Evidence map›Paper›PMID 32276072›Full record

ArticleNeuroImage2020

Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing.

Joonas A Autio, Matthew F Glasser, Takayuki Ose, Chad J Donahue, Matteo Bastiani, Masahiro Ohno, Yoshihiko Kawabata, Yuta Urushibata, Katsutoshi Murata, Kantaro Nishigori and 11 more

Abstract read
In one paragraph

Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Brainana: an end-to-end preprocessing framework for macaque neuroimaging.bioRxiv : the preprint server for biology · 2026
    Article
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  7. Review
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  9. Article
  10. Article
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  13. Spectral imprint of structural embedding in effective connectivity.bioRxiv : the preprint server for biology · 2025
    Article
  14. Article
  15. Article
  16. Geometric influences on the regional organization of the mammalian brain.bioRxiv : the preprint server for biology · 2025
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  17. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Joonas A AutioLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
Matthew F GlasserDepartment of Neuroscience, Washington University School of Medicine, St Louis, Missouri, USA; Department of Radiology, Washington University School of Medicine, St Louis, MO, USA; St. Luke's Hospital, St. Louis, Missouri, USA.
Takayuki OseLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
Chad J DonahueDepartment of Neuroscience, Washington University School of Medicine, St Louis, Missouri, USA.
Matteo BastianiWellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK.
Masahiro OhnoLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
Yoshihiko KawabataTakashima Seisakusho Co., Ltd, Tokyo, Japan.
Yuta UrushibataSiemens Healthcare Japan, Tokyo, Japan.
Katsutoshi MurataSiemens Healthcare Japan, Tokyo, Japan.
Kantaro NishigoriLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Sumitomo Dainippon Pharma Co., Ltd, Osaka, Japan.
Masataka YamaguchiLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Sumitomo Dainippon Pharma Co., Ltd, Osaka, Japan.
Yuki HoriLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
Atsushi YoshidaLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
Yasuhiro GoCognitive Genomics Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Japan; Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan; Department of Physiological Sciences, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Japan.
Timothy S CoalsonDepartment of Neuroscience, Washington University School of Medicine, St Louis, Missouri, USA.
Saad JbabdiWellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
Stamatios N SotiropoulosWellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK.
Henry KennedyUniversité Lyon, Université Claude Bernard Lyon, France; Inserm, Stem Cell and Brain Research Institute, Bron, France; Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
Stephen SmithWellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
David C Van EssenDepartment of Neuroscience, Washington University School of Medicine, St Louis, Missouri, USA.
Takuya HayashiLaboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan. Electronic address: takuya.hayashi@riken.jp.

Funding

RECONSTRUCTIONS AND REPRESENTATIONS OF CEREBRAL CORTEXR01MH060974 · NIMH · WASHINGTON UNIVERSITY · PI VAN ESSEN, DAVID C · 1999 to 2024
$12.7M
21ST CENTURY IMAGING SCIENCES: GRADUATE STUDENT TRAININGT32EB014855 · NIBIB · WASHINGTON UNIVERSITY · PI JOSEPH P CULVER, JOSEPH A O'SULLIVAN · 2012 to 2026
$3.1M
Non-invasive Multi-modal Parcellation of the Human Cerebral CortexF30MH097312 · NIMH · WASHINGTON UNIVERSITY · PI GLASSER, MATTHEW FREDERICK · 2012 to 2014
$105k
Medical Research Council MR/L009013/1NIBIB NIH HHS T32 EB014855NIMH NIH HHS F30 MH097312NIMH NIH HHS R01 MH060974Wellcome Trust 203139
6 · The paper itself

Abstract

Macaque monkeys are an important animal model where invasive investigations can lead to a better understanding of the cortical organization of primates including humans. However, the tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the smaller macaque brain, high spatial, temporal, and angular resolutions combined with high signal-to-noise ratio are required to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enables adaptation of the Human Connectome Project (HCP) image acquisition protocols to the in-vivo macaque brain. In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available in studies of the human brain.

Indexed as

AnimalsBrainConnectomeImage Processing, Computer-AssistedMacaca fascicularisMacaca fuscataMacaca mulattaMagnetic Resonance ImagingNeural PathwaysCortexDiffusionHuman connectome projectMacaqueParallel imagingPrimateResting-state

Identifiers

PMID32276072
PMCPMC7116593

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.