# A consensus protocol for functional connectivity analysis in the rat brain

Joanes Grandjean, Gabriel Desrosiers-Gregoire, Cynthia Anckaerts, Diego Angeles-Valdez, Fadi Ayad, David A Barrière, Ines Blockx, Aleksandra Bortel, Margaret Broadwater, Beatriz M Cardoso, et al.

► To cite this version:

Joanes Grandjean, Gabriel Desrosiers-Gregoire, Cynthia Anckaerts, Diego Angeles-Valdez, Fadi Ayad, et al.. A consensus protocol for functional connectivity analysis in the rat brain. Nature Neuroscience, 2023, 26 (4), pp.673-681. 10.1038/s41593-023-01286-8 . hal-04268925

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
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**Fig. 2 | FC specificity.** a, Diagram illustrating the logic behind FC specificity. The sensory (barrel field, S1bf) area (blue) chiefly projects to the contralateral homotopic area (light blue) but not to the ACA area (purple). b, Example of temporal dynamics in the resting-state signal. Correlated signal between the ipsilateral and contralateral S1bf and anti-correlated signal from the ACA. c, Distribution of FC categories as a function of confound correction models. d, FC in left S1bf relative to specific (right S1bf) and non-specific (ACA) regions of interest using the global regression correction model. Dots represent

scans (n = 638 rats); dotted lines indicate the thresholds used to delineate the categories. e, Distribution of connectivity categories as a function of anesthesia. Example of individual seed-based analysis maps for each connectivity category. f, Distribution of connectivity categories as a function of imaging sequence (EPI, echo-planar imaging; GE, gradient echo; SE, spin echo). Group-level FC incidence map (n = 65 datasets). g, Example of individual seed-based analysis maps for each connectivity category. a.u., arbitrary units; ROI, region of interest; WMCSFs, white-matter + cerebrospinal fluid.
<figure><img src="image_10.png" /><figcaption>**Fig. 3 | Incidence of FC at the group level (n = 65 datasets of n - 10 subjects per dataset) for four seeds.** Cpu, caudate-putamen; MOp, primary motor area. MOp and CPu seeds were generally observed in 50–75% of the datasets.</figcaption></figure>

image acquisitions (magnetic field strength, sequence and sequence parameters; Fig. 1d,f,g). Notably, there was a large sex bias in favor of males (Fig. 1a). Similarly, the distribution of anesthesia protocols was also in line with current trends in the field (Fig. 1c)⁹. Despite the heterogeneous distribution of the acquisition parameters and ensuing image quality (Fig. 1g,h,i), 638 of 646 of the scans passed pre-processing quality control (one scan with excessive motion, one empty scan and six scans with failed image registrations; Fig. 1j,k and Extended Data Fig. 2). As further quality controls, we described temporal signal-to-noise (Extended Data Fig. 3) and motion parameters (Extended Data Fig. 4). Overall, we found that the aggregated datasets represent current rodent fMRI acquisition trends⁹. Moreover, given the low exclusion rate due to mis-registration, we concluded that the RABIES toolbox can be effectively employed to pre-process rat datasets despite widely varying acquisition parameters¹⁴. This paves the way for reproducible and interoperable data processing across sites.

We focused on examining FC in the sensory cortex, as sensory networks are robust to assess anesthesia effects in the anesthesia depth range typically used in fMRI¹⁵. More specifically, we evaluated

the specificity of the connectivity of the S1 barrel field area (S1bf) using two complementary criteria as indexes of accurate FC identification (Fig. 2a,b)¹². The first criterion was the strong connectivity between inter-hemispheric sensory cortices (barrel field, S1bf). Indeed, in both humans and animals, dating back to the original description of FC¹⁶, most networks, including sensory-motor networks, have a bilateral homotopic organization. The second criterion was a weak or anti-correlation between S1bf and the anterior cingulate area (ACA). The ACA is a major node in the task-negative rodent default mode network. Task-positive (as the S1bf-associated sensory network¹⁷,¹⁸) and task-negative networks are generally non-correlated or anti-correlated¹⁹.

FC was evaluated for each animal and divided into four categories based on these two criteria referred above: specific, non-specific, spurious and no connectivity. Five confound correction models were tested (Supplementary Table 2 and Fig. 2c). The global signal regression (GSR) nuisance model was the one that performed the best for specific connectivity detection (40.8% of the animals with specific connectivity, 11.8% as non-specific, 13.6% as spurious and 33.9% as containing no detectable FC; Fig. 2c,d,g). To test the generalizability of the specificity metric to other networks, we implemented the same quality control metric to the cingulate cortex, a node of the rodent default mode network, which uncovered lower connectivity specificity (22.1%; Extended Data Fig. 5). Because network inference is often assessed at the group level rather than at the individual level, we performed a one-sample *t*-test per dataset to estimate the incidence of contralateral connectivity detected within groups, relative to the S1bf seed as well as three other seeds (Fig. 3). Up to 70% of the 65 datasets presented limited evidence of contralateral connectivity relative to the seeds, and 50% of the datasets captured the features of a larger sensory network at the group level. We conclude that rat datasets do not capture FC equally, similar to what we reported in the mouse¹². We also found that GSR enhances the incidence of specific connectivity of S1bf seeds, so we decided to use this confound model for the remainder of our analysis.

Our observations underline the need for an improved acquisition protocol to maximize individual-level inferences to potentiate discovery in experimental network neuroscience. To address this, we evaluated parameters associated with increased specificity incidence in the MultiRat\_rest collection. Because our analysis relied on categorical data, we tested for differences against the expected frequency distribution of the four connectivity categories as a function of anesthesia and sequence categories using the $\chi^2$ test. Medetomidine/isoflurane anesthesia combination condition was enriched in scans categorized as specific (Fig. 2e; 92/187 scans, $\chi^2$ test connectivity category-anesthesia: $\varphi = 0.27$, degrees of freedom (dof) = 15, $g = 92.38$, $P = 3.5 \times 10^{-13}$). The use of gradient echo imaging sequence was also associated with higher specificity incidence (Fig. 2f; 241/568 scans, $\chi^2$ test connectivity category-sequence: $\varphi = 0.11$, dof = 3, $g = 16.00$, $P = 0.001$). Based on these observations, we devised an anesthesia protocol derived from dataset ds01031 (9/10 specific scans) and an imaging sequence based on ds01028 (8/10 specific scans), acquired on a 4.7T mid-field system. The sequence selection is justified as it allows for the detection of specific connectivity on one of the lower-field systems used in this collection and should also perform well for higher-field systems. We optimized relevant sequence parameters (echo time, flip angle and bandwidth) as a function of field strength (Supplementary Table 1). We hypothesized that this protocol would enhance functional specificity while being compatible with lower-field systems that continue to represent a relevant share of the systems used (Fig. 1d)⁹.

Using this consensus protocol, we curated the StandardRat collection of 21 datasets obtained across 20 centers. This consisted of *n* = 209 rats (93/116 female/male), mainly Wistar (189/209), aged -2 months (Extended Data Fig. 6). Dataset acquisitions were performed at magnetic field strengths ranging from 4.7T to 17.2T. Pre-processing
⁶µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium. ⁷Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico. ⁸Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada. ⁹McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada. ¹⁰Montreal Neurological Institute, McGill University, Montreal, QC, Canada. ¹¹UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction, Physiologie de la reproduction et des comportements, Centre de recherche INRAE de Nouzilly, Tours, France. ¹²Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada. ¹³Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ¹⁴Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

¹⁵Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ¹⁶Preclinical MRI, Champalmaud Research, Champalmaud Centre for the Unknown, Lisbon, Portugal. ¹⁷Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France. ¹⁸Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México.

¹⁹Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. ²⁰Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany.

²¹Institute Biomedical Technology (IBiTech), Electronics and Information Systems (ELIS), Ghent University, Gent, Belgium. ²²Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA. ²³Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA. ²⁴Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA. ²⁵Centre for Advanced Biomedical Imaging, University College London, London, UK.

²⁶Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland. ²⁷Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland. ²⁸CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. ²⁹Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

³⁰Musculoskeletal Diseases Department, Novartis Institutes for BioMedical Research, Basel, Switzerland. ³¹Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland. ³²Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany. ³³Seaver Autism Center for Research & Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA. ³⁴Department of Neuroscience and Pharmacology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. ³⁵Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia. ³⁶Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia.

³⁷Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA. ³⁸Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA. ³⁹Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany. ⁴⁰Department of Psychology, University of Cambridge, Cambridge, UK. ⁴¹Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany. ⁴²Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK. ⁴³Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany. ⁴⁴Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK. ⁴⁵School of Life Sciences, University of Nottingham, Nottingham, UK. ⁴⁶Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK. ⁴⁷Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China. ⁴⁸Magnetic Imaging Resonance Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain. ⁴⁹Center for Translational Neuroimaging, Northeastern University, Boston, MA, USA. ⁵⁰Khokhar Lab, Department of Anatomy and Cell Biology, Western University, London, ON, Canada. ⁵¹Laboratory of Acute Brain Injury and Therapeutic Strategies, Department of Neuroscienceselstituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy. ⁵²Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.

⁵³Facultad de Medicina, Universidad Autónoma de Querétaro, Querétaro, México. ⁵⁴Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México. ⁵⁵Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan. ⁵⁶Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France. ⁵⁷Febo Laboratory, Department of Psychiatry, University of Florida, Gainesville, FL, USA. ⁵⁸School of Medicine, University of Nottingham, Nottingham, UK. ⁵⁹Comparative Medicine Unit, Trinity College Dublin, Dublin, Ireland. ⁶⁰Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.

⁶¹Translational Imaging, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany. ⁶²Psychiatric neurosciences, Center for Psychiatric Neuroscience, Lausanne University and University Hospital Center, Unicentre, Lausanne, Switzerland. ⁶³Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany. ⁶⁴Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland. ⁶⁵Centre for Microscopy, Characterisation & Analysis, Research Infrastructure Centres, University of Western Australia, Nedlands, WA, Australia. ⁶⁶NeuroSpin, CEA Saclay, Paris, France. ⁶⁷iHuman Institute, ShanghaiTech University, Shanghai, China. ⁶⁸Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany. ⁶⁹Faculty of Biology and Psychology, Georg-August University of Göttingen, Göttingen, Germany. ⁷⁰DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany. ⁷¹Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK. ⁷²Patrick Wild Centre, University of Edinburgh, Edinburgh, UK. ⁷³Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK. ⁷⁴Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France. ⁷⁵Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands. ⁷⁶National Institutes for Quantum Science and Technology, Chiba, Japan. ⁷⁷Group of Biomedical Imaging, Consorcio Centro de Investigación Biomédica en Red (CIBER) de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), University of Barcelona, Barcelona, Spain. ⁷⁸BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA. ⁷⁹Animal Imaging and Technology Section, Center for Biomedical Imaging, École polytechnique fédérale de Lausanne, Lausanne, Switzerland. ⁸⁰Focused Ultrasound Laboratory, Department of Radiology Brigham and Women's Hospital, Boston, MA, USA. ⁸¹Neurosciences Department, Novartis Institutes for BioMedical Research, Basel, Switzerland. ⁸²Inria, University Grenoble Alpes, CNRS, Grenoble, France. ⁸³NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK. ⁸⁴Gipsa-lab, University Grenoble Alpes, CNRS, Grenoble, France. ⁸⁵CERMEP - Imagerie du vivant, Lyon, France. ⁸⁶Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany. ⁸⁷Center for Behavioral Brain Sciences, Magdeburg, Germany. ⁸⁸Queensland Brain Institute and Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD, Australia. ⁸⁹Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. ⁹⁰Biology of Neurodgenerative Disorders, Department of Neuroscience Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy. ⁹¹Conway Institute of Biomedical and Biomolecular Sciences, School of Medicine, University College Dublin, Dublin, Ireland. ⁹²Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy. ⁹³Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Bochum, Germany. ⁹⁴Max-Delbrück Center
for Molecular Medicine in the Helmholtz Association, Berlin, Germany. ⁹⁵School of Arts & Sciences, University of Houston-Victoria, Victoria, TX, USA.

⁹⁶School of Psychology, Trinity College Dublin, Dublin, Ireland. ⁹⁷Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.

⁹⁸Trinity Centre for Biomedical Engineering, School of Medicine, Trinity College Dublin, Dublin, Ireland. ⁹⁹Centre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, Bangalore, India. ¹⁰⁰Department of Medical Biophysics, University of Toronto, Toronto, QC, Canada. ¹⁰¹Université Clermont Auvergne, Inserm U1107 Neuro-Dol, Pharmacologie Fondamentale et Clinique de la Douleur, Clermont-Ferrand, France.

¹⁰²Animal Care Unit, Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy. ¹⁰³Physical Sciences Platform, Sunnybrook Research Institute, Toronto, QC, Canada. ¹⁰⁴Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany. ¹⁰⁵Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands. ¹⁰⁶Medical Imaging Physics (INM-4), Institute of Neuroscience and Medicine, Forschungszentrum Juelich, Juelich, Germany. ¹⁰⁷Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Crawley, WA, Australia. ¹⁰⁸School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK. ¹⁰⁹Translational Neuroimaging Laboratory, Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. ¹¹⁰Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany. ¹¹¹Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ¹¹²Department of Physiology, McGill University, Montreal, QC, Canada. ¹¹³Laboratory of Surgical Neuroanatomy, Institute of Neuroscience, University of Barcelona, Barcelona, Spain. ¹¹⁴Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA. ¹¹⁵Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland. ¹¹⁶Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland. ¹¹⁷Hospices Civils de Lyon, Lyon, France. ¹¹⁸Department of Psychiatry, McGill University, Montreal, QC, Canada. e-mail: joanes.grandjean@radboudumc.nl
## Code availability

Jupiter 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).

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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/BO6, 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 (Ei114985); 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 Tecnología 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).

## Author 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.

## 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.

## Additional information

**Extended data** is available for this paper at https://doi.org/10.1038/s41593-023-01286-8.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41593-023-01286-8.

**Correspondence and requests for materials** should be addressed to Joanes Grandjean.

**Peer review information** *Nature Neuroscience* thanks Peter Klink, Afonso Silva and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

**Reprints and permissions information** is available at www.nature.com/reprints.
<figure><img src="image_11.png" /><figcaption>Extended Data Fig. 1 | Age and weight distributions. Age (a) and weight (b) distribution for the rats in the MultiRat rest collection.</figcaption></figure>
<figure><img src="image_12.png" /></figure>

**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.
<figure><img src="image_13.png" /></figure>

**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.
<figure><img src="image_14.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_19.png" /></figure>

**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.
a

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

b

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

**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.
<figure><img src="image_24.png" /><figcaption>IC 2: Auditory</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>IC 4: Retrosplenial</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>IC 6: Cerebellum (2)</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>IC 8: Parietal Associative</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>IC 10: Somatosensory (bf)</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>IC 12: Insula</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>IC 1: Cerebellum (1)</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>IC 3: Somatosensory (mouth)</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>IC 5: Caudate-putamen</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>IC 7: Cingulate/Prelimbic</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>IC 9: Visual</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>IC 11: Motor</figcaption></figure>

<figure><img src="image_36.png" /></figure>

Functional connectivity [a.u.]

3 mm

**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.
## Human research participants

Policy information about studies involving human research participants and Sex and Gender in Research.

Reporting on sex and gender na

Population characteristics na

Recruitment na

Ethics oversight na

Note that full information on the approval of the study protocol must also be provided in the manuscript.

## Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_37.png" /></figure>

Life sciences

<figure><img src="image_38.png" /></figure>

Behavioural & social sciences

<figure><img src="image_39.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

## Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

Sample size n = 10 per dataset. This value is based on pre-existing data availability in the participating laboratories

Data exclusions Unsuitability for preprocessing (e.g., dedicated image reconstruction needs, restricted field-of-view), image misregistration, excessive motion, and missing or corrupted data

Replication All dataset and code is released under permissive licenses to allow replication of the analysis

Randomization The study consist of an analysis after the acquisition of the data. Randomization is not applicable.

Blinding The experimenter was blind to the origin of the data during the analysis

## Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

### Materials & experimental systems

n/a Involved in the study

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

Antibodies

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

Eukaryotic cell lines

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

Palaeontology and archaeology

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

Animals and other organisms

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

Clinical data

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

Dual use research of concern

### Methods

n/a Involved in the study

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

ChIP-seq

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

Flow cytometry

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

MRI-based neuroimaging

## Animals and other research organisms

Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research, and Sex and Gender in Research

Laboratory animals Rats; Sprague Dawley, Long Evans, Wistar, Fischer 344, Lister Hooded; 0 to 20 months

Wild animals No wild animals were used
Reporting on sex Sex effect was tested

Field-collected samples No field collected sampled were used

Ethics oversight This study was approved by the authorities of:
Radboudumc, The Netherlands;
Univ. Grenoble Alpes, France;
The University of Western Australia, Australia;
University of Queensland, Australia;
Brigham and Women's Hospital, USA;
McGill University, Canada;
University Hospital Münster, Germany;
University of Florida, USA;
University of Eastern Finland, Finland;
German Primate Center - Leibniz Institute for Primate Research, Germany;
Icahn School of Medicine at Mount Sinai, USA;
Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Germany;
École polytechnique fédérale de Lausanne, Switzerland;
Universidad Nacional Autónoma de México, Mexico;
The University of Edinburgh, UK;
King's College London, UK;
Pennsylvania State University, USA;
Tohoku University, Japan;
Max Planck Institute for Biological Cybernetics, USA;
University and ETH Zurich, Switzerland;
Trinity College Dublin, Ireland;
University of Antwerp, Belgium;
University Medical Center Utrecht, Netherlands;
The University of North Carolina at Chapel Hill, USA;
University of Barcelona, Spain;
Ghent University, Belgium;
Yale University School of Medicine, USA;
University of Nottingham, UK;
Institute of Automation, Chinese Academy of Sciences, China;
University of Cambridge, UK;
Amsterdam UMC location University of Amsterdam, Netherlands;
CEA Saclay, France;
FAU Erlangen-Nürnberg, Germany;
Champalimaud Centre for the Unknown, Portugal;
University of Guelph, Canada;
Emory University/Georgia Institute of Technology, USA;
Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Italy;
Novartis Institutes for BioMedical Research, Switzerland;
Ruhr University Bochum, Germany;
Northeastern University, USA;

Note that full information on the approval of the study protocol must also be provided in the manuscript.

## Magnetic resonance imaging

### Experimental design

Design type resting-state

Design specifications One run and one session per subject.

Behavioral performance measures na

### Acquisition

Imaging type(s) functional

Field strength 3 to 17.6T

Sequence & imaging parameters Gradient and spin echo echo-planar imaging

Area of acquisition Whole-brain

Diffusion MRI ☐ Used

☒ Not used
## Preprocessing

Preprocessing software RABIES 0.3.5 default parameters

Normalization ANTs 2.3.5

Normalization template SIGMA rat template

Noise and artifact removal RABIES 0.3.5 default parameters

Volume censoring na

## Statistical modeling & inference

Model type and settings Chi-square test between categorical data

Effect(s) tested Differences in the expected distribution of the functional connectivity categories

Specify type of analysis: ☒ Whole brain ☐ ROI-based ☐ Both

Statistic type for inference
(See Eklund et al. 2016) Effect size and confidence intervals

Correction na

## Models & analysis

n/a Involved in the study

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

Functional and/or effective connectivity

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

Graph analysis

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

Multivariate modeling or predictive analysis

Functional and/or effective connectivity Pearson's correlation