IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49144-w.html
   My bibliography  Save this article

Brain-state mediated modulation of inter-laminar dependencies in visual cortex

Author

Listed:
  • Anirban Das

    (Yale University
    Yale University
    Intel Corp.)

  • Alec G. Sheffield

    (Yale University)

  • Anirvan S. Nandy

    (Yale University
    Yale University
    Yale University
    Yale University)

  • Monika P. Jadi

    (Yale University
    Yale University
    Yale University)

Abstract

Spatial attention is critical for recognizing behaviorally relevant objects in a cluttered environment. How the deployment of spatial attention aids the hierarchical computations of object recognition remains unclear. We investigated this in the laminar cortical network of visual area V4, an area strongly modulated by attention. We found that deployment of attention strengthened unique dependencies in neural activity across cortical layers. On the other hand, shared dependencies were reduced within the excitatory population of a layer. Surprisingly, attention strengthened unique dependencies within a laminar population. Crucially, these modulation patterns were also observed during successful behavioral outcomes that are thought to be mediated by internal brain state fluctuations. Successful behavioral outcomes were also associated with phases of reduced neural excitability, suggesting a mechanism for enhanced information transfer during optimal states. Our results suggest common computation goals of optimal sensory states that are attained by either task demands or internal fluctuations.

Suggested Citation

  • Anirban Das & Alec G. Sheffield & Anirvan S. Nandy & Monika P. Jadi, 2024. "Brain-state mediated modulation of inter-laminar dependencies in visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49144-w
    DOI: 10.1038/s41467-024-49144-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49144-w
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-49144-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. Lyle Muller & Alexandre Reynaud & Frédéric Chavane & Alain Destexhe, 2014. "The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave," Nature Communications, Nature, vol. 5(1), pages 1-14, May.
    2. V Anne Smith & Jing Yu & Tom V Smulders & Alexander J Hartemink & Erich D Jarvis, 2006. "Computational Inference of Neural Information Flow Networks," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-14, November.
    3. Zachary W. Davis & Lyle Muller & Julio Martinez-Trujillo & Terrence Sejnowski & John H. Reynolds, 2020. "Spontaneous travelling cortical waves gate perception in behaving primates," Nature, Nature, vol. 587(7834), pages 432-436, November.
    4. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    5. Jonathan W. Pillow & Jonathon Shlens & Liam Paninski & Alexander Sher & Alan M. Litke & E. J. Chichilnisky & Eero P. Simoncelli, 2008. "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, Nature, vol. 454(7207), pages 995-999, August.
    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. Dominik P. Koller & Michael Schirner & Petra Ritter, 2024. "Human connectome topology directs cortical traveling waves and shapes frequency gradients," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Adeeti Aggarwal & Connor Brennan & Jennifer Luo & Helen Chung & Diego Contreras & Max B. Kelz & Alex Proekt, 2022. "Visual evoked feedforward–feedback traveling waves organize neural activity across the cortical hierarchy in mice," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Seif Eldawlatly & Karim G Oweiss, 2011. "Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-14, June.
    4. Gabriel B. Benigno & Roberto C. Budzinski & Zachary W. Davis & John H. Reynolds & Lyle Muller, 2023. "Waves traveling over a map of visual space can ignite short-term predictions of sensory input," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Georgios Spyropoulos & Matteo Saponati & Jarrod Robert Dowdall & Marieke Louise Schölvinck & Conrado Arturo Bosman & Bruss Lima & Alina Peter & Irene Onorato & Johanna Klon-Lipok & Rasmus Roese & Serg, 2022. "Spontaneous variability in gamma dynamics described by a damped harmonic oscillator driven by noise," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    6. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    7. Alberti, Federica & Mantilla, César, 2020. "Provision of noxious facilities using a market-like mechanism: A simple implementation in the lab," Working papers 35, Red Investigadores de Economía.
    8. Arne F Meyer & Jan-Philipp Diepenbrock & Max F K Happel & Frank W Ohl & Jörn Anemüller, 2014. "Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-15, April.
    9. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Post-Print halshs-00917797, HAL.
    10. Sandro Radovanovic & Boris Delibasic & Milija Suknovic & Dajana Matovic, 2019. "Where will the next ski injury occur? A system for visual and predictive analytics of ski injuries," Operational Research, Springer, vol. 19(4), pages 973-992, December.
    11. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    12. Jonathan Rubin & Nachum Ulanovsky & Israel Nelken & Naftali Tishby, 2016. "The Representation of Prediction Error in Auditory Cortex," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
    13. Franklin Leong & Babak Rahmani & Demetri Psaltis & Christophe Moser & Diego Ghezzi, 2024. "An actor-model framework for visual sensory encoding," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    14. Lucas Rudelt & Daniel González Marx & Michael Wibral & Viola Priesemann, 2021. "Embedding optimization reveals long-lasting history dependence in neural spiking activity," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-51, June.
    15. Zhang, Guike & Gao, Zengan & Dong, June & Mei, Dexiang, 2023. "Machine learning approaches for constructing the national anti-money laundering index," Finance Research Letters, Elsevier, vol. 52(C).
    16. Pengcheng Zhou & Shawn D Burton & Adam C Snyder & Matthew A Smith & Nathaniel N Urban & Robert E Kass, 2015. "Establishing a Statistical Link between Network Oscillations and Neural Synchrony," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-25, October.
    17. Lee Anthony & Caron Francois & Doucet Arnaud & Holmes Chris, 2012. "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-31, January.
    18. Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    19. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
    20. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," SFB 649 Discussion Papers 2014-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    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:15:y:2024:i:1:d:10.1038_s41467-024-49144-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.