IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1008153.html
   My bibliography  Save this article

Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

Author

Listed:
  • Ru-Yuan Zhang
  • Xue-Xin Wei
  • Kendrick Kay

Abstract

Previous studies in neurophysiology have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We show that this form of voxelwise NCs can improve MVPA performance if NCs are sufficiently strong. We also confirm these results using standard information-theoretic analyses in computational neuroscience. In the same theoretical framework, we further demonstrate that the effects of noise correlations at both the neuronal level and the voxel level may manifest differently in typical fMRI data, and their effects are modulated by tuning heterogeneity. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.Author summary: Noise correlation (NC) is the key component of multivariate response distributions and thus characterizing its effects on population codes is the cornerstone for understanding probabilistic computation in the brain. Despite extensive studies of NCs in neurophysiology, little is known with respect to their role in functional magnetic resonance imaging (fMRI). We characterize the effect of voxelwise NC by building voxel-encoding models and directly quantifying the amount of information in simulated multivariate fMRI data. In contrast to the detrimental effects of NC implied in neurophysiological studies, we find that voxelwise NCs can enhance information codes if NC is sufficiently strong. Our work highlights the important role of noise correlations in decipher population codes using fMRI.

Suggested Citation

  • Ru-Yuan Zhang & Xue-Xin Wei & Kendrick Kay, 2020. "Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-29, August.
  • Handle: RePEc:plo:pcbi00:1008153
    DOI: 10.1371/journal.pcbi.1008153
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008153
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008153&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1008153?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. Ingmar Kanitscheider & Ruben Coen-Cagli & Adam Kohn & Alexandre Pouget, 2015. "Measuring Fisher Information Accurately in Correlated Neural Populations," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-27, June.
    2. Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    3. Vikranth R Bejjanki & Rava Azeredo da Silveira & Jonathan D Cohen & Nicholas B Turk-Browne, 2017. "Noise correlations in the human brain and their impact on pattern classification," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-23, August.
    4. Paul T E Cusack, 2020. "The Human Brain," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24261-24266, October.
    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. Dominic Holland & Oleksandr Frei & Rahul Desikan & Chun-Chieh Fan & Alexey A Shadrin & Olav B Smeland & V S Sundar & Paul Thompson & Ole A Andreassen & Anders M Dale, 2020. "Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model," PLOS Genetics, Public Library of Science, vol. 16(5), pages 1-30, May.
    2. Ashok Litwin-Kumar & Anne-Marie M Oswald & Nathaniel N Urban & Brent Doiron, 2011. "Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-14, December.
    3. Julia Berezutskaya & Zachary V Freudenburg & Umut Güçlü & Marcel A J van Gerven & Nick F Ramsey, 2020. "Brain-optimized extraction of complex sound features that drive continuous auditory perception," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-34, July.
    4. Abigail B. Schneider & Bridget Leonard, 2022. "From anxiety to control: Mask‐wearing, perceived marketplace influence, and emotional well‐being during the COVID‐19 pandemic," Journal of Consumer Affairs, Wiley Blackwell, vol. 56(1), pages 97-119, March.
    5. Geonhui Lee & Woong Choi & Hanjin Jo & Wookhyun Park & Jaehyo Kim, 2020. "Analysis of motor control strategy for frontal and sagittal planes of circular tracking movements using visual feedback noise from velocity change and depth information," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    6. Odelaisy León-Triana & Julián Pérez-Beteta & David Albillo & Ana Ortiz de Mendivil & Luis Pérez-Romasanta & Elisabet González-Del Portillo & Manuel Llorente & Natalia Carballo & Estanislao Arana & Víc, 2021. "Brain Metastasis Response to Stereotactic Radio Surgery: A Mathematical Approach," Mathematics, MDPI, vol. 9(7), pages 1-19, March.
    7. Mirren Charnley & Saba Islam & Guneet K. Bindra & Jeremy Engwirda & Julian Ratcliffe & Jiangtao Zhou & Raffaele Mezzenga & Mark D. Hulett & Kyunghoon Han & Joshua T. Berryman & Nicholas P. Reynolds, 2022. "Neurotoxic amyloidogenic peptides in the proteome of SARS-COV2: potential implications for neurological symptoms in COVID-19," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    8. Samy Castro & Wael El-Deredy & Demian Battaglia & Patricio Orio, 2020. "Cortical ignition dynamics is tightly linked to the core organisation of the human connectome," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-23, July.
    9. Wensheng Sun & Dennis L Barbour, 2017. "Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex," PLOS Biology, Public Library of Science, vol. 15(11), pages 1-22, November.
    10. Nguyen, Ha Trong & Brinkman, Sally & Le, Huong Thu & Zubrick, Stephen R. & Mitrou, Francis, 2022. "Gender differences in time allocation contribute to differences in developmental outcomes in children and adolescents," Economics of Education Review, Elsevier, vol. 89(C).
    11. Gregor Wolbring, 2022. "Auditing the ‘Social’ of Quantum Technologies: A Scoping Review," Societies, MDPI, vol. 12(2), pages 1-38, March.
    12. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    13. April R. Kriebel & Joshua D. Welch, 2022. "UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    14. Boada, Júlia Pareto & Maestre, Begoña Román & Genís, Carme Torras, 2021. "The ethical issues of social assistive robotics: A critical literature review," Technology in Society, Elsevier, vol. 67(C).
    15. Hamed Nili & Alexander Walther & Arjen Alink & Nikolaus Kriegeskorte, 2020. "Inferring exemplar discriminability in brain representations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    16. Valtteri Arstila & Alexandra L Georgescu & Henri Pesonen & Daniel Lunn & Valdas Noreika & Christine M Falter-Wagner, 2020. "Event timing in human vision: Modulating factors and independent functions," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-22, August.
    17. Don, Arjuna P.H. & Peters, James F. & Ramanna, Sheela & Tozzi, Arturo, 2021. "Quaternionic views of rs-fMRI hierarchical brain activation regions. Discovery of multilevel brain activation region intensities in rs-fMRI video frames," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    18. Linzmajer, Marc & Hubert, Mirja & Hubert, Marco, 2021. "It’s about the process, not the result: An fMRI approach to explore the encoding of explicit and implicit price information," Journal of Economic Psychology, Elsevier, vol. 86(C).
    19. Natalie J Shook & Barış Sevi & Jerin Lee & Benjamin Oosterhoff & Holly N Fitzgerald, 2020. "Disease avoidance in the time of COVID-19: The behavioral immune system is associated with concern and preventative health behaviors," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    20. Alexander Anders & Bhaswar Ghosh & Timo Glatter & Victor Sourjik, 2020. "Design of a MAPK signalling cascade balances energetic cost versus accuracy of information transmission," Nature Communications, Nature, vol. 11(1), pages 1-10, December.

    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:plo:pcbi00:1008153. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.