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A consensus protocol for functional connectivity analysis in the rat brain

An Author Correction to this article was published on 18 April 2023

This article has been updated


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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Fig. 1: MultiRat_rest dataset description.
Fig. 2: FC specificity.
Fig. 3: Incidence of FC at the group level (n = 65 datasets of n ~ 10 subjects per dataset) for four seeds.
Fig. 4: StandardRat dataset description.

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Data availability

The raw datasets are available here: unstandardized resting-state fMRI (MultiRat_rest) (; standardized resting-state fMRI (StandardRat) ( The pre-processed volumes, time series and quality control files are available here: Image pre-processing, confound correction and connectivity analysis were performed using RABIES 0.3.5 ( (ref. 14).

Code availability

Jupyter notebooks demonstrating the analysis code are available under the terms of the Apache-2.0 license (;

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  1. Elam, J. S. et al. The Human Connectome Project: a retrospective. Neuroimage 244, 118543 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Mennes, M., Biswal, B., Castellanos, F. X. & Milham, M. P. Making data sharing work: the FCP/INDI experience. Neuroimage 82, 683 (2013).

    Article  PubMed  Google Scholar 

  3. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. Neuroimage 80, 144–168 (2013).

    Article  PubMed  Google Scholar 

  5. Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).

    Article  PubMed  Google Scholar 

  6. Homberg, J. R. et al. The continued need for animals to advance brain research. Neuron 109, 2374–2379 (2021).

    Article  CAS  PubMed  Google Scholar 

  7. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).

    Article  PubMed  Google Scholar 

  8. Esteban, O. et al. FMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Mandino, F. et al. Animal functional magnetic resonance imaging: trends and path toward standardization. Front. Neuroinform. 13, 78 (2019).

    Article  PubMed  Google Scholar 

  10. Reimann, H. M. & Niendorf, T. The (un)conscious mouse as a model for human brain functions: key principles of anesthesia and their impact on translational neuroimaging. Front. Syst. Neurosci. 14, 8 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pais-Roldán, P. et al. Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 245, 118630 (2021).

    Article  PubMed  Google Scholar 

  12. Grandjean, J. et al. Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis. Neuroimage 205, 116278 (2020).

    Article  PubMed  Google Scholar 

  13. Milham, M. P. et al. An open resource for non-human primate imaging. Neuron 100, 61–74 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Desrosiers-Gregoire, G., Devenyi, G. A., Grandjean, J. & Chakravarty, M. M. Rodent Automated Bold Improvement of EPI Sequences (RABIES): a standardized image processing and data quality platform for rodent fMRI. 2022.08.20.504597 Preprint at (2022).

  15. Paasonen, J., Stenroos, P., Salo, R. A., Kiviniemi, V. & Gröhn, O. Functional connectivity under six anesthesia protocols and the awake condition in rat brain. Neuroimage 172, 9–20 (2018).

    Article  PubMed  Google Scholar 

  16. Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).

    Article  CAS  PubMed  Google Scholar 

  17. Gozzi, A. & Schwarz, A. J. Large-scale functional connectivity networks in the rodent brain. Neuroimage 127, 496–509 (2016).

    Article  PubMed  Google Scholar 

  18. Mandino, F. et al. A triple-network organization for the mouse brain. Mol. Psychiatry 27, 865–872 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zerbi, V., Grandjean, J., Rudin, M. & Wenderoth, N. Mapping the mouse brain with rs-fMRI: an optimized pipeline for functional network identification. Neuroimage 123, 11–21 (2015).

    Article  PubMed  Google Scholar 

  21. Liu, Y. et al. An open database of resting-state fMRI in awake rats. Neuroimage 220, 117094 (2020).

    Article  PubMed  Google Scholar 

  22. Lee, S.-H. et al. An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI. Neuroimage 243, 118541 (2021).

    Article  PubMed  Google Scholar 

  23. Lee, H.-L., Li, Z., Coulson, E. J. & Chuang, K.-H. Ultrafast fMRI of the rodent brain using simultaneous multi-slice EPI. Neuroimage 195, 48–58 (2019).

    Article  PubMed  Google Scholar 

  24. Milham, M. P. et al. Assessment of the impact of shared brain imaging data on the scientific literature. Nat. Commun. 9, 2818 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Barrière, D. A. et al. The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization. Nat. Commun. 10, 5699 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A. & Bjaalie, J. G. Waxholm Space atlas of the Sprague Dawley rat brain. Neuroimage 97, 374–386 (2014).

    Article  PubMed  Google Scholar 

  27. Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).

    Article  CAS  PubMed  Google Scholar 

  29. Avants, B., Tustison, N. J. & Song, G. Advanced Normalization Tools: V1.0. Insight J. (2009).

  30. Pruim, R. H. R. et al. ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage 112, 267–277 (2015).

    Article  PubMed  Google Scholar 

  31. Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014).

  32. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Vallat, R. Pingouin: statistics in Python. J. Open Source Softw. 3, 1026 (2018).

    Article  Google Scholar 

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This research was enabled, in part, by support provided by Compute Ontario ( and Compute Canada ( 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).

Author information

Authors and Affiliations



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.

Corresponding author

Correspondence to Joanes Grandjean.

Ethics declarations

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

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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.

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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).

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