Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Brain functions emerge as thermal equilibrium states of the connectome

Elkaïoum M. Moutuou* and Habib Benali†

  • Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada

  • *Contact author: elkaioum.moutuou@concordia.ca
  • †Contact author: habib.benali@concordia.ca

Phys. Rev. Research 7, 033156 – Published 14 August, 2025

DOI: https://doi.org/10.1103/jmqh-bqnc

Abstract

A fundamental idea in neuroscience is that cognitive functions—such as perception, learning, memory, and locomotion—are shaped and constrained by the brain's structural organization. Despite significant progress in mapping and analyzing structural connectomes, the principles linking the brain's physical architecture to its functional capabilities remain elusive. Here, we introduce an algebraic quantum model to bridge this theoretical gap, offering insights into the relationship between the connectome and emergent brain functions while connecting structural data to functional predictions. Using the well-mapped C. elegans anatomical and extrasynaptic connectomes, we demonstrate that brain functions, defined as functional networks of a neural system, emerge as thermal equilibrium states of an algebraic quantum system derived from the graph algebra of the underlying directed multigraph. Specifically, these equilibrium states, characterized by the Kubo-Martin-Schwinger formalism, reveal how individual neurons contribute to functional network formation. Our model illuminates the structure-function relationship in neural circuits through two key features: (1) a functional connectome that delineates topologically driven neuronal interactions and (2) an integration capacity index that quantifies how effectively neurons coordinate and modulate diverse information flows. Together these features provide a statistical and mechanistic account of information flow and reveal how the network topology of the connectome predicts cognition and complex behaviors.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (69)

  1. L. H. Hartwell, J. J. Hopfield, S. Leibler, and A. W. Murray, From molecular to modular cell biology, Nature (London) 402, C47 (1999).
  2. C. I. Bargmann and E. Marder, From the connectome to brain function, Nat. Methods 10, 483 (2013).
  3. S. W. Emmons, The mood of a worm, Science 338, 475 (2012).
  4. O. Sporns, G. Tononi, and R. Kötter, The human connectome: A structural description of the human brain, PLoS Comp. Biol. 1, e42 (2005).
  5. O. Sporns, The human connectome: A complex network, Ann. N.Y. Acad. Sci. 1224, 109 (2011).
  6. J. DeFelipe, From the connectome to the synaptome: An epic love story, Science 330, 1198 (2010).
  7. E. Bullmore and O. Sporns, The economy of brain network organization, Nat. Rev. Neurosci. 13, 336 (2012).
  8. F. Pulvermüller, M. Garagnani, and T. Wennekers, Thinking in circuits: Toward neurobiological explanation in cognitive neuroscience, Biol. Cybern. 108, 573 (2014).
  9. J. Doyon and H. Benali, Reorganization and plasticity in the adult brain during learning of motor skills, Curr. Opin. Neurobiol. 15, 161 (2005).
  10. M. Yu, O. Sporns, and A. J. Saykin, The human connectome in Alzheimer disease—relationship to biomarkers and genetics, Nat. Rev. Neurol. 17, 545 (2021).
  11. P. Bellec, V. Perlbarg, S. Jbabdi, M. Pélégrini-Issac, J.-L. Anton, J. Doyon, and H. Benali, Identification of large-scale networks in the brain using fMRI, NeuroImage 29, 1231 (2006).
  12. R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman, M. S. Albert, and R. J. Killiany, An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, NeuroImage 31, 968 (2006).
  13. J. G. White, E. Southgate, J. N. Thomson, and S. Brenner, The structure of the nervous system of the nematode Caenorhabditis elegans, Phil. Trans. R. Soc. Lond. B 314, 1 (1986).
  14. S. Cook, T. Jarrell, C. Brittin, Y. Wang, A. Bloniarz, M. Yakovlev, K. Nguyen, L. Tang, E. Bayer, J. Duerr, H. Bülow, O. Hobert, D. Hall, and S. Emmons, Whole-animal connectomes of both Caenorhabditis elegans sexes, Nature (London) 571, 63 (2019).
  15. D. Witvliet, B. Mulcahy, J. K. Mitchell, Y. Meirovitch, D. R. Berger, Y. Wu, Y. Liu, W. X. Koh, R. Parvathala, D. Holmyard, R. L. Schalek, N. Shavit, A. D. Chisholm, J. W. Lichtman, A. D. T. Samuel, and M. Zhen, Connectomes across development reveal principles of brain maturation, Nature (London) 596, 257 (2021).
  16. M. Winding, B. D. Pedigo, C. L. Barnes, H. G. Patsolic, Y. Park, T. Kazimiers, A. Fushiki, I. V. Andrade, A. Khandelwal, J. Valdes-Aleman, F. Li, N. Randel, E. Barsotti, A. Correia, R. D. Fetter, V. Hartenstein, C. E. Priebe, J. T. Vogelstein, A. Cardona, and M. Zlatic, The connectome of an insect brain, Science 379, eadd9330 (2023).
  17. C. Verasztó, S. Jasek, M. Gühmann, L. A. Bezares-Calderón, E. A. Williams, R. Shahidi, and G. Jékely, Whole-body connectome of a segmented annelid larva, eLife 13, RP97964 (2024).
  18. K. Friston, C. Frith, P. Liddle, and R. Frackowiak, Functional connectivity: The principal-component analysis of large (PET) data sets, J. Cereb. Blood Flow Metab. 13, 5 (1993).
  19. A. Messé, D. Rudrauf, H. Benali, and G. Marrelec, Relating structure and function in the human brain: Relative contributions of anatomy, stationary dynamics, and non-stationarities, PLoS Comput. Biol. 10, e1003530 (2014).
  20. R. G. Bettinardi, G. Deco, V. M. Karlaftis, T. J. Van Hartevelt, H. M. Fernandes, Z. Kourtzi, M. L. Kringelbach, and G. Zamora-López, How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure, Chaos 27, 047409 (2017).
  21. H.-J. Park and K. Friston, Structural and functional brain networks: From connections to cognition, Science 342, 1238411 (2013).
  22. O. Sporns, Graph theory methods: Applications in brain networks, Dialogues Clin. Neurosci., 20, 111 (2018).
  23. L. F. Abbott, D. D. Bock, E. M. Callaway, W. Denk, C. Dulac, A. L. Fairhall, I. Fiete, K. M. Harris, M. Helmstaedter, V. Jain, N. Kasthuri, Y. LeCun, J. W. Lichtman, P. B. Littlewood, L. Luo, J. H. Maunsell, R. C. Reid, B. R. Rosen, G. M. Rubin, T. J. Sejnowski et al. The mind of a mouse, Cell 182, 1372 (2020).
  24. A. Shapson-Coe, M. Januszewski, D. R. Berger, A. Pope, Y. Wu, T. Blakely, R. L. Schalek, P. H. Li, S. Wang, J. Maitin-Shepard, N. Karlupia, S. Dorkenwald, E. Sjostedt, L. Leavitt, D. Lee, J. Troidl, F. Collman, L. Bailey, A. Fitzmaurice, R. Kar et al., A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution, Science 384, eadk4858 (2024).
  25. F. Morone and H. A. Makse, Symmetry group factorization reveals the structure-function relation in the neural connectome of Caenorhabditis elegans, Nat. Commun. 10, 4961 (2019).
  26. L. R. Varshney, B. L. Chen, E. Paniagua, D. H. Hall, and D. B. Chklovskii, Structural properties of the Caenorhabditis elegans neuronal network, PLoS Comput. Biol. 7, e1001066 (2011).
  27. G. Yan, P. E. Vértes, E. K. Towlson, Y. L. Chew, D. S. Walker, W. R. Schafer, and A.-L. Barabási, Network control principles predict neuron function in the Caenorhabditis elegans connectome, Nature (London) 550, 519 (2017).
  28. E. M. Moutuou and H. Benali, KMS states of information flow in directed brain synaptic networks, arXiv:2410.18222.
  29. J. J. R. Cuntz and W. Krieger, A class of C*-algebras and topological Markov chains, Invent. Math. 56, 251 (1980).
  30. I. Raeburn, Graph Algebras, Regional Conference Series in Mathematics (Conference Board of the Mathematical Sciences, Providence, RI, 2005).
  31. R. Haag, N. M. Hugenholtz, and M. Winnink, On the equilibrium states in quantum statistical mechanics, Commun. Math. Phys. 5, 215 (1967).
  32. N. M. Hugenholtz, The How, Why and Wherefore of C*-Algebras in Statistical Mechanics (Springer, Berlin, 1972), pp. 1–647.
  33. I. Mori, H. Sasakura, and A. Kuhara, Worm thermotaxis: A model system for analyzing thermosensation and neural plasticity, Curr. Opin Neurobiol. 17, 712 (2007), Motor systems/Neurobiology of behaviour.
  34. A. Kuhara, N. Ohnishi, T. Shimowada, and I. Mori, Neural coding in a single sensory neuron controlling opposite seeking behaviours in Caenorhabditis elegans, Nat. Commun. 2, 355 (2011).
  35. J. M. Kaplan and H. R. Horvitz, A dual mechanosensory and chemosensory neuron in Caenorhabditis elegans, Proc. Nat. Acad. Sci. USA 90, 2227 (1993).
  36. M. Chalfie, J. E. Sulston, J. G. White, E. Southgate, J. N. Thomson, and S. Brenner, The neural circuit for touch sensitivity in Caenorhabditis elegans, J. Neurosci. 5, 956 (1985).
  37. M. de Bono and A. Villu Maricq, Neuronal substrates of complex behaviors in C. elegans, Annu. Rev. Neurosci. 28, 451 (2005).
  38. Z. Altun, L. Herndon, C. Wolkow, C. Crocker, R. Lints, and D. Hall, WormAtlas, https://www.wormatlas.org (accessed March 20, 2024).
  39. https://github.com/elkMm/KMSnet/tree/main/examples/data/connectivity.
  40. A. E. Pereda, Electrical synapses and their functional interactions with chemical synapses, Nat. Rev. Neurosci. 15, 250 (2014).
  41. J. M. Bekkers, Synaptic transmission: Functional autapses in the cortex, Curr. Biol. 13, R433 (2003).
  42. G. Tamás, E. H. Buhl, and P. Somogyi, Massive autaptic self-innervation of GABAergic neurons in cat visual cortex, J. Neurosci. 17, 6352 (1997).
  43. L. Katz, A new status index derived from sociometric analysis, Psychometrika 18, 39 (1953).
  44. E. Estrada and N. Hatano, Communicability in complex networks, Phys. Rev. E 77, 036111 (2008).
  45. A. Ghavasieh and M. De Domenico, Diversity of information pathways drives sparsity in real-world networks, Nat. Phys. 20, 512 (2024).
  46. A. Uhlmann, The “transition probability” in the state space of a *-algebra, Rep. Math. Phys. 9, 273 (1976).
  47. R. Jozsa, Fidelity for mixed quantum states, J. Mod. Opt. 41, 2315 (1994).
  48. A. Barrios, R. Ghosh, C. Fang, S. W. Emmons, and M. M. Barr, PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans, Nat. Neurosci. 15, 1675 (2012).
  49. M. A. Lim, J. Chitturi, V. Laskova, J. Meng, D. Findeis, A. Wiekenberg, B. Mulcahy, L. Luo, Y. Li, Y. Lu, W. Hung, Y. Qu, C.-Y. Ho, D. Holmyard, N. Ji, R. McWhirter, A. D. Samuel, D. M. Miller, R. Schnabel, J. A. Calarco, and M. Zhen, Neuroendocrine modulation sustains the C. elegans forward motor state, eLife 5, e19887 (2016).
  50. B. Bentley, R. Branicky, C. L. Barnes, Y. L. Chew, E. Yemini, E. T. Bullmore, P. E. Vértes, and W. R. Schafer, The multilayer connectome of Caenorhabditis elegans, PLoS Comput. Biol. 12, e1005283 (2016).
  51. L. Ripoll-Sánchez, J. Watteyne, H. Sun, R. Fernandez, S. R. Taylor, A. Weinreb, B. L. Bentley, M. Hammarlund, D. M. Miller, O. Hobert, I. Beets, P. E. Vértes, and W. R. Schafer, The neuropeptidergic connectome of C. elegans, Neuron 111, 3570 (2023).
  52. F. Randi, A. K. Sharma, S. Dvali, and A. M. Leifer, Neural signal propagation atlas of Caenorhabditis elegans, Nature (London) 623, 406 (2023).
  53. M. Skuhersky, T. Wu, E. Yemini, A. Nejatbakhsh, E. Boyden, and M. Tegmark, Toward a more accurate 3D atlas of C. elegans neurons, BMC Bioinf. 23, 195 (2022).
  54. M. B. Goodman, Mechanosensation, in WormBook: The Online Review of C. elegans Biology, edited by E. M. Jorgensen and J. M. Kaplan (WormBook Research Community, Pasadena, CA, 2005).
  55. C. I. Bargmann, Chemosensation in C. elegans, in WormBook: The Online Review of C. elegans Biology, edited by E. M. Jorgensen (WormBook Research Community, Pasadena, CA, 2006).
  56. A. Kuhara, M. Okumura, T. Kimata, Y. Tanizawa, R. Takano, K. D. Kimura, H. Inada, K. Matsumoto, and I. Mori, Temperature sensing by an olfactory neuron in a circuit controlling behavior of C. elegans, Science 320, 803 (2008).
  57. X. Ma and Y. Shen, Structural basis for degeneracy among thermosensory neurons in caenorhabditis elegans, J. Neurosci. 32, 1 (2012).
  58. D. D. Ghosh, M. N. Nitabach, Y. Zhang, and G. Harris, Multisensory integration in C. elegans, Curr. Opin Neurobiol. 43, 110 (2017).
  59. M. A. Hilliard, C. I. Bargmann, and P. Bazzicalupo, C. elegans responds to chemical repellents by integrating sensory inputs from the head and the tail, Curr. Biol. 12, 730 (2002).
  60. T. A. Jarrell, Y. Wang, A. E. Bloniarz, C. A. Brittin, M. Xu, J. N. Thomson, D. G. Albertson, D. H. Hall, and S. W. Emmons, The connectome of a decision-making neural network, Science 337, 437 (2012).
  61. B. Piggott, J. Liu, Z. Feng, S. Wescott, and X. Xu, The neural circuits and synaptic mechanisms underlying motor initiation in C. elegans, Cell 147, 922 (2011).
  62. S. Wicks and C. Rankin, Integration of mechanosensory stimuli in Caenorhabditis elegans, J. Neuroscience 15, 2434 (1995).
  63. A. Kano, H. J. Matsuyama, S. Nakano, and I. Mori, AWC thermosensory neuron interferes with information processing in a compact circuit regulating temperature-evoked posture dynamics in the nematode Caenorhabditis elegans, Neuroscience Res. 188, 10 (2023).
  64. M. P. van den Heuvel, S. C. de Lange, A. Zalesky, C. Seguin, B. T. Yeo, and R. Schmidt, Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations, NeuroImage 152, 437 (2017).
  65. M. Porta-de-la Riva, A. C. Gonzalez, N. Sanfeliu-Cerdán, S. Karimi, N. Malaiwong, A. Pidde, L.-F. Morales-Curiel, P. Fernandez, S. González-Bolívar, C. Hurth, and M. Krieg, Neural engineering with photons as synaptic transmitters, Nat. Methods 20, 761 (2023).
  66. I. Rabinowitch, D. A. Colón-Ramos, and M. Krieg, Understanding neural circuit function through synaptic engineering, Nat. Rev. Neurosci. 25, 131 (2024).
  67. D. N. Scott and M. J. Frank, Adaptive control of synaptic plasticity integrates micro- and macroscopic network function, Neuropsychopharmacology 48, 121 (2023).
  68. M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, and M. A. Porter, Multilayer networks, J. Complex Networks 2, 203 (2014).
  69. E. M. Moutuou, O. B. K. Ali, and H. Benali, Topology and spectral interconnectivities of higher-order multilayer networks, Front. Complex Syst. 1, 1281714 (2023).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation