| Literature DB >> 31614221 |
Joanes Grandjean1, Carola Canella2, Cynthia Anckaerts3, Gülebru Ayrancı4, Salma Bougacha5, Thomas Bienert6, David Buehlmann7, Ludovico Coletta2, Daniel Gallino4, Natalia Gass8, Clément M Garin5, Nachiket Abhay Nadkarni5, Neele S Hübner6, Meltem Karatas9, Yuji Komaki10, Silke Kreitz11, Francesca Mandino12, Anna E Mechling6, Chika Sato13, Katja Sauer11, Disha Shah14, Sandra Strobelt11, Norio Takata15, Isabel Wank11, Tong Wu16, Noriaki Yahata13, Ling Yun Yeow17, Yohan Yee18, Ichio Aoki13, M Mallar Chakravarty19, Wei-Tang Chang17, Marc Dhenain5, Dominik von Elverfeldt6, Laura-Adela Harsan20, Andreas Hess11, Tianzi Jiang21, Georgios A Keliris3, Jason P Lerch22, Andreas Meyer-Lindenberg23, Hideyuki Okano24, Markus Rudin25, Alexander Sartorius8, Annemie Van der Linden3, Marleen Verhoye3, Wolfgang Weber-Fahr8, Nicole Wenderoth26, Valerio Zerbi26, Alessandro Gozzi27.
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.Entities:
Keywords: Connectome; Default-mode network; Functional connectivity; ICA; Seed-based
Mesh:
Year: 2019 PMID: 31614221 PMCID: PMC7116112 DOI: 10.1016/j.neuroimage.2019.116278
Source DB: PubMed Journal: Neuroimage ISSN: 1053-8119 Impact factor: 6.556