Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

Nature Neuroscience volume 27, pages 148–158 (2024)Cite this article

Subjects

Abstract

Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mesoscopic imaging of cortical activity and functional connectivity.
Fig. 2: Dynamic functional connectivity encodes rapid behavioral variations.
Fig. 3: Local circuit dynamics encode spontaneous behavioral variations.
Fig. 4: Dynamic functional connectivity reveals distinct cortical subnetworks.
Fig. 5: Functional connectivity across spatial scales encodes behavior.

Similar content being viewed by others

Data availability

The full datasets generated and analyzed in this study are available from the corresponding author on reasonable request. Data for mesoscopic imaging experiments with CCFv3-based parcellation have been deposited on the figshare archive (https://figshare.com/projects/Benisty_Higley_2023/175317).

Code availability

Custom-written MATLAB scripts used in this study are available on GitHub (https://github.com/cardin-higley-lab/Benisty_Higley_2023).

References

  1. Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20, 340–352 (2017).

    CAS  Google Scholar 

  2. Calhoun, V. D., Miller, R., Pearlson, G. & Adali, T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84, 262–274 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Cardin, J. A., Crair, M. C. & Higley, M. J. Mesoscopic imaging: shining a wide light on large-scale neural dynamics. Neuron 108, 33–43 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Boly, M. et al. Baseline brain activity fluctuations predict somatosensory perception in humans. Proc. Natl Acad. Sci. USA 104, 12187–12192 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. de Gee, J. W. et al. Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains. eLife 9, e54014 (2020).

    PubMed Central  Google Scholar 

  6. Jacobs, E. A. K., Steinmetz, N. A., Peters, A. J., Carandini, M. & Harris, K. D. Cortical state fluctuations during sensory decision making. Curr. Biol. 30, 4944–4955 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. McGinley, M. J., David, S. V. & McCormick, D. A. Cortical membrane potential signature of optimal states for sensory signal detection. Neuron 87, 179–192 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Palva, J. M. & Palva, S. Roles of multiscale brain activity fluctuations in shaping the variability and dynamics of psychophysical performance. Prog. Brain Res. 193, 335–350 (2011).

    PubMed  Google Scholar 

  9. Tang, L. & Higley, M. J. Layer 5 circuits in V1 differentially control visuomotor behavior. Neuron 105, 346–354 (2020).

    CAS  PubMed  Google Scholar 

  10. Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).

    CAS  PubMed  Google Scholar 

  11. Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019).

    PubMed  PubMed Central  Google Scholar 

  13. Vinck, M., Batista-Brito, R., Knoblich, U. & Cardin, J. A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron 86, 740–754 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Lohani, S. et al. Spatiotemporally heterogeneous coordination of cholinergic and neocortical activity. Nat. Neurosci. 25, 1706–1713 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Lurie, D. J. et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw. Neurosci. 4, 30–69 (2020).

    PubMed Central  Google Scholar 

  16. MacDowell, C. J. & Buschman, T. J. Low-dimensional spatiotemporal dynamics underlie cortex-wide neural activity. Curr. Biol. 30, 2665–2680 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Vanni, M. P., Chan, A. W., Balbi, M., Silasi, G. & Murphy, T. H. Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules. J. Neurosci. 37, 7513–7533 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Gregoriou, G. G., Gotts, S. J., Zhou, H. & Desimone, R. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324, 1207–1210 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Ito, T. et al. Task-evoked activity quenches neural correlations and variability across cortical areas. PLoS Comput. Biol. 16, e1007983 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Mohajerani, M. H. et al. Spontaneous cortical activity alternates between motifs defined by regional axonal projections. Nat. Neurosci. 16, 1426–1435 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Cohen, M. R. & Kohn, A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 14, 811–819 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Gonzalez-Castillo, J. et al. Manifold learning for fMRI time-varying FC. Front. Hum. Neurosci. https://doi.org/10.1101/2023.01.14.523992 (2023).

  23. Spruston, N. Pyramidal neurons: dendritic structure and synaptic integration. Nat. Rev. Neurosci. 9, 206–221 (2008).

    CAS  PubMed  Google Scholar 

  24. Lafon, S., Keller, Y. & Coifman, R. R. Data fusion and multicue data matching by diffusion maps. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1784–1797 (2006).

    PubMed  Google Scholar 

  25. Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, e12727 (2016).

    PubMed  PubMed Central  Google Scholar 

  26. Barson, D. et al. Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits. Nat. Methods 17, 107–113 (2020).

    CAS  PubMed  Google Scholar 

  27. Hamodi, A. S., Martinez Sabino, A., Fitzgerald, N. D., Moschou, D. & Crair, M. C. Transverse sinus injections drive robust whole-brain expression of transgenes. eLife 9, e53639 (2020).

    PubMed  PubMed Central  Google Scholar 

  28. Syeda, A. et al. Facemap: a framework for modeling neural activity based on orofacial tracking. Preprint at bioRxiv https://doi.org/10.1101/2022.11.03.515121 (2022).

  29. Mohan, H. et al. Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks. Nat. Neurosci. 26, 481–494 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  31. Mishne, G., Coifman, R. R., Lavzin, M. & Schiller, J. Automated cellular structure extraction in biological images with applications to calcium imaging data. Preprint at bioRxiv https://doi.org/10.1101/313981 (2018).

  32. Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953 (2020).

    CAS  PubMed Central  Google Scholar 

  33. Saxena, S. et al. Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput. Biol. 16, e1007791 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Wood, K. C., Angeloni, C. F., Oxman, K., Clopath, C. & Geffen, M. N. Neuronal activity in sensory cortex predicts the specificity of learning in mice. Nat. Commun. 13, 1167 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Hallinen, K. M. et al. Decoding locomotion from population neural activity in moving C. elegans. eLife 10, e66135 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Livneh, Y. et al. Estimation of current and future physiological states in insular cortex. Neuron 105, 1094–1111 (2020).

    CAS  PubMed Central  Google Scholar 

  38. Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Gonzalez-Castillo, J. et al. Imaging the spontaneous flow of thought: distinct periods of cognition contribute to dynamic functional connectivity during rest. NeuroImage 202, 116129 (2019).

    PubMed  Google Scholar 

  40. Constantinople, C. M. & Bruno, R. M. Effects and mechanisms of wakefulness on local cortical networks. Neuron 69, 1061–1068 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Polack, P. O., Friedman, J. & Golshani, P. Cellular mechanisms of brain state-dependent gain modulation in visual cortex. Nat. Neurosci. 16, 1331–1339 (2013).

    CAS  PubMed Central  Google Scholar 

  42. Tagliazucchi, E. & Laufs, H. Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82, 695–708 (2014).

    CAS  PubMed  Google Scholar 

  43. Gao, R., van den Brink, R. L., Pfeffer, T. & Voytek, B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife 9, e61277 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Raut, R. V., Snyder, A. Z. & Raichle, M. E. Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proc. Natl Acad. Sci. USA 117, 20890–20897 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Reimer, J. et al. Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun. 7, 13289 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Joshi, S., Li, Y., Kalwani, R. M. & Gold, J. I. Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron 89, 221–234 (2016).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Clancy, K. B., Orsolic, I. & Mrsic-Flogel, T. D. Locomotion-dependent remapping of distributed cortical networks. Nat. Neurosci. 22, 778–786 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Peters, A. J., Fabre, J. M. J., Steinmetz, N. A., Harris, K. D. & Carandini, M. Striatal activity topographically reflects cortical activity. Nature 591, 420–425 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Musall, S. et al. Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making. Nat. Neurosci. 26, 495–505 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Puscian, A., Benisty, H. & Higley, M. J. NMDAR-dependent emergence of behavioral representation in primary visual cortex. Cell Rep. 32, 107970 (2020).

    CAS  PubMed Central  Google Scholar 

  52. Poort, J. et al. Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron 86, 1478–1490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Makino, H. & Komiyama, T. Learning enhances the relative impact of top–down processing in the visual cortex. Nat. Neurosci. 18, 1116–1122 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Miller-Hansen, A. J. & Sherman, S. M. Conserved patterns of functional organization between cortex and thalamus in mice. Proc. Natl Acad. Sci. USA 119, e2201481119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Huang, L. et al. BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals. Cell 182, 177–188 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Jing, M. et al. An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat. Methods 17, 1139–1146 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Wekselblatt, J. B., Flister, E. D., Piscopo, D. M. & Niell, C. M. Large-scale imaging of cortical dynamics during sensory perception and behavior. J. Neurophysiol. 115, 2852–2866 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Chang, C. C. & Lin, C. J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. https://doi.org/10.1145/1961189.1961199 (2011).

  59. Gavish, M. & Donoho, D. L. The optimal hard threshold for singular values is 4/√3. IEEE Trans. Inform. Theory 60, 5040–5053 (2014).

    Google Scholar 

  60. Cheng, X. & Mishne, G. Spectral embedding norm: looking deep into the spectrum of the graph Laplacian. SIAM J. Imaging Sci. 13, 1015–1048 (2020).

    PubMed Central  Google Scholar 

  61. Diamond, S. & Boyd, S. CVXPY: a Python-embedded modeling language for convex optimization. J. Mach. Learn. Res. 17, 83 (2016).

    PubMed Central  Google Scholar 

  62. Venkatesh, M., Jaja, J. & Pessoa, L. Comparing functional connectivity matrices: a geometry-aware approach applied to participant identification. NeuroImage 207, 116398 (2020).

    PubMed  Google Scholar 

  63. Tuzel, O., Porikli, F. & Meer, P. Pedestrian detection via classification on Riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1713–1727 (2008).

    PubMed  Google Scholar 

  64. Barachant, A., Bonnet, S., Congedo, M. & Jutten, C. Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing 112, 172–178 (2013).

    Google Scholar 

  65. Yair, O., Ben-Chen, M. & Talmon, R. Parallel transport on the cone manifold of SPD matrices for domain adaptation. IEEE Trans. Signal Process. 67, 1797–1811 (2019).

    Google Scholar 

  66. Abbas, K. et al. Geodesic distance on optimally regularized functional connectomes uncovers individual fingerprints. Brain Connect. 11, 333–348 (2021).

    PubMed Central  Google Scholar 

  67. Fowlkes, C., Belongie, S., Chung, F. & Malik, J. Spectral grouping using the Nystrom method. IEEE Trans. Pattern Anal. Mach. Intell. 26, 214–225 (2004).

    Google Scholar 

Download references

Acknowledgements

We thank members of the Higley and Cardin laboratories for helpful input throughout all stages of this study. We thank R. Pant and E. Murillo for the generation of adeno-associated virus vectors. We thank the GENIE (Genetically Encoded Neuronal Indicator and Effector) Project for jRCaMP1b plasmids. This work was supported by funding from the National Institutes of Health (MH099045, MH121841 and EY033975 to M.J.H.; EY022951 to J.A.C.; MH113852 to M.J.H. and J.A.C.; EY029581 and GM007205 to D.B.; EY031133 to A.H.M.; EY026878 to the Yale Vision Core Program; EB026936 to G.M. and R.R.C.) and the National Science Foundation (CCF-2217058 to G.M.), an award from the Yale Kavli Institute of Neuroscience (to M.J.H. and R.R.C.), an award from the Swartz Foundation (to H.B.), an award from the Simons Foundation (Simons Foundation Autism Research Initiative award to M.J.H. and J.A.C.), an award from the Smith–Magenis Syndrome Research Foundation (to M.J.H. and J.A.C.) and a Brain & Behavior Research Foundation Young Investigator Grant (to S.L.).

Author information

Author notes

  1. These authors contributed equally: Hadas Benisty, Daniel Barson.

Authors and Affiliations

  1. Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA

    Hadas Benisty, Daniel Barson, Andrew H. Moberly, Sweyta Lohani, Lan Tang, Michael C. Crair, Jessica A. Cardin & Michael J. Higley

  2. Program in Applied Mathematics, Yale University, New Haven, CT, USA

    Ronald R. Coifman

  3. Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA

    Gal Mishne

Authors
  1. Hadas Benisty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Barson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew H. Moberly
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sweyta Lohani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lan Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ronald R. Coifman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael C. Crair
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Gal Mishne
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jessica A. Cardin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Michael J. Higley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.B., R.R.C., G.M., M.C.C., J.A.C. and M.J.H. designed the study. H.B., D.B., R.R.C., G.M. and M.J.H. developed the analytical approach. H.B. carried out all analyses. D.B., A.H.M., S.L. and L.T. collected experimental data. H.B. and M.J.H. wrote the manuscript.

Corresponding author

Correspondence to Michael J. Higley.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benisty, H., Barson, D., Moberly, A.H. et al. Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior. Nat Neurosci 27, 148–158 (2024). https://doi.org/10.1038/s41593-023-01498-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-023-01498-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing