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fMRI data acquisition and analysis for task-free, anesthetized rats

Nature Protocols volume 20, pages 1393–1412 (2025)Cite this article

Subjects

Abstract

Templates for the acquisition of large datasets such as the Human Connectome Project guide the neuroimaging community to reproducible data acquisition and scientific rigor. By contrast, small animal neuroimaging often relies on laboratory-specific protocols, which limit cross-study comparisons. The establishment of broadly validated protocols may facilitate the acquisition of large datasets, which are essential for uncovering potentially small effects often seen in functional MRI (fMRI) studies. Here, we outline a procedure for the acquisition of fMRI datasets in rats and describe animal handling, MRI sequence parameters, data conversion, preprocessing, quality control and data analysis. The procedure is designed to be generalizable across laboratories, has been validated by using datasets across 20 research centers with different scanners and field strengths ranging from 4.7 to 17.2 T and can be used in studies in which it is useful to compare functional connectivity measures across an extensive range of datasets. The MRI procedure requires 1 h per rat to complete and can be carried out by users with limited expertise in rat handling, MRI and data processing.

Key points

  • The procedure covers all aspects of a typical neuroimaging experiment, thus enabling the use and comparability of data acquired across sites and instruments, facilitating the availability of large neuroimaging datasets for the neuroimaging community.

  • The use of this fMRI protocol enables the comparison of functional connectivity measures across an extensive range of datasets.

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Fig. 1: Specific functional connectivity.
Fig. 2: Timeline of the anesthesia protocol.
Fig. 3: Slice package, shim voxel position and anticipated MRI results.
Fig. 4: Quality control outputs and fMRI data after registration and confound removal.
Fig. 5: Temporal data diagnosis.
Fig. 6: Spatial data diagnosis.
Fig. 7: Results of data analysis.

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Data availability

The raw datasets for standardized resting-state fMRI (StandardRat) are available at (https://doi.org/10.18112/openneuro.ds004116.v1.0.0). The pre-processed volumes, time series and quality control files are available at https://doi.org/10.34973/1gp6-gg97. All data are available under a CC0 license.

Code availability

Jupyter notebooks demonstrating the code are available under the terms of the Apache-2.0 license (https://github.com/grandjeanlab/StandardRat/tree/master/protocol_paper).

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Charles River. Transgenic Model Creation Services. Available at https://www.criver.com/products-services/research-models-services/genetically-engineered-model-services/transgenic-mouse-rat-model-creation?region=3696 Accessed 9 Nov 2022.

  3. Gutierrez-Barragan, D., Ramirez, J. S. B., Panzeri, S., Xu, T. & Gozzi, A. Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain. Nat. Commun. 15, 8518 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mandino, F. et al. A triple-network organization for the mouse brain. Mol. Psychiatry 27, 865–872 (2022).

    Article  PubMed  Google Scholar 

  5. Biswal, B., Zerrin Yetkin, F., 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 

  6. Damoiseaux, J. S. et al. Consistent resting-state networks across healthy subjects. Proc. Natl Acad. Sci. USA 103, 13848–13853 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  8. 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 

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

    Article  PubMed  Google Scholar 

  10. Hutchison, R. M. & Everling, S. Monkey in the middle: why non-human primates are needed to bridge the gap in resting-state investigations. Front. Neuroanat. 6, 29 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lu, H. et al. Rat brains also have a default mode network. Proc. Natl Acad. Sci. USA 109, 3979–3984 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  13. Sudlow, C. et al. UK Biobank: an Open Access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Van Essen, D. C. et al. The Human Connectome Project: a data acquisition perspective. Neuroimage 62, 2222–2231 (2012).

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  16. Grandjean, J. et al. A consensus protocol for functional connectivity analysis in the rat brain. Nat. Neurosci. 26, 673–681 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Desrosiers-Grégoire, G. et al. A standardized image processing and data quality platform for rodent fMRI. Nat. Commun. 15, 6708 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Grandjean, J. et al. Psilocybin exerts distinct effects on resting state networks associated with serotonin and dopamine in mice. Neuroimage 225, 117456 (2021).

    Article  CAS  PubMed  Google Scholar 

  20. Grandjean, J. et al. A brain-wide functional map of the serotonergic responses to acute stress and fluoxetine. Nat. Commun. 10, 350 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Schroeter, A., Schlegel, F., Seuwen, A., Grandjean, J. & Rudin, M. Specificity of stimulus-evoked fMRI responses in the mouse: the influence of systemic physiological changes associated with innocuous stimulation under four different anesthetics. Neuroimage 94, 372–384 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Giorgi, A. et al. Brain-wide mapping of endogenous serotonergic transmission via chemogenetic fMRI. Cell Rep. 21, 910–918 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Menon, V. et al. Optogenetic stimulation of anterior insular cortex neurons in male rats reveals causal mechanisms underlying suppression of the default mode network by the salience network. Nat. Commun. 14, 866 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chao, T.-H. H. et al. Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data. Neurophotonics 9, 032205 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lake, E. M. et al. Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI. Nat. Methods 17, 1262–1271 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Grandjean, J. AwakeRodent, the multi-center, multi-species, multi-modality study. OSF https://osf.io/te2m7 (2023).

  27. Dorr, A. E., Lerch, J. P., Spring, S., Kabani, N. & Henkelman, R. M. High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice. Neuroimage 42, 60–69 (2008).

    Article  CAS  PubMed  Google Scholar 

  28. Markicevic, M., Savvateev, I., Grimm, C. & Zerbi, V. Emerging imaging methods to study whole-brain function in rodent models. Transl. Psychiatry 11, 1–14 (2021).

    Article  Google Scholar 

  29. Rabut, C. et al. 4D functional ultrasound imaging of whole-brain activity in rodents. Nat. Methods 16, 994–997 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cramer, S. W. et al. Through the looking glass: a review of cranial window technology for optical access to the brain. J. Neurosci. Methods 354, 109100 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hillman, E. M. C. Optical brain imaging in vivo: techniques and applications from animal to man. J. Biomed. Opt. 12, 051402 (2007).

    Article  PubMed  Google Scholar 

  32. Ma, Y. et al. Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches. Phil. Trans. R. Soc. B 371, 20150360 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Steinmetz, N. A. et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Han, Z. et al. Awake and behaving mouse fMRI during Go/No-Go task. Neuroimage 188, 733–742 (2019).

    Article  PubMed  Google Scholar 

  35. Dvořáková, L. et al. Light sedation with short habituation time for large-scale functional magnetic resonance imaging studies in rats. NMR Biomed. 35, e4679 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Stenroos, P. et al. Awake rat brain functional magnetic resonance imaging using standard radio frequency coils and a 3D printed restraint kit. Front. Neurosci. 12, 548 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 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 

  38. Tsurugizawa, T. & Yoshimaru, D. Impact of anesthesia on static and dynamic functional connectivity in mice. Neuroimage 241, 118413 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. Grandjean, J., Schroeter, A., Batata, I. & Rudin, M. Optimization of anesthesia protocol for resting-state fMRI in mice based on differential effects of anesthetics on functional connectivity patterns. Neuroimage 102, 838–847 (2014).

    Article  PubMed  Google Scholar 

  40. Pawela, C. P. et al. A protocol for use of medetomidine anesthesia in rats for extended studies using task-induced BOLD contrast and resting-state functional connectivity. Neuroimage 46, 1137–1147 (2009).

    Article  PubMed  Google Scholar 

  41. Pan, W.-J., Billings, J. C., Grooms, J. K., Shakil, S. & Keilholz, S. D. Considerations for resting state functional MRI and functional connectivity studies in rodents. Front. Neurosci. 9, 269 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Jonckers, E. et al. Different anesthesia regimes modulate the functional connectivity outcome in mice. Magn. Reson. Med. 72, 1103–1112 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. 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 

  44. Chang, P.-C. et al. Novel method for functional brain imaging in awake minimally restrained rats. J. Neurophysiol. 116, 61–80 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ferrari, L. et al. A robust experimental protocol for pharmacological fMRI in rats and mice. J. Neurosci. Methods 204, 9–18 (2012).

    Article  PubMed  Google Scholar 

  46. Fukuda, M., Vazquez, A. L., Zong, X. & Kim, S.-G. Effects of the α2-adrenergic receptor agonist dexmedetomidine on neural, vascular and BOLD fMRI responses in the somatosensory cortex. Eur. J. Neurosci. 37, 80–95 (2013).

    Article  PubMed  Google Scholar 

  47. 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 

  48. Zwiers, M. P., Moia, S. & Oostenveld, R. BIDScoin: a user-friendly application to convert source data to brain imaging data structure. Front. Neuroinformatics 15, 770608 (2022).

    Article  Google Scholar 

  49. Lee, S. et al. BrkRaw/brkraw: BrkRaw v0.3.5. Zenodo https://zenodo.org/records/6803744 (Zenodo, 2022).

  50. Ioanas, H.-I., Marks, M., Zerbi, V., Yanik, M. F. & Rudin, M. An optimized registration workflow and standard geometric space for small animal brain imaging. Neuroimage 241, 118386 (2021).

    Article  PubMed  Google Scholar 

  51. Carp, J. The secret lives of experiments: methods reporting in the fMRI literature. Neuroimage 63, 289–300 (2012).

    Article  PubMed  Google Scholar 

  52. Li, X. et al. Moving beyond processing and analysis-related variation in neuroscience. Nat. Hum. Behav. 8, 2003–2017 (2024).

    Article  PubMed  Google Scholar 

  53. Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5, 13 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Merkel, D. Docker: lightweight Linux containers for consistent development and deployment. Linux Journal https://www.linuxjournal.com/content/docker-lightweight-linux-containers-consistent-development-and-deployment (Linux Journal, 2014).

  55. Kurtzer, G. M., Sochat, V. & Bauer, M. W. Singularity: scientific containers for mobility of compute. PLoS One 12, e0177459 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Nickerson, L. D., Smith, S. M., Öngür, D. & Beckmann, C. F. Using dual rto investigate network shape and amplitude in functional connectivity analyses. Front. Neurosci. 11, 115 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  58. 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 

  59. Lehto, L. J. et al. MB-SWIFT functional MRI during deep brain stimulation in rats. Neuroimage 159, 443–448 (2017).

    Article  PubMed  Google Scholar 

  60. Ljungberg, E. et al. Silent zero TE MR neuroimaging: current state-of-the-art and future directions. Prog. Nucl. Magn. Reson. Spectrosc. 123, 73–93 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Miller, K. L. FMRI using balanced steady-state free precession (SSFP). Neuroimage 62, 713–719 (2012).

    Article  PubMed  Google Scholar 

  62. 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 

  63. McCarthy, P. FSLeyes (1.8.1). Zenodo https://zenodo.org/records/8253783 (Zenodo, 2023).

  64. Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).

    Article  PubMed  Google Scholar 

  65. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

    Article  Google Scholar 

  66. Neyman, J. & Pearson, E. S. On the use and interpretation of certain test criteria for purposes of statistical inference: part I. Biometrika 20A, 175–240 (1928).

    Google Scholar 

  67. Ho, J., Tumkaya, T., Aryal, S., Choi, H. & Claridge-Chang, A. Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16, 565–566 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Taylor, P. A. et al. Highlight results, don’t hide them: enhance interpretation, reduce biases and improve reproducibility. Neuroimage 274, 120138 (2023).

    Article  PubMed  Google Scholar 

  69. Sirmpilatze, N., Baudewig, J. & Boretius, S. Temporal stability of fMRI in medetomidine-anesthetized rats. Sci. Rep. 9, 16673 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the Dutch Research Council grant OCENW.KLEIN.334 and OSF23.1.037 awarded to J.G.

Author information

Authors and Affiliations

  1. Donders Institute for Brain, Behaviour, and Cognition, Nijmegen, The Netherlands

    Roël M. Vrooman, Wessel A. van Engelenburg, Marie E. Galteau, Judith R. Homberg, Marcel Zwiers & Joanes Grandjean

  2. Bio-imaging lab, University of Antwerp, Antwerp, Belgium

    Monica van den Berg & Marleen Verhoye

  3. µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium

    Monica van den Berg & Marleen Verhoye

  4. Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada

    Gabriel Desrosiers-Gregoire, M. Mallar Chakravarty & Gabriel A. Devenyi

  5. Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada

    Gabriel Desrosiers-Gregoire

  6. Center for Animal MRI, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Sung-Ho Lee & Yen-Yu Ian Shih

  7. Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Sung-Ho Lee & Yen-Yu Ian Shih

  8. Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Sung-Ho Lee & Yen-Yu Ian Shih

  9. Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands

    Andor Veltien, Tom Scheenen & Joanes Grandjean

  10. UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction, Physiologie de la reproduction et des comportements, Centre de recherche INRA de Nouzilly, Tours, France

    David A. Barrière

  11. Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging, King’s College London, London, UK

    Diana Cash

  12. Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada

    M. Mallar Chakravarty

  13. Department of Psychiatry, McGill University, Montreal, Quebec, Canada

    M. Mallar Chakravarty & Gabriel A. Devenyi

  14. Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy

    Alessandro Gozzi

  15. Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

    Olli Gröhn

  16. Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany

    Andreas Hess

  17. Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland

    Ileana O. Jelescu

  18. Institute for Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece

    Georgios A. Keliris

  19. Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Yen-Yu Ian Shih

  20. Bruker BioSpin MRI GmbH, Ettlingen, Germany

    Claire Wary

Authors
  1. Roël M. Vrooman
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  2. Monica van den Berg
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  3. Gabriel Desrosiers-Gregoire
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  4. Wessel A. van Engelenburg
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  5. Marie E. Galteau
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  6. Sung-Ho Lee
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  7. Andor Veltien
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  8. David A. Barrière
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  9. Diana Cash
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  10. M. Mallar Chakravarty
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  11. Gabriel A. Devenyi
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  12. Alessandro Gozzi
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  13. Olli Gröhn
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  14. Andreas Hess
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  15. Judith R. Homberg
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  16. Ileana O. Jelescu
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  17. Georgios A. Keliris
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  18. Tom Scheenen
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  19. Yen-Yu Ian Shih
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  20. Marleen Verhoye
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  21. Claire Wary
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  22. Marcel Zwiers
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  23. Joanes Grandjean
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Contributions

R.M.V., W.A.v.E. and G.D.-G. contributed to the preprocessing and quality control. R.M.V. performed the data analysis. R.M.V. and J.G. wrote the first draft of the manuscript. All authors edited the manuscript.

Corresponding author

Correspondence to Joanes Grandjean.

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Competing interests

C.W. is an employee of Bruker, a manufacturer of preclinical MRI systems.

Peer review

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Nature Protocols thanks Xiaoqing Zhou and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references

Grandjean, J. et al. Neuroimage 102, 838–847 (2014): https://doi.org/10.1016/j.neuroimage.2014.01.046

Desrosiers-Grégoire, G. et al. Nat. Commun. 15, 6708 (2024): https://doi.org/10.1038/s41467-024-50826-8

Grandjean, J. et al. Nat. Neurosci. 26, 673–681 (2023): https://doi.org/10.1038/s41593-023-01286-8

Supplementary information

Supplementary Table 1

Table for recording of relevant information during scanning

Reporting Summary

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Vrooman, R.M., van den Berg, M., Desrosiers-Gregoire, G. et al. fMRI data acquisition and analysis for task-free, anesthetized rats. Nat Protoc 20, 1393–1412 (2025). https://doi.org/10.1038/s41596-024-01110-y

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