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Neuroimage. 2019 Oct 12:116278. doi: 10.1016/j.neuroimage.2019.116278. [Epub ahead of print]

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis.

Author information

1
Singapore Bioimaging Consortium, Agency for Science, Technology and Research, 11 Biopolis Way, 138667, Singapore. Electronic address: Joanes.Grandjean@radboudumc.nl.
2
Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Centre for Neuroscience and Cognitive Systems @ UNITN, 38068, Rovereto, Italy; CIMeC, Centre for Mind/Brain Sciences, University of Trento, 38068, Rovereto, Italy.
3
Bio-Imaging Lab, University of Antwerp, CDE, Universiteitsplein 1, 2610, Antwerp, Belgium.
4
Douglas Mental Health University Institute & Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
5
Commissariat à l'Énergie Atomique et Aux Énergies Alternatives (CEA), Direction de la Recherche Fondamentale (DRF), Institut François Jacob, MIRCen, Fontenay-aux-roses, France; Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Université Paris-Saclay UMR 9199, Neurodegenerative Diseases Laboratory, Fontenay-aux-Roses, France.
6
Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Killianstr. 5a, 79106, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Georges-Köhler-Allee 80, 79110, Freiburg, Germany.
7
Institute for Biomedical Engineering, University and ETH Zürich, Wolfgang-Pauli-Str. 27, 8093, Zürich, Switzerland; Institute of Pharmacology and Toxicology, University of Zürich, Winterthurerstrasse 190, 8057, Zürich, Switzerland.
8
Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
9
Department of Biophysics and Nuclear Medicine, University Hospital Strasbourg, 67000, Strasbourg, France.
10
Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan; Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.
11
Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Fahrstraße 17, 91054, Erlangen, Germany.
12
Singapore Bioimaging Consortium, Agency for Science, Technology and Research, 11 Biopolis Way, 138667, Singapore; Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom.
13
Functional and Molecular Imaging Team, Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Anagawa 4-9-1, Inage, Chiba-city, Chiba, 263-8555, Japan.
14
Bio-Imaging Lab, University of Antwerp, CDE, Universiteitsplein 1, 2610, Antwerp, Belgium; Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, O&N4 Herestraat 49 Box 602, 3000, Leuven, Belgium.
15
Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan; Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.
16
Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia; Centre for Medical Image Computing, Department of Computer Science, & Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
17
Singapore Bioimaging Consortium, Agency for Science, Technology and Research, 11 Biopolis Way, 138667, Singapore.
18
Hospital for Sick Children and Department of Medical Biophysics, The University of Toronto, Toronto, Ontario, Canada.
19
Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia; Brainnetome Centre, Institute of Automation, Chinese Academy of Sciences, Beijing & Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China.
20
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany.
21
Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan; Laboratory for Marmoset Neural Architecture, RIKEN Brain Science Institute, Wako, Saitama, 351-0198, Japan.
22
Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zürich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
23
Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Centre for Neuroscience and Cognitive Systems @ UNITN, 38068, Rovereto, Italy.

Abstract

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

KEYWORDS:

Connectome; Default-mode network; Functional connectivity; ICA; Seed-based

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