IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34018-w.html
   My bibliography  Save this article

Micro-scale functional modules in the human temporal lobe

Author

Listed:
  • Julio I. Chapeton

    (Surgical Neurology Branch, NINDS, National Institutes of Health)

  • John H. Wittig

    (Surgical Neurology Branch, NINDS, National Institutes of Health)

  • Sara K. Inati

    (Surgical Neurology Branch, NINDS, National Institutes of Health)

  • Kareem A. Zaghloul

    (Surgical Neurology Branch, NINDS, National Institutes of Health)

Abstract

The sensory cortices of many mammals are often organized into modules in the form of cortical columns, yet whether modular organization at this spatial scale is a general property of the human neocortex is unknown. The strongest evidence for modularity arises when measures of connectivity, structure, and function converge. Here we use microelectrode recordings in humans to examine functional connectivity and neuronal spiking responses in order to assess modularity in submillimeter scale networks. We find that the human temporal lobe consists of temporally persistent spatially compact modules approximately 1.3mm in diameter. Functionally, the information coded by single neurons during an image categorization task is more similar for neurons belonging to the same module than for neurons from different modules. The geometry, connectivity, and spiking responses of these local cortical networks provide converging evidence that the human temporal lobe is organized into functional modules at the micro scale.

Suggested Citation

  • Julio I. Chapeton & John H. Wittig & Sara K. Inati & Kareem A. Zaghloul, 2022. "Micro-scale functional modules in the human temporal lobe," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34018-w
    DOI: 10.1038/s41467-022-34018-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34018-w
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-34018-w?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
    ---><---

    References listed on IDEAS

    as
    1. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    2. 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.
    3. Jessica Schrouff & Omri Raccah & Sori Baek & Vinitha Rangarajan & Sina Salehi & Janaina Mourão-Miranda & Zeinab Helili & Amy L. Daitch & Josef Parvizi, 2020. "Fast temporal dynamics and causal relevance of face processing in the human temporal cortex," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    4. Mohammad Dastjerdi & Muge Ozker & Brett L. Foster & Vinitha Rangarajan & Josef Parvizi, 2013. "Numerical processing in the human parietal cortex during experimental and natural conditions," Nature Communications, Nature, vol. 4(1), pages 1-11, December.
    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. Wilhelm, Thomas & Hollunder, Jens, 2007. "Information theoretic description of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 385-396.
    2. Rodica Ioana Lung & Camelia Chira & Anca Andreica, 2014. "Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    3. Zhong, Weiqiong & An, Haizhong & Shen, Lei & Fang, Wei & Gao, Xiangyun & Dong, Di, 2017. "The roles of countries in the international fossil fuel trade: An emergy and network analysis," Energy Policy, Elsevier, vol. 100(C), pages 365-376.
    4. Kim, Paul & Kim, Sangwook, 2015. "Detecting overlapping and hierarchical communities in complex network using interaction-based edge clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 46-56.
    5. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    6. Li Wang & Jiang Wang & Yuanjun Bi & Weili Wu & Wen Xu & Biao Lian, 2014. "Noise-tolerance community detection and evolution in dynamic social networks," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 600-612, October.
    7. Angelou, Konstantinos & Maragakis, Michael & Kosmidis, Kosmas & Argyrakis, Panos, 2020. "A hybrid model for the patent citation network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    9. Jun Gui & Zeyu Zheng & Dianzheng Fu & Zihao Yang & Yuan Gao & Zhi Liu, 2020. "Dynamics of calling activity to toll-free numbers in China," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-16, March.
    10. Mattia G. Bergomi & Massimo Ferri & Pietro Vertechi & Lorenzo Zuffi, 2021. "Beyond Topological Persistence: Starting from Networks," Mathematics, MDPI, vol. 9(23), pages 1-15, November.
    11. Meng, Fanyuan & Zhu, Jiadong & Yao, Yuheng & Fenoaltea, Enrico Maria & Xie, Yubo & Yang, Pingle & Liu, Run-Ran & Zhang, Jianlin, 2023. "Disagreement and fragmentation in growing groups," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    12. Nedioui, Med Abdelhamid & Moussaoui, Abdelouahab & Saoud, Bilal & Babahenini, Mohamed Chaouki, 2020. "Detecting communities in social networks based on cliques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    13. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    14. Andreas Fischer & Igor Litvinchev & Tetyana Romanova & Petro Stetsyuk & Georgiy Yaskov, 2023. "Quasi-Packing Different Spheres with Ratio Conditions in a Spherical Container," Mathematics, MDPI, vol. 11(9), pages 1-19, April.
    15. An, Haizhong & Zhong, Weiqiong & Chen, Yurong & Li, Huajiao & Gao, Xiangyun, 2014. "Features and evolution of international crude oil trade relationships: A trading-based network analysis," Energy, Elsevier, vol. 74(C), pages 254-259.
    16. Zhang, Beibei & Zhou, Yadong & Xu, Xiaoyan & Wang, Dai & Guan, Xiaohong, 2016. "Dynamic structure evolution of time-dependent network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 347-358.
    17. Xuanru Zhou & Hua Zhang & Shuxian Zheng & Wanli Xing & Pei Zhao & Haiying Li, 2022. "The Crude Oil International Trade Competition Networks: Evolution Trends and Estimating Potential Competition Links," Energies, MDPI, vol. 15(7), pages 1-20, March.
    18. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    19. Aslan, Serpil & Kaya, Buket & Kaya, Mehmet, 2019. "Predicting potential links by using strengthened projections in evolving bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 998-1011.
    20. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.

    More about this item

    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:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34018-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.