IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v200y2025ip1s0960077925009427.html

Hyperbolic distance reveals functional communication pathways in the Drosophila circadian clock network

Author

Listed:
  • Song, Yuxuan
  • Gu, Changgui
  • Zheng, Wenxin
  • Sun, Tianbao
  • Zhao, Huaxiang
  • Zheng, Muhua

Abstract

Circadian clocks are widespread across organisms, regulating behavioral and physiological processes. Drosophila, with its relatively small number of neurons and fully mapped connectome, provides an ideal model for investigating functional interactions among circadian clock neurons. To explore the communication pathways within the circadian clock network, we constructed a network of 242 neurons based on synaptic connectivity and embedded it in hyperbolic space, enabling navigation based on hyperbolic distances. The results demonstrated that hyperbolic distance effectively captures functional interactions within the Drosophila circadian clock network. Notably, neurons in LNITP, DN3, and DN1p play central roles in mediating global information flow, while pairs of subgroups such as DN3–DN3, DN3–DN1p, and DN3–LNITP exhibit strong functional coupling despite being spatially separated. Moreover, efficient navigation can be achieved within the network embedded in hyperbolic space. We further identified key pathways and subgroups—particularly LNITP and DN3—as crucial for contralateral communication, and revealed that certain right hemisphere neurons are more critical for contralateral communication than left counterparts. These results are verified through simulations of collective dynamics among neurons using the Kuramoto model. In conclusion, hyperbolic geometry not only provides a more accurate representation of functional interactions in the Drosophila circadian clock network but also offers new geometric insights into neuronal routing mechanisms and synchronization properties.

Suggested Citation

  • Song, Yuxuan & Gu, Changgui & Zheng, Wenxin & Sun, Tianbao & Zhao, Huaxiang & Zheng, Muhua, 2025. "Hyperbolic distance reveals functional communication pathways in the Drosophila circadian clock network," Chaos, Solitons & Fractals, Elsevier, vol. 200(P1).
  • Handle: RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925009427
    DOI: 10.1016/j.chaos.2025.116929
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077925009427
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2025.116929?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Nils Reinhard & Ayumi Fukuda & Giulia Manoli & Emilia Derksen & Aika Saito & Gabriel Möller & Manabu Sekiguchi & Dirk Rieger & Charlotte Helfrich-Förster & Taishi Yoshii & Meet Zandawala, 2024. "Synaptic connectome of the Drosophila circadian clock," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Antoine Allard & M Ángeles Serrano, 2020. "Navigable maps of structural brain networks across species," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-20, February.
    3. Aste, T. & Di Matteo, T. & Hyde, S.T., 2005. "Complex networks on hyperbolic surfaces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 20-26.
    4. Marián Boguñá & Fragkiskos Papadopoulos & Dmitri Krioukov, 2010. "Sustaining the Internet with hyperbolic mapping," Nature Communications, Nature, vol. 1(1), pages 1-8, December.
    5. Marcus Kaiser & Claus C Hilgetag, 2006. "Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems," PLOS Computational Biology, Public Library of Science, vol. 2(7), pages 1-11, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Robert Jankowski & Antoine Allard & Marián Boguñá & M. Ángeles Serrano, 2023. "The D-Mercator method for the multidimensional hyperbolic embedding of real networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Sambale, Holger & Thäle, Christoph & Trauthwein, Tara, 2025. "Central limit theorems for the nearest neighbour embracing graph in Euclidean and hyperbolic space," Stochastic Processes and their Applications, Elsevier, vol. 188(C).
    3. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    4. Ashish Raj & Yu-hsien Chen, 2011. "The Wiring Economy Principle: Connectivity Determines Anatomy in the Human Brain," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    5. Antonio Briola & Silvia Bartolucci & Tomaso Aste, 2024. "HLOB -- Information Persistence and Structure in Limit Order Books," Papers 2405.18938, arXiv.org, revised Jun 2024.
    6. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    7. Alessandra Griffa & Mathieu Mach & Julien Dedelley & Daniel Gutierrez-Barragan & Alessandro Gozzi & Gilles Allali & Joanes Grandjean & Dimitri Ville & Enrico Amico, 2023. "Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    8. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    9. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    10. Grassetti, Francesca, 2025. "Optimizing index tracking: A Random Matrix Theory approach to portfolio selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    11. repec:plo:pone00:0023460 is not listed on IDEAS
    12. Xue Wen & Delong Zhang & Bishan Liang & Ruibin Zhang & Zengjian Wang & Junjing Wang & Ming Liu & Ruiwang Huang, 2015. "Reconfiguration of the Brain Functional Network Associated with Visual Task Demands," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
    13. Hideyuki Miyahara & Hai Qian & Pavan S Holur & Vwani Roychowdhury, 2024. "Emergent invariance and scaling properties in the collective return dynamics of a stock market," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-20, February.
    14. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
    15. Ma, Lili & Jiang, Xin & Wu, Kaiyuan & Zhang, Zhanli & Tang, Shaoting & Zheng, Zhiming, 2012. "Surveying network community structure in the hidden metric space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 371-378.
    16. Wang, Zuxi & Li, Qingguang & Xiong, Wei & Jin, Fengdong & Wu, Yao, 2016. "Fast community detection based on sector edge aggregation metric model in hyperbolic space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 178-191.
    17. Nicol'o Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Risk diversification: a study of persistence with a filtered correlation-network approach," Papers 1410.5621, arXiv.org.
    18. Antoine Allard & M Ángeles Serrano, 2020. "Navigable maps of structural brain networks across species," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-20, February.
    19. De Blasis, Riccardo & Galati, Luca & Grassi, Rosanna & Rizzini, Giorgio, 2024. "Information flow in the FTX bankruptcy: A network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 655(C).
    20. M. Raddant & T. Di Matteo, 2023. "A look at financial dependencies by means of econophysics and financial economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(4), pages 701-734, October.
    21. Giuseppe Brandi & T. Di Matteo, 2020. "A new multilayer network construction via Tensor learning," Papers 2004.05367, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925009427. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.