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Learning dynamic representations of the functional connectome in neurobiological networks (2402.14102v2)

Published 21 Feb 2024 in q-bio.NC, cs.LG, and cs.SI

Abstract: The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior. Code is available at https://github.com/dyballa/dynamic-connectomes.

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References (59)
  1. Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2):221–248, 2015.
  2. Adam A Atanas et al. Brain-wide representations of behavior spanning multiple timescales and states in C. elegans. Cell, 186(19):4134–4151.e31, 2023. ISSN 0092-8674.
  3. From the connectome to brain function. Nature Methods, 10(6):483–490, 2013.
  4. Danielle S Bassett et al. Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci USA, 108(18):7641–7646, 2011.
  5. Isabel Beets et al. System-wide mapping of peptide-GPCR interactions in C. elegans. Cell Reports, 42(9), 2023.
  6. Alexander Belyi. GNNS. https://github.com/Alexander-Belyi/GNNS, 2022.
  7. Barry Bentley et al. The multilayer connectome of Caenorhabditis elegans. PLoS Computational Biology, 12(12):e1005283, 2016.
  8. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008, 2008.
  9. A fast non-negativity-constrained least squares algorithm. J Chemom, 11(5):393–401, 1997.
  10. Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition. Psychometrika, 35(3):283–319, 1970.
  11. Zachary T Cecere et al. State-dependent network interactions differentially gate sensory input at the motor and command neuron level in Caenorhabditis elegans. bioRxiv, pp.  2021–04, 2021.
  12. Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 92(3):708–721, 2009.
  13. Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. John Wiley & Sons, 2009.
  14. Steven J Cook et al. Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature, 571(7763):63–71, 2019.
  15. Ugur Dag et al. Dissecting the functional organization of the C. elegans serotonergic system at whole-brain scale. Cell, 186(12):2574–2592.e20, 2023. ISSN 0092-8674. doi: https://doi.org/10.1016/j.cell.2023.04.023.
  16. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09):P09008, 2005.
  17. Luciano Dyballa et al. Population encoding of stimulus features along the visual hierarchy. Proc Natl Acad Sci USA, 121(4):e2317773121, 2024.
  18. The C. elegans Research Community. Behavior. In Anne C Hart (ed.), WormBook. WormBook. doi: 10.1895/wormbook.1.87.1. URL http://www.wormbook.org.
  19. Santo Fortunato. Community detection in graphs. Physics Reports, 486(3-5):75–174, 2010.
  20. Community detection in networks: a user guide. Physics Reports, 659:1–44, 2016.
  21. Efficient discovery of overlapping communities in massive networks. Proc Natl Acad Sci USA, 110(36):14534–14539, 2013.
  22. Feedback from network states generates variability in a probabilistic olfactory circuit. Cell, 161(2):215–227, 2015.
  23. Evolutionary community structure discovery in dynamic weighted networks. Physica A: Statistical Mechanics and its Applications, 413:565–576, 2014.
  24. Richard A Harshman et al. Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis. UCLA Working Papers in Phonetics, 16:1–84, 1970.
  25. Stochastic blockmodels: First steps. Social Networks, 5(2):109–137, 1983.
  26. Travis A Jarrell et al. The connectome of a decision-making neural network. Science, 337(6093):437–444, 2012.
  27. Saul Kato et al. Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell, 163(3):656–669, 2015.
  28. Tamara G Kolda. Multilinear operators for higher-order decompositions. Technical Report SAND2006-2081, Sandia National Laboratories, April 2006. URL http://www.osti.gov/scitech/biblio/923081.
  29. Tensor decompositions and applications. SIAM Rev, 51(3):455–500, 2009.
  30. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys Rev E, 80(1):016118, 2009.
  31. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788–791, 1999.
  32. A weighted network community detection algorithm based on deep learning. Applied Mathematics and Computation, 401:126012, 2021.
  33. Tiago P Peixoto. The graph-tool Python library. figshare, 2014. doi: 10.6084/m9.figshare.1164194. URL http://figshare.com/articles/graph˙tool/1164194.
  34. Tiago P Peixoto. Model selection and hypothesis testing for large-scale network models with overlapping groups. Phys Rev X, 5(1):011033, 2015.
  35. Tiago P Peixoto. Nonparametric weighted stochastic block models. Phys Rev E, 97(1):012306, 2018.
  36. Tiago P Peixoto. Bayesian stochastic blockmodeling. In Advances in Network Clustering and Blockmodeling, chapter 11, pp.  289–332. John Wiley & Sons, 2019.
  37. Multi-way nonnegative tensor factorization using fast hierarchical alternating least squares algorithm (hals). In Proceedings of The 2008 International Symposium on Nonlinear Theory and its Applications, 2008.
  38. Inducible and titratable silencing of Caenorhabditis elegans neurons in vivo with histamine-gated chloride channels. Proc Natl Acad Sci USA, 111(7):2770–2775, 2014.
  39. Neural signal propagation atlas of C. elegans, 2023.
  40. Lidia Ripoll-Sánchez et al. The neuropeptidergic connectome of C. elegans. bioRxiv, pp.  2022–10, 2022.
  41. Community discovery in dynamic networks: a survey. ACM Computing Surveys (CSUR), 51(2):1–37, 2018.
  42. Cdlib: a python library to extract, compare and evaluate communities from complex networks. Applied Network Science, 4(1):1–26, 2019.
  43. Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20), pp.  3125–3132. ACM, 2020.
  44. Foundations and modeling of dynamic networks using dynamic graph neural networks: a survey. IEEE Access, 9:79143–79168, 2021.
  45. Graph neural network inspired algorithm for unsupervised network community detection. Applied Network Science, 7(1):1–19, 2022.
  46. General optimization technique for high-quality community detection in complex networks. Phys Rev E, 90(1):012811, 2014.
  47. A non-negative symmetric encoder-decoder approach for community detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp.  597–606, 2017.
  48. Vladislav Susoy et al. Natural sensory context drives diverse brain-wide activity during C. elegans mating. Cell, 184(20):5122–5137, 2021.
  49. Detecting communities using asymptotical surprise. Phys Rev E, 92:022816, 2015.
  50. Reprogramming chemotaxis responses: sensory neurons define olfactory preferences in C. elegans. Cell, 91(2):161–169, 1997.
  51. A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Current Biology, 32(16):3443–3459, 2022.
  52. Structural properties of the Caenorhabditis elegans neuronal network. PLoS Computational Biology, 7(2):e1001066, 2011.
  53. Han Wang et al. cGAL, a temperature-robust GAL4-UAS system for Caenorhabditis elegans. Nature Methods, 14(2):145–148, 2017.
  54. John G White et al. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos Trans R Soc Lond B Biol Sci, 314(1165):1–340, 1986.
  55. Alex H Williams. tensortools. https://github.com/neurostatslab/tensortools, 2024.
  56. Alex H Williams et al. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron, 98(6):1099–1115, 2018.
  57. Daniel Witvliet et al. Connectomes across development reveal principles of brain maturation. Nature, 596(7871):257–261, 2021.
  58. A comparative analysis of community detection algorithms on artificial networks. Scientific Reports, 6(1):30750, 2016.
  59. Eviatar Yemini et al. Neuropal: a multicolor atlas for whole-brain neuronal identification in C. elegans. Cell, 184(1):272–288, 2021.

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