Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection

•Non-human primate MRI standardization.•Poor reproducibility in non-human primate resting-state fMRI.•Guidelines enable improved and more reproducible MRI measures.•Convergence between non-human primate and human neuroimaging strategies. Recent methodological advances in MRI have enabled substantial...

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Published inNeuroImage (Orlando, Fla.) Vol. 236; p. 118082
Main Authors Autio, Joonas A., Zhu, Qi, Li, Xiaolian, Glasser, Matthew F., Schwiedrzik, Caspar M., Fair, Damien A., Zimmermann, Jan, Yacoub, Essa, Menon, Ravi S., Van Essen, David C., Hayashi, Takuya, Russ, Brian, Vanduffel, Wim
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.08.2021
Elsevier Limited
Elsevier
Subjects
Algorithms
Animal cognition
Animals
Biobanks
Brain - anatomy & histology
Brain - diagnostic imaging
Brain - physiology
Brain research
Data acquisition
Data collection
Data processing
Echo-Planar Imaging - methods
Echo-Planar Imaging - standards
Experiments
Functional Neuroimaging - methods
Functional Neuroimaging - standards
Laboratory animals
Macaca mulatta
Magnetic fields
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
Medical imaging
MRI
Multi-site
Neuroimaging
Neuroimaging - methods
Neuroimaging - standards
Non-human primate
Physiology
PRIME-DE
Reproducibility
Reproducibility of Results
Software
Standardization
United Kingdom > UK
PRIME-DE
MRI
Standardization
Non-human primate
Multi-site
Online AccessGet full text

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Abstract •Non-human primate MRI standardization.•Poor reproducibility in non-human primate resting-state fMRI.•Guidelines enable improved and more reproducible MRI measures.•Convergence between non-human primate and human neuroimaging strategies. 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.
AbstractList 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.
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.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.
•Non-human primate MRI standardization.•Poor reproducibility in non-human primate resting-state fMRI.•Guidelines enable improved and more reproducible MRI measures.•Convergence between non-human primate and human neuroimaging strategies. 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.
ArticleNumber 118082
Author Fair, Damien A.
Menon, Ravi S.
Glasser, Matthew F.
Autio, Joonas A.
Schwiedrzik, Caspar M.
Yacoub, Essa
Russ, Brian
Vanduffel, Wim
Li, Xiaolian
Zimmermann, Jan
Hayashi, Takuya
Zhu, Qi
Van Essen, David C.
AuthorAffiliation j Department of Psychiatry, New York University Langone, New York City, New York, USA
h Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
o Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
b Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium
g Perception and Plasticity Group, German Primate Center — Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
k Center for the Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York, USA
m Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
d Departments of Radiology, Washington University School of Medicine, St. Louis, MO, USA
a Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
f Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen – A Joint Initiative of the University M
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/33882349$$D View this record in MEDLINE/PubMed
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Copyright © 2021. Published by Elsevier Inc.
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Keywords PRIME-DE
MRI
Standardization
Non-human primate
Multi-site
Language English
License This is an open access article under the CC BY license.
Copyright © 2021. Published by Elsevier Inc.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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content type line 14
content type line 23
Equal contributions
Credit authorship contribution statement
Joonas A. Autio: Conceptualization, Investigation, Formal Analysis, Writing-original draft, review & editing. Qi Zhu: Investigation, Formal Analysis, Review & editing. Xiaolian Li: Investigation, Formal Analysis, Review & editing. Matthew F. Glasser: Conceptualization, Review & editing. Caspar M. Schwiedrzik: Review & editing. Essa Yacoub: Funding acquisition, Review & editing. Damien A. Fair: Review & editing. Jan Zimmermann: Review & editing. Ravi S. Menon: Review & editing. David C. Van Essen: Conceptualization, Funding acquisition, Review & editing. Takuya Hayashi: Conceptualization, Funding acquisition, Investigation, Formal Analysis, Review & editing. Brian Russ: Writing-original draft, Review & editing. Wim Vanduffel: Conceptualization, Funding acquisition, Investigation, Formal Analysis, Writing-original draft, Review & editing.
ORCID 0000-0003-3648-7554
0000-0002-0993-3400
0000-0002-2232-9259
0000-0003-3345-6074
0000-0003-0320-1257
0000-0002-7916-0263
0000-0001-7044-4721
OpenAccessLink https://www.sciencedirect.com/science/article/pii/S1053811921003591
PMID 33882349
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PublicationTitle NeuroImage (Orlando, Fla.)
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Snippet •Non-human primate MRI standardization.•Poor reproducibility in non-human primate resting-state fMRI.•Guidelines enable improved and more reproducible MRI...
Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the...
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SubjectTerms Algorithms
Animal cognition
Animals
Biobanks
Brain - anatomy & histology
Brain - diagnostic imaging
Brain - physiology
Brain research
Data acquisition
Data collection
Data processing
Echo-Planar Imaging - methods
Echo-Planar Imaging - standards
Experiments
Functional Neuroimaging - methods
Functional Neuroimaging - standards
Laboratory animals
Macaca mulatta
Magnetic fields
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
Medical imaging
MRI
Multi-site
Neuroimaging
Neuroimaging - methods
Neuroimaging - standards
Non-human primate
Physiology
PRIME-DE
Reproducibility
Reproducibility of Results
Software
Standardization
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Title Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection
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