, Higuchi M, Richmond BJ, Minamimoto T. Chemogenetic Disconnection between the Orbitofrontal Cortex and the Rostromedial Caudate Nucleus Disrupts Motivational Control of Goal-Directed Action. J Neurosci 2022; 42:6267-6275. [PMID: 35794012 PMCID: PMC9374131 DOI: 10.1523/jneurosci.0229-22.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/20/2022] [Accepted: 06/05/2022] [Indexed: 11/21/2022] Open
Abstract
The orbitofrontal cortex (OFC) and its major downstream target within the basal ganglia-the rostromedial caudate nucleus (rmCD)-are involved in reward-value processing and goal-directed behavior. However, a causal contribution of the pathway linking these two structures to goal-directed behavior has not been established. Using the chemogenetic technology of designer receptors exclusively activated by designer drugs with a crossed inactivation design, we functionally and reversibly disrupted interactions between the OFC and rmCD in two male macaque monkeys. We injected an adeno-associated virus vector expressing an inhibitory designer receptor, hM4Di, into the OFC and contralateral rmCD, the expression of which was visualized in vivo by positron emission tomography and confirmed by postmortem immunohistochemistry. Functional disconnection of the OFC and rmCD resulted in a significant and reproducible loss of sensitivity to the cued reward value for goal-directed action. This decreased sensitivity was most prominent when monkeys had accumulated a certain amount of reward. These results provide causal evidence that the interaction between the OFC and the rmCD is needed for motivational control of action on the basis of the relative reward value and internal drive. This finding extends the current understanding of the physiological basis of psychiatric disorders in which goal-directed behavior is affected, such as obsessive-compulsive disorder.SIGNIFICANCE STATEMENT In daily life, we routinely adjust the speed and accuracy of our actions on the basis of the value of expected reward. Abnormalities in these kinds of motivational adjustments might be related to behaviors seen in psychiatric disorders such as obsessive-compulsive disorder. In the current study, we show that the connection from the orbitofrontal cortex to the rostromedial caudate nucleus is essential for motivational control of action in monkeys. This finding expands our knowledge about how the primate brain controls motivation and behavior and provides a particular insight into disorders like obsessive-compulsive disorder in which altered connectivity between the orbitofrontal cortex and the striatum has been implicated.
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Affiliation(s)
- Kei Oyama
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Yukiko Hori
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Koki Mimura
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Yuji Nagai
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Mark A G Eldridge
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Richard C Saunders
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Naohisa Miyakawa
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Toshiyuki Hirabayashi
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Yuki Hori
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Ken-Ichi Inoue
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama 484-8506, Japan
| | - Tetsuya Suhara
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Masahiko Takada
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama 484-8506, Japan
| | - Makoto Higuchi
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
| | - Barry J Richmond
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Takafumi Minamimoto
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan
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20
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Grier MD, Yacoub E, Adriany G, Lagore RL, Harel N, Zhang RY, Lenglet C, Uğurbil K, Zimmermann J, Heilbronner SR. Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain. Neuroimage 2022; 255:119200. [PMID: 35427769 PMCID: PMC9446284 DOI: 10.1016/j.neuroimage.2022.119200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/08/2022] [Accepted: 04/07/2022] [Indexed: 11/26/2022] Open
Abstract
Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.
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Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Gregor Adriany
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Russell L Lagore
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Ru-Yuan Zhang
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States.
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21
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Goda N, Hasegawa T, Koketsu D, Chiken S, Kikuta S, Sano H, Kobayashi K, Nambu A, Sadato N, Fukunaga M. Cerebro-cerebellar interactions in non-human primates examined by optogenetic functional magnetic resonance imaging. Cereb Cortex Commun 2022; 3:tgac022. [PMID: 35769971 PMCID: PMC9233902 DOI: 10.1093/texcom/tgac022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 11/14/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is a promising approach for the simultaneous and extensive scanning of whole-brain activities. Optogenetics is free from electrical and magnetic artifacts and is an ideal stimulation method for combined use with fMRI. However, the application of optogenetics in nonhuman primates (NHPs) remains limited. Recently, we developed an efficient optogenetic intracortical microstimulation method of the primary motor cortex (M1), which successfully induced forelimb movements in macaque monkeys. Here, we aimed to investigate how optogenetic M1 stimulation causes neural modulation in the local and remote brain regions in anesthetized monkeys using 7-tesla fMRI. We demonstrated that optogenetic stimulation of the M1 forelimb and hindlimb regions successfully evoked robust direct and remote fMRI activities. Prominent remote activities were detected in the anterior and posterior lobes in the contralateral cerebellum, which receive projections polysynaptically from the M1. We further demonstrated that the cerebro-cerebellar projections from these M1 regions were topographically organized, which is concordant with the somatotopic map in the cerebellar cortex previously reported in macaques and humans. The present study significantly enhances optogenetic fMRI in NHPs, resulting in profound understanding of the brain network, thereby accelerating the translation of findings from animal models to humans.
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Affiliation(s)
- Naokazu Goda
- Division of Cerebral Integration , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences , SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
| | - Taku Hasegawa
- Division of System Neurophysiology , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Laboratory for Imagination and Executive functions , RIKEN Center for Brain Science, Wako, 351-0198, Japan
| | - Daisuke Koketsu
- Division of System Neurophysiology , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
| | - Satomi Chiken
- Department of Physiological Sciences , SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
- Division of System Neurophysiology , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
| | - Satomi Kikuta
- Division of System Neurophysiology , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Neurophysiology , National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, 187-8502, Japan
| | - Hiromi Sano
- Department of Physiological Sciences , SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
- Division of System Neurophysiology , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- International Center for Brain Science , Fujita Health University, Toyoake, 470-1192, Japan
| | - Kenta Kobayashi
- Department of Physiological Sciences , SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
- Section of Viral Vector Development , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
| | - Atsushi Nambu
- Department of Physiological Sciences , SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
- Division of System Neurophysiology , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Section of Viral Vector Development , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences , SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration , National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences , SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
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22
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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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23
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A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure. Brain Sci 2022; 12:brainsci12020260. [PMID: 35204023 PMCID: PMC8870262 DOI: 10.3390/brainsci12020260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 01/31/2022] [Accepted: 02/09/2022] [Indexed: 11/17/2022] Open
Abstract
Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, is constrained. A new transfer learning training strategy was utilized to address the limitations, such as insufficient training data and unsatisfactory model generalization ability, when deep neural networks processing the limited samples of macaque magnetic resonance imaging(MRI). First, the project combines two human brain MRI data modes to pre-train the neural network, in order to achieve faster training and more accurate brain extraction. Then, a residual network structure in the U-Net model was added, in order to propose a ResTLU-Net model that aims to improve the generalization ability of multiple research sites data. The results demonstrated that the ResTLU-Net, combined with the proposed transfer learning strategy, achieved comparable accuracy for the macaque brain MRI extraction tasks on different macaque brain MRI volumes that were produced by various medical centers. The mean Dice of the ResTLU-Net was 95.81% (no need for denoise and recorrect), and the method required only approximately 30–60 s for one extraction task on an NVIDIA 1660S GPU.
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24
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Shahdloo M, Schüffelgen U, Papp D, Miller KL, Chiew M. Model-based dynamic off-resonance correction for improved accelerated fMRI in awake behaving nonhuman primates. Magn Reson Med 2022; 87:2922-2932. [PMID: 35081259 PMCID: PMC9306555 DOI: 10.1002/mrm.29167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/26/2021] [Accepted: 01/03/2022] [Indexed: 11/18/2022]
Abstract
Purpose To estimate dynamic off‐resonance due to vigorous body motion in accelerated fMRI of awake behaving nonhuman primates (NHPs) using the echo‐planar imaging reference navigator, in order to attenuate the effects of time‐varying off‐resonance on the reconstruction. Methods In NHP fMRI, the animal’s head is usually head‐posted, and the dynamic off‐resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off‐resonance at each frame can be approximated as a spatially linear perturbation of the off‐resonance at a reference frame, and is manifested as a relative linear shift in k‐space. Using GRAPPA operators, we estimated these shifts by comparing the navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data. Results The proposed method is shown to successfully estimate spatially low‐order dynamic off‐resonance perturbations, including induced linear off‐resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multislice in vivo acquisitions in awake‐behaving NHPs. Conclusion A method is proposed that does not need sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state‐of‐the‐art human imaging.
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Affiliation(s)
- Mo Shahdloo
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Urs Schüffelgen
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Daniel Papp
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,NeuroPoly Lab, Electrical Engineering Department, Polytechnique Montréal, Montreal, Canada
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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25
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Li X, Zhu Q, Vanduffel W. Submillimeter fMRI reveals an extensive, fine-grained and functionally-relevant scene-processing network in monkeys. Prog Neurobiol 2022; 211:102230. [DOI: 10.1016/j.pneurobio.2022.102230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/20/2021] [Accepted: 01/26/2022] [Indexed: 11/27/2022]
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26
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [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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27
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Yokoyama C, Autio JA, Ikeda T, Sallet J, Mars RB, Van Essen DC, Glasser MF, Sadato N, Hayashi T. Comparative connectomics of the primate social brain. Neuroimage 2021; 245:118693. [PMID: 34732327 PMCID: PMC9159291 DOI: 10.1016/j.neuroimage.2021.118693] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/27/2021] [Accepted: 10/29/2021] [Indexed: 01/13/2023] Open
Abstract
Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior.
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Affiliation(s)
- Chihiro Yokoyama
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan.
| | - Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Takuro Ikeda
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, Oxford University, Oxford, United Kingdom; University of Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - 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, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - David C Van Essen
- Departments of Neuroscience, Washington University Medical School, St Louis, MO, United States of America
| | - Matthew F Glasser
- Departments of Neuroscience, Washington University Medical School, St Louis, MO, United States of America; Department of Radiology, Washington University Medical School, St Louis, MO, United States of America
| | - Norihiro Sadato
- National Institute for Physiological Sciences, Okazaki, Japan; The Graduate University for Advanced Studies (SOKENDAI), Kanagawa, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan; School of Medicine, Kyoto University, Kyoto, Japan.
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28
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Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen JM, Hodge MR, Cler EA, Marcus DM, Barch DM, Yacoub E, Smith SM, Ugurbil K, Van Essen DC. The Human Connectome Project: A retrospective. Neuroimage 2021; 244:118543. [PMID: 34508893 PMCID: PMC9387634 DOI: 10.1016/j.neuroimage.2021.118543] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/13/2021] [Accepted: 08/30/2021] [Indexed: 01/21/2023] Open
Abstract
The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.
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Affiliation(s)
| | | | - Michael P Harms
- Washington University School of Medicine, St. Louis, MO, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre & NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | | | | | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | | | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Michael R Hodge
- Washington University School of Medicine, St. Louis, MO, USA
| | - Eileen A Cler
- Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel M Marcus
- Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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29
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Autio JA, Zhu Q, Li X, Glasser MF, Schwiedrzik CM, Fair DA, Zimmermann J, Yacoub E, Menon RS, Van Essen DC, Hayashi T, Russ B, Vanduffel W. Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection. Neuroimage 2021; 236:118082. [PMID: 33882349 PMCID: PMC8594288 DOI: 10.1016/j.neuroimage.2021.118082] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 02/16/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023] Open
Abstract
Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the PRIME-DE initiative has increased the availability of NHP MRI data and the need for robust multi-subject multi-center analyses. Streamlined acquisition and analysis protocols would accelerate and improve these efforts. However, consensus on minimal standards for data acquisition protocols and analysis pipelines for NHP imaging remains to be established, particularly for multi-center studies. Here, we draw parallels between NHP and human neuroimaging and provide minimal guidelines for harmonizing and standardizing data acquisition. We advocate robust translation of widely used open-access toolkits that are well established for analyzing human data. We also encourage the use of validated, automated pre-processing tools for analyzing NHP data sets. These guidelines aim to refine methodological and analytical strategies for small and large-scale NHP neuroimaging data. This will improve reproducibility of results, and accelerate the convergence between NHP and human neuroimaging strategies which will ultimately benefit fundamental and translational brain science.
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Affiliation(s)
- Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Qi Zhu
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
| | - Xiaolian Li
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium
| | - Matthew F Glasser
- Departments of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstraße 5, 37077 Göttingen, Germany; Perception and Plasticity Group, German Primate Center - Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Damien A Fair
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Jan Zimmermann
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada
| | - David C Van Essen
- Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Brian Russ
- Department of Psychiatry, New York University Langone, New York City, New York, USA; Center for the Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York, USA; Department of Neuroscience, Icahn School of Medicine, Mount Sinai, New York City, New York, USA
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven 3000, 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
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30
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Visualization of iron-rich subcortical structures in non-human primates in vivo by quantitative susceptibility mapping at 3T MRI. Neuroimage 2021; 241:118429. [PMID: 34311068 DOI: 10.1016/j.neuroimage.2021.118429] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/13/2021] [Accepted: 07/22/2021] [Indexed: 02/06/2023] Open
Abstract
Magnetic resonance imaging (MRI) is now an essential tool in the field of neuroscience involving non-human primates (NHP). Structural MRI scanning using T1-weighted (T1w) or T2-weighted (T2w) images provides anatomical information, particularly for experiments involving deep structures such as the basal ganglia and cerebellum. However, for certain subcortical structures, T1w and T2w image contrasts are insufficient for their detection of important anatomical details. To better visualize such structures in the macaque brain, we applied a relatively new method called quantitative susceptibility mapping (QSM), which enhances tissue contrast based on the local tissue magnetic susceptibility. The QSM significantly improved the visualization of important structures, including the ventral pallidum (VP), globus pallidus external and internal segments (GPe and GPi), substantia nigra (SN), subthalamic nucleus (STN) in the basal ganglia and the dentate nucleus (DN) in the cerebellum. We quantified this the contrast enhancement by systematically comparing of contrast-to-noise ratios (CNRs) of QSM images relative to the corresponding T1w and T2w images. In addition, QSM values of some structures were correlated to the age of the macaque subjects. These results identify the QSM method as a straightforward and useful tool for clearly visualizing details of subcortical structures that are invisible with more traditional scanning sequences.
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31
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Bedini M, Baldauf D. Structure, function and connectivity fingerprints of the frontal eye field versus the inferior frontal junction: A comprehensive comparison. Eur J Neurosci 2021; 54:5462-5506. [PMID: 34273134 PMCID: PMC9291791 DOI: 10.1111/ejn.15393] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 06/16/2021] [Accepted: 07/02/2021] [Indexed: 02/01/2023]
Abstract
The human prefrontal cortex contains two prominent areas, the frontal eye field and the inferior frontal junction, that are crucially involved in the orchestrating functions of attention, working memory and cognitive control. Motivated by comparative evidence in non-human primates, we review the human neuroimaging literature, suggesting that the functions of these regions can be clearly dissociated. We found remarkable differences in how these regions relate to sensory domains and visual topography, top-down and bottom-up spatial attention, spatial versus non-spatial (i.e., feature- and object-based) attention and working memory and, finally, the multiple-demand system. Functional magnetic resonance imaging (fMRI) studies using multivariate pattern analysis reveal the selectivity of the frontal eye field and inferior frontal junction to spatial and non-spatial information, respectively. The analysis of functional and effective connectivity provides evidence of the modulation of the activity in downstream visual areas from the frontal eye field and inferior frontal junction and sheds light on their reciprocal influences. We therefore suggest that future studies should aim at disentangling more explicitly the role of these regions in the control of spatial and non-spatial selection. We propose that the analysis of the structural and functional connectivity (i.e., the connectivity fingerprints) of the frontal eye field and inferior frontal junction may be used to further characterize their involvement in a spatial ('where') and a non-spatial ('what') network, respectively, highlighting segregated brain networks that allow biasing visual selection and working memory performance to support goal-driven behaviour.
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Affiliation(s)
- Marco Bedini
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Daniel Baldauf
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
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32
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Wang X, Li XH, Cho JW, Russ BE, Rajamani N, Omelchenko A, Ai L, Korchmaros A, Sawiak S, Benn RA, Garcia-Saldivar P, Wang Z, Kalin NH, Schroeder CE, Craddock RC, Fox AS, Evans AC, Messinger A, Milham MP, Xu T. U-net model for brain extraction: Trained on humans for transfer to non-human primates. Neuroimage 2021; 235:118001. [PMID: 33789137 PMCID: PMC8529630 DOI: 10.1016/j.neuroimage.2021.118001] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 01/21/2023] Open
Abstract
Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.
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Affiliation(s)
- Xindi Wang
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Xin-Hui Li
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Jae Wook Cho
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Brian E Russ
- Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, USA; Department of Psychiatry, New York University School of Medicine, New York City, NY, USA
| | - Nanditha Rajamani
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Alisa Omelchenko
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Lei Ai
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | | | - Stephen Sawiak
- Translational Neuroimaging Laboratory, Department of Physiology, Development and Neuroscience University of Cambridge, Cambridge CB2 3EG, UK
| | - R Austin Benn
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Pamela Garcia-Saldivar
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, México
| | - Zheng Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Science, Shanghai, China; University of Chinese Academy of Science, China
| | - Ned H Kalin
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, 6001 Research Park Blvd, Madison, WI 53719, USA
| | - Charles E Schroeder
- Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, USA
| | - Andrew S Fox
- Department of Psychology, and the California National Primate Research Center, University of California, Davis, One Shields Ave., Davis, CA 95616, USA
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA
| | - Michael P Milham
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA; Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA
| | - Ting Xu
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA.
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33
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Rushmore RJ, Bouix S, Kubicki M, Rathi Y, Rosene DL, Yeterian EH, Makris N. MRI-based Parcellation and Morphometry of the Individual Rhesus Monkey Brain: the macaque Harvard-Oxford Atlas (mHOA), a translational system referencing a standardized ontology. Brain Imaging Behav 2021; 15:1589-1621. [PMID: 32960419 PMCID: PMC8608281 DOI: 10.1007/s11682-020-00357-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Investigations of the rhesus monkey (Macaca mulatta) brain have shed light on the function and organization of the primate brain at a scale and resolution not yet possible in humans. A cornerstone of the linkage between non-human primate and human studies of the brain is magnetic resonance imaging, which allows for an association to be made between the detailed structural and physiological analysis of the non-human primate and that of the human brain. To further this end, we present a novel parcellation method and system for the rhesus monkey brain, referred to as the macaque Harvard-Oxford Atlas (mHOA), which is based on the human Harvard-Oxford Atlas (HOA) and grounded in an ontological and taxonomic framework. Consistent anatomical features were used to delimit and parcellate brain regions in the macaque, which were then categorized according to functional systems. This system of parcellation will be expanded with advances in technology and, like the HOA, will provide a framework upon which the results from other experimental studies (e.g., functional magnetic resonance imaging (fMRI), physiology, connectivity, graph theory) can be interpreted.
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Affiliation(s)
- R Jarrett Rushmore
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Center for Morphometric Analysis, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, MA, 02129, USA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Center for Morphometric Analysis, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, MA, 02129, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Center for Morphometric Analysis, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, MA, 02129, USA
| | - Douglas L Rosene
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Edward H Yeterian
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Center for Morphometric Analysis, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, MA, 02129, USA
- Department of Psychology, Colby College, Waterville, ME, USA
| | - Nikos Makris
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA.
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA.
- Center for Morphometric Analysis, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, MA, 02129, USA.
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34
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Kringelbach ML, Deco G. Brain States and Transitions: Insights from Computational Neuroscience. Cell Rep 2021; 32:108128. [PMID: 32905760 DOI: 10.1016/j.celrep.2020.108128] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/22/2020] [Accepted: 08/19/2020] [Indexed: 11/25/2022] Open
Abstract
Within the field of computational neuroscience there are great expectations of finding new ways to rebalance the complex dynamic system of the human brain through controlled pharmacological or electromagnetic perturbation. Yet many obstacles remain between the ability to accurately predict how and where best to perturb to force a transition from one brain state to another. The foremost challenge is a commonly agreed definition of a given brain state. Recent progress in computational neuroscience has made it possible to robustly define brain states and force transitions between them. Here, we review the state of the art and propose a framework for determining the functional hierarchical organization describing any given brain state. We describe the latest advances in creating sophisticated whole-brain computational models with interacting neuronal and neurotransmitter systems that can be studied fully in silico to predict and design novel pharmacological and electromagnetic interventions to rebalance them in disease.
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Affiliation(s)
- Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia.
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35
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Sheng J, Zhang L, Feng J, Liu J, Li A, Chen W, Shen Y, Wang J, He Y, Xue G. The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases. Neuroimage 2021; 237:118187. [PMID: 34020011 DOI: 10.1016/j.neuroimage.2021.118187] [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: 08/23/2020] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SDBOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSDBOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSDBOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSDBOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSDBOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSDBOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.
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Affiliation(s)
- Jintao Sheng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Liang Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Junjiao Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Jing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Anqi Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang 310000, PR China
| | - Yuedi Shen
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang 310000, PR China
| | - Jinhui Wang
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou 510631, PR China; Key Laboratory of Brain, Ministry of Education, Cognition and Education Sciences (South China Normal University), PR China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China.
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36
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Hayashi T, Hou Y, Glasser MF, Autio JA, Knoblauch K, Inoue-Murayama M, Coalson T, Yacoub E, Smith S, Kennedy H, Van Essen DC. The nonhuman primate neuroimaging and neuroanatomy project. Neuroimage 2021; 229:117726. [PMID: 33484849 PMCID: PMC8079967 DOI: 10.1016/j.neuroimage.2021.117726] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/13/2020] [Accepted: 01/02/2021] [Indexed: 11/29/2022] Open
Abstract
Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, 'ground truth' validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how "functional connectivity" from fMRI and "tractographic connectivity" from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.
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Affiliation(s)
- Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 MI R&D Center 3F, Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan; Department of Neurobiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yujie Hou
- Inserm, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1, Bron, France
| | - Matthew F Glasser
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA; Department of Neuroscience and Radiology, Washington University Medical School, St Louis, MO USA
| | - Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 MI R&D Center 3F, Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Kenneth Knoblauch
- Inserm, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1, Bron, France
| | | | - Tim Coalson
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA
| | - Stephen Smith
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Henry Kennedy
- Inserm, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1, Bron, France; Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences (CAS) Key Laboratory of Primate Neurobiology, CAS, Shanghai, China
| | - David C Van Essen
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA
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Jung B, Taylor PA, Seidlitz J, Sponheim C, Perkins P, Ungerleider LG, Glen D, Messinger A. A comprehensive macaque fMRI pipeline and hierarchical atlas. Neuroimage 2021; 235:117997. [PMID: 33789138 PMCID: PMC9272767 DOI: 10.1016/j.neuroimage.2021.117997] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 01/27/2021] [Accepted: 03/23/2021] [Indexed: 12/14/2022] Open
Abstract
Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging . Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNI's new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI.
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Affiliation(s)
- Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Neuroscience, Brown University, Providence, RI, USA
| | - Paul A Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Caleb Sponheim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Pierce Perkins
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Leslie G Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, USA.
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
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Lepage C, Wagstyl K, Jung B, Seidlitz J, Sponheim C, Ungerleider L, Wang X, Evans AC, Messinger A. CIVET-Macaque: An automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques. Neuroimage 2021; 227:117622. [PMID: 33301944 PMCID: PMC7615896 DOI: 10.1016/j.neuroimage.2020.117622] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/27/2020] [Accepted: 11/28/2020] [Indexed: 12/21/2022] Open
Abstract
The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness from in-vivo human MRI has been extended for processing macaque brains. Processing is performed based on the NIMH Macaque Template (NMT), as the reference template, with the anatomical parcellation of the surface following the D99 and CHARM atlases. The modifications needed to adapt CIVET to the macaque brain are detailed. Results have been obtained using CIVET-macaque to process the anatomical scans of the 31 macaques used to generate the NMT and another 95 macaques from the PRIME-DE initiative. It is anticipated that the open usage of CIVET-macaque will promote collaborative efforts in data collection and processing, sharing, and automated analyses from which the non-human primate brain imaging field will advance.
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Affiliation(s)
- Claude Lepage
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Konrad Wagstyl
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Neuroscience, Brown University, Providence, RI, USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Caleb Sponheim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Leslie Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Xindi Wang
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Alan C Evans
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
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Rapan L, Froudist-Walsh S, Niu M, Xu T, Funck T, Zilles K, Palomero-Gallagher N. Multimodal 3D atlas of the macaque monkey motor and premotor cortex. Neuroimage 2021; 226:117574. [PMID: 33221453 PMCID: PMC8168280 DOI: 10.1016/j.neuroimage.2020.117574] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/19/2020] [Accepted: 11/10/2020] [Indexed: 01/16/2023] Open
Abstract
In the present study we reevaluated the parcellation scheme of the macaque frontal agranular cortex by implementing quantitative cytoarchitectonic and multireceptor analyses, with the purpose to integrate and reconcile the discrepancies between previously published maps of this region. We applied an observer-independent and statistically testable approach to determine the position of cytoarchitectonic borders. Analysis of the regional and laminar distribution patterns of 13 different transmitter receptors confirmed the position of cytoarchitectonically identified borders. Receptor densities were extracted from each area and visualized as its "receptor fingerprint". Hierarchical and principal components analyses were conducted to detect clusters of areas according to the degree of (dis)similarity of their fingerprints. Finally, functional connectivity pattern of each identified area was analyzed with areas of prefrontal, cingulate, somatosensory and lateral parietal cortex and the results were depicted as "connectivity fingerprints" and seed-to-vertex connectivity maps. We identified 16 cyto- and receptor architectonically distinct areas, including novel subdivisions of the primary motor area 4 (i.e. 4a, 4p, 4m) and of premotor areas F4 (i.e. F4s, F4d, F4v), F5 (i.e. F5s, F5d, F5v) and F7 (i.e. F7d, F7i, F7s). Multivariate analyses of receptor fingerprints revealed three clusters, which first segregated the subdivisions of area 4 with F4d and F4s from the remaining premotor areas, then separated ventrolateral from dorsolateral and medial premotor areas. The functional connectivity analysis revealed that medial and dorsolateral premotor and motor areas show stronger functional connectivity with areas involved in visual processing, whereas 4p and ventrolateral premotor areas presented a stronger functional connectivity with areas involved in somatomotor responses. For the first time, we provide a 3D atlas integrating cyto- and multi-receptor architectonic features of the macaque motor and premotor cortex. This atlas constitutes a valuable resource for the analysis of functional experiments carried out with non-human primates, for modeling approaches with realistic synaptic dynamics, as well as to provide insights into how brain functions have developed by changes in the underlying microstructure and encoding strategies during evolution.
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Affiliation(s)
- Lucija Rapan
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | | | - Meiqi Niu
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, New York
| | - Thomas Funck
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, RWTH Aachen, and JARA - Translational Brain Medicine, Aachen, Germany; C. & O. Vogt Institute for Brain Research, Heinrich-Heine-University, 40225 Düsseldorf, Germany.
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Garcia-Saldivar P, Garimella A, Garza-Villarreal EA, Mendez FA, Concha L, Merchant H. PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface. Neuroimage 2020; 227:117671. [PMID: 33359348 DOI: 10.1016/j.neuroimage.2020.117671] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/04/2020] [Accepted: 12/16/2020] [Indexed: 01/18/2023] Open
Abstract
Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.
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Affiliation(s)
- Pamela Garcia-Saldivar
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Arun Garimella
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México; International Institute of Information Technology, Hyderabad, India
| | - Eduardo A Garza-Villarreal
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Felipe A Mendez
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
| | - Hugo Merchant
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
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Liu C, Yen CCC, Szczupak D, Tian X, Glen D, Silva AC. Marmoset Brain Mapping V3: Population multi-modal standard volumetric and surface-based templates. Neuroimage 2020; 226:117620. [PMID: 33307224 PMCID: PMC7908070 DOI: 10.1016/j.neuroimage.2020.117620] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/27/2020] [Accepted: 11/28/2020] [Indexed: 12/14/2022] Open
Abstract
The standard anatomical brain template provides a common space and coordinate system for visualizing and analyzing neuroimaging data from large cohorts of subjects. Previous templates and atlases for the common marmoset brain were either based on data from a single individual or lacked essential functionalities for neuroimaging analysis. Here, we present new population-based in-vivo standard templates and tools derived from multi-modal data of 27 marmosets, including multiple types of T1w and T2w contrast images, DTI contrasts, and large field-of-view MRI and CT images. We performed multi-atlas labeling of anatomical structures on the new templates and constructed highly accurate tissue-type segmentation maps to facilitate volumetric studies. We built fully featured brain surfaces and cortical flat maps to facilitate 3D visualization and surface-based analyses, which are compatible with most surface analyzing tools, including FreeSurfer, AFNI/SUMA, and the Connectome Workbench. Analysis of the MRI and CT datasets revealed significant variations in brain shapes, sizes, and regional volumes of brain structures, highlighting substantial individual variabilities in the marmoset population. Thus, our population-based template and associated tools provide a versatile analysis platform and standard coordinate system for a wide range of MRI and connectome studies of common marmosets. These new template tools comprise version 3 of our Marmoset Brain Mapping Project and are publicly available via marmosetbrainmapping.org/v3.html.
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Affiliation(s)
- Cirong Liu
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Cecil Chern-Chyi Yen
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Diego Szczupak
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Xiaoguang Tian
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health (NIMH/NIH), Bethesda, MD, USA
| | - Afonso C Silva
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
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42
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Yacoub E, Grier MD, Auerbach EJ, Lagore RL, Harel N, Adriany G, Zilverstand A, Hayden BY, Heilbronner SR, Uğurbil K, Zimmermann J. Ultra-high field (10.5 T) resting state fMRI in the macaque. Neuroimage 2020; 223:117349. [PMID: 32898683 PMCID: PMC7745777 DOI: 10.1016/j.neuroimage.2020.117349] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/20/2020] [Accepted: 08/31/2020] [Indexed: 01/02/2023] Open
Abstract
Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.
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Affiliation(s)
- Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States
| | - Edward J Auerbach
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Russell L Lagore
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Gregor Adriany
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Anna Zilverstand
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455, United States
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kamil Uğurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States.
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Li Y, Lee J, Long X, Qiao Y, Ma T, He Q, Cao P, Zhang X, Zheng H. A Magnetic Resonance-Guided Focused Ultrasound Neuromodulation System With a Whole Brain Coil Array for Nonhuman Primates at 3 T. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4401-4412. [PMID: 32833632 DOI: 10.1109/tmi.2020.3019087] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The phased-array radio frequency (RF) coil plays a vital role in magnetic resonance-guided focused ultrasound (MRgFUS) neuromodulation studies, where accurate brain functional stimulations and neural circuit observations are required. Although various designs of phased-array coils have been reported, few are suitable for ultrasound stimulations. In this study, an MRgFUS neuromodulation system comprised of a whole brain coverage non-human primate (NHP) RF coil and an MRI-compatible ultrasound device was developed. When compared to a single loop coil, the NHP coil provided up to a 50% increase in the signal-to-noise ratio (SNR) in the brain and acquired better anatomical image-quality. The NHP coil also demonstrated the ability to achieve higher spatial resolution and reduce distortion in echo-planer imaging (EPI). Ultrasound beam characteristics and transcranial magnetic resonance acoustic radiation force (MR-ARF) were measured for simulated positions, and calculated B0 maps were employed to establish MRI-compatibility. The differences between focused off and on ultrasound techniques were measured using SNR, g-factors, and temporal SNR (tSNR) analyses and all deviations were under 2.3%. The EPI images quality and stable tSNR demonstrated the suitability of the MRgFUS neuromodulation system to conduct functional MRI studies. Last, the time course of the blood oxygen level dependent (BOLD) signal of posterior cingulate cortex in a focused ultrasound neuromodulation study was detected and repeated with MR thermometry.
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Xu T, Nenning KH, Schwartz E, Hong SJ, Vogelstein JT, Goulas A, Fair DA, Schroeder CE, Margulies DS, Smallwood J, Milham MP, Langs G. Cross-species functional alignment reveals evolutionary hierarchy within the connectome. Neuroimage 2020; 223:117346. [PMID: 32916286 PMCID: PMC7871099 DOI: 10.1016/j.neuroimage.2020.117346] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/04/2020] [Accepted: 08/31/2020] [Indexed: 11/22/2022] Open
Abstract
Evolution provides an important window into how cortical organization
shapes function and vice versa. The complex mosaic of changes in brain
morphology and functional organization that have shaped the mammalian cortex
during evolution, complicates attempts to chart cortical differences across
species. It limits our ability to fully appreciate how evolution has shaped our
brain, especially in systems associated with unique human cognitive capabilities
that lack anatomical homologues in other species. Here, we develop a
function-based method for cross-species alignment that enables the
quantification of homologous regions between humans and rhesus macaques, even
when their location is decoupled from anatomical landmarks. Critically, we find
cross-species similarity in functional organization reflects a gradient of
evolutionary change that decreases from unimodal systems and culminates with the
most pronounced changes in posterior regions of the default mode network
(angular gyrus, posterior cingulate and middle temporal cortices). Our findings
suggest that the establishment of the default mode network, as the apex of a
cognitive hierarchy, has changed in a complex manner during human evolution
– even within subnetworks.
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Affiliation(s)
- Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, MD, USA
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Damien A Fair
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Departments of neurosurgery and Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Jonny Smallwood
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Psychology Department, University of York, York, UK
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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Warrington S, Bryant KL, Khrapitchev AA, Sallet J, Charquero-Ballester M, Douaud G, Jbabdi S, Mars RB, Sotiropoulos SN. XTRACT - Standardised protocols for automated tractography in the human and macaque brain. Neuroimage 2020; 217:116923. [PMID: 32407993 PMCID: PMC7260058 DOI: 10.1016/j.neuroimage.2020.116923] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 03/27/2020] [Accepted: 05/01/2020] [Indexed: 01/19/2023] Open
Abstract
We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science. A new software package for standardised and automated cross-species tractography. Homologous white matter bundles in the human and macaque brain. Human white matter tract atlases generated from large datasets (1000 subjects). Tractography protocols are standardised, but preserve individual variability. Generalisability across datasets shown using the HCP and the UK Biobank data.
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Affiliation(s)
- Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK.
| | - Katherine L Bryant
- Donders Institute for Brain, Cognition, & Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Alexandr A Khrapitchev
- CRUK and MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, UK
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging - Department of Experimental Psychology, University of Oxford, UK; Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Marina Charquero-Ballester
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Rogier B Mars
- Donders Institute for Brain, Cognition, & Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK.
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