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

Grandjean J, Canella C, Anckaerts C, Ayrancı G, Bougacha S, Bienert T, Buehlmann D, Coletta L, Gallino D, Gass N, Garin CM, Nadkarni NA, Hübner NS, Karatas M, Komaki Y, Kreitz S, Mandino F, Mechling AE, Sato C, Sauer K, Shah D, Strobelt S, Takata N, Wank I, Wu T, Yahata N, Yeow LY, Yee Y, Aoki I, Chakravarty MM, Chang WT, Dhenain M, von Elverfeldt D, Harsan LA, Heß A, Jiang T, Keliris GA, Lerch JP, Meyer-Lindenberg A, Okano H, Rudin M, Sartorius A, Van der Linden A, Verhoye M, Weber-Fahr W, Wenderoth N, Zerbi V, Gozzi A (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 205

Article Number: 116278

DOI: 10.1016/j.neuroimage.2019.116278

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.

Authors with CRIS profile

Involved external institutions

Singapore Bioimaging Consortium (SBIC) Singapore (SG) Italian Institute of Technology / Istituto Italiano di Tecnologia (IIT) Italy (IT) University of Antwerp / Universiteit Antwerpen Belgium (BE) McGill University Canada (CA) French Alternative Energies and Atomic Energy Commission (CEA) France (FR) Albert-Ludwigs-Universität Freiburg Germany (DE) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich Switzerland (CH) Universitätsklinikum Mannheim Germany (DE) Central Institute for Experimental Animals (CIEA) Japan (JP) National Institute of Radiological Sciences (NIRS) / 放射線医学総合研究所 Japan (JP) University of Queensland Australia (AU) The Hospital for Sick Children (SickKids) Canada (CA) Hôpitaux universitaires de Strasbourg (HUS) / University Hospital Strasbourg France (FR) Ruprecht-Karls-Universität Heidelberg Germany (DE) Keio University Japan (JP)

How to cite

APA:

Grandjean, J., Canella, C., Anckaerts, C., Ayrancı, G., Bougacha, S., Bienert, T.,... Gozzi, A. (2020). Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis. NeuroImage, 205. https://dx.doi.org/10.1016/j.neuroimage.2019.116278

MLA:

Grandjean, Joanes, et al. "Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis." NeuroImage 205 (2020).

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