Abstract
Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.
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Data availability
The raw datasets are available here: unstandardized resting-state fMRI (MultiRat_rest) (https://doi.org/10.18112/openneuro.ds004114.v1.0.0); standardized resting-state fMRI (StandardRat) (https://doi.org/10.18112/openneuro.ds004116.v1.0.0). The pre-processed volumes, time series and quality control files are available here: https://doi.org/10.34973/1gp6-gg97. Image pre-processing, confound correction and connectivity analysis were performed using RABIES 0.3.5 (https://github.com/CoBrALab/RABIES (ref. 14).
Code availability
Jupyter notebooks demonstrating the analysis code are available under the terms of the Apache-2.0 license (https://github.com/grandjeanlab/MultiRat; https://doi.org/10.5281/zenodo.7614670).
Change history
18 April 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41593-023-01328-1
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Acknowledgements
This research was enabled, in part, by support provided by Compute Ontario (https://www.computeontario.ca/) and Compute Canada (www.computecanada.ca). For the purpose of open access, we have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This research was funded by: the National Institutes of Health (K01EB023983, R03DA042971, R21AG065819, K25DA047458, I015I01CX000642-04, R01NS085200, R01MH098003, RF1MH114224, T32AA007573, R01MH067528, P30NS05219, T32GM007205, R01MH111416, R01NS078095, R01EB029857, F31 MH115656 and 1R21MH116473-01A1); the Wellcome Trust (212934/Z/18/Z, 109062/Z/15/Z, 110027/Z/15/Z, 204814/Z/16/Z and 203139/Z/16/Z); the Dutch Research Council (OCENW.KLEIN.334, 021.002.053, 016.130.662 and 016.168.038); the German Research Foundation (SA 1869/15-1, SA 2897/2-1, SFB 1436/B06, SFB874/B3 project no.: 122679504, SFB 1280/A04 project no.: 316803389); the French National Research Agency (ANR-15-IDEX-02 and ANR-11-INBS-0006); Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (IN212219, IA202120 and IA201622); the UK Medical Research Council (MR/N013700/1 and 1653552); the Portuguese Foundation for Science and Technology (LISBOA-01-0145-FEDER-022170 and 275-FCT-PTDC/BBB/IMG/5132/2014); the Swiss National Science Foundation (PCEFP3_203005 and PCEFP2_194260); King’s College London, Biotechnology and Biological Sciences Research Council (BB/N009088/1); the European Community’s Seventh Framework Program (FP7/2007-2013); TACTICS (278948); the Brain and Behaviour Foundation (NARSAD, 25861); the Dutch Brain Foundation (F2014(1)-06); the National Science Foundation (DMR-1644779, DMR-1533260, DMR-2128556); the Human Brain Project (945539); the Canadian Institutes of Health Research (PJT-148751, PJT-173442 and MOP-102599); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2020-05917, RGPIN-375457-09 and RGPIN-2015-05103); the Horizon 2020 Framework Programme of the European Union (740264 and 802371); the Academy of Finland (298007); the European Research Council (679058 and 802371); Innosuisse (18546.1); the Research Foundation – Flanders (12W1619N, FWO-G048917N and G045420N); the Stichting Alzheimer Onderzoek (SAO-FRA-20180003); Special Research Programmes (1158); CIBER-BBN; Instituto de Salut Carlos III - FEDER (PI18/00893); Versus Arthritis (20777); the Brain Behavior Foundation (25861); the Telethon Foundation (GGP19177); Eurostars (E!114985); the Brain Canada Foundation platform support grant (PSG15-3755); the National Natural Science Foundation of China (81950410637); Science Foundation Ireland (20/FFPP/8799); Trinity Foundation (RCN 20028626); Consejo Nacional de Ciencia y Tecnologia Ciencia de Frontera (171874); PAPIIT-DGAPA (IA208022); Fonds de recherche du Québec – Nature et technologies; the Forrest Research Foundation; the Australian National Imaging Facility; the University of Western Australia; the National Health and Medical Research Council of the Australian Government; the Perron Institute for Neurological and Translational Science; McGill University’s Faculty of Medicine; the Seaver Foundation; Autism Speaks; the Centre d’Imagerie BioMédicale of the UNIL, UNIGE, HUG, CHUV, EPFL; the Leenaards and Louis-Jeantet Foundations; the DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain; the Synapsis Foundation; the Simons Initiative for the Developing Brain; the Patrick Wild Centre; the Department of Biotechnology India; Utrecht University High Potential Program; ERA-NET NEURON Neuromarket; Mannheim Advanced Clinician Scientist Program; ICON – Interfaces and Interventions in Complex Chronic Conditions; the Werner Siemens Foundation; the Lisboa Regional Operational Programme; the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT); ShanghaiTech University; the Shanghai Municipal Government; and the Interdisciplinary Center for Clinical Research Münster (PIX).
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Contributions
J.G. designed, planned and executed the study and wrote the manuscript. G.D.G., G.A.D. and M.M.C. provided the software and hardware environment for the analysis. All authors contributed experimental data and edited the manuscript. A.H. initiated the study.
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Competing interests
A.S. is an employee of Bruker, the manufacturer of preclinical MRI systems used for the acquisition of most of the datasets in this collection. E.L.B. is a consultant for Bruker. B.V. is an employee of Theranexus. S.Z., A.D. and N.B. are employees of Novartis Pharma AG. T.N. is founder and CEO of MRI.TOOLS GmbH. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Age and weight distributions.
Age (a) and weight (b) distribution for the rats in the MultiRat_rest collection.
Extended Data Fig. 2 Quality control examples.
Failed quality controls for anatomical to template registrations (a) and functional to anatomical registrations (b). The top rows are the moving objects, bottom rows are the reference objects. The red lines indicate the outlines of the other object. Four slices along the sagittal, axial, and coronal axis are shown for each case.
Extended Data Fig. 3 Temporal signal-to-noise ratio.
Temporal signal-to-noise ratio in the sensory cortex (tSNR S1) in the MultiRat_rest dataset collection as a function of (a) magnetic field strength, (b) repetition time, (c) echo time, (d) temporal signal-to-noise ratio in the striatum.
Extended Data Fig. 4 Framewise displacement.
MFW in the MultiRat_rest dataset collection as a function of (a) strain, (b) anesthesia, (c) breathing rate, (d) maximal framewise displacement.
Extended Data Fig. 5 FC in the default-mode network.
The reference seed is positioned in the anterior cingulate cortex (Fig. 2a), the specific region-of-interest is positioned 3.3 mm posterior in the cingulate cortex and the nonspecific region-of-interest is positioned in the S1bf.
Extended Data Fig. 6 StandardRat dataset description.
a. Strain. b. Sex. c. Field strength. d. Weight. e. Breathing rate as a function of MFW. f. FC specificity as a function of confound correction models.
Extended Data Fig. 7 FC incidence.
Incidence of FC at the group level in the StandardRat collection for four selected seeds (n = 21 datasets, n ~ 10 subjects per dataset). Connectivity incidence is improved in the StandardRat collection relative to MultiRat_rest (Fig. 3).
Extended Data Fig. 8 Between-datasets connectivity comparisons.
FC category comparison between MultiRat_rest and StandardRat (a) and between the awake datasets of MultiRat_rest and the awake dataset from Lui et al. 2020 (b).
Extended Data Fig. 9 Connectivity specificity as a function of breathing rate and signal-to-noise ratio.
FC specificity as a function of binned breathing rate (a) AND temporal signal-to-noise ratio (b) in the StandardRat collection. The percentage of each condition is size and color-coded. High levels of connectivity specificity were achieved in scans where the breathing rates were in the 84 to 114 bpm range. Similarly, higher connectivity specificity incidences were found when the cortical temporal signal-to-noise ratio was > 53. These observations support the notion of an optimal breathing rate when applying the StandardRat protocol, along with temporal signal-to-noise ratio and movement targets.
Extended Data Fig. 10 Group independent components analysis.
Plausible independent components overlapping with known rodent networks, obtained after group-level decomposition with n = 20 components. Labels are based on the SIGMA anatomical atlas.
Supplementary information
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Supplementary tables 1–3.
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Grandjean, J., Desrosiers-Gregoire, G., Anckaerts, C. et al. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci 26, 673–681 (2023). https://doi.org/10.1038/s41593-023-01286-8
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DOI: https://doi.org/10.1038/s41593-023-01286-8
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