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Smoothness without Smoothing: Why Gaussian Naive Bayes Is Not Naive for Multi-Subject Searchlight Studies

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  • Rajeev D S Raizada
  • Yune-Sang Lee

Abstract

Spatial smoothness is helpful when averaging fMRI signals across multiple subjects, as it allows different subjects' corresponding brain areas to be pooled together even if they are slightly misaligned. However, smoothing is usually not applied when performing multivoxel pattern-based analyses (MVPA), as it runs the risk of blurring away the information that fine-grained spatial patterns contain. It would therefore be desirable, if possible, to carry out pattern-based analyses which take unsmoothed data as their input but which produce smooth images as output. We show here that the Gaussian Naive Bayes (GNB) classifier does precisely this, when it is used in “searchlight” pattern-based analyses. We explain why this occurs, and illustrate the effect in real fMRI data. Moreover, we show that analyses using GNBs produce results at the multi-subject level which are statistically robust, neurally plausible, and which replicate across two independent data sets. By contrast, SVM classifiers applied to the same data do not generate a replication, even if the SVM-derived searchlight maps have smoothing applied to them. An additional advantage of GNB classifiers for searchlight analyses is that they are orders of magnitude faster to compute than more complex alternatives such as SVMs. Collectively, these results suggest that Gaussian Naive Bayes classifiers may be a highly non-naive choice for multi-subject pattern-based fMRI studies.

Suggested Citation

  • Rajeev D S Raizada & Yune-Sang Lee, 2013. "Smoothness without Smoothing: Why Gaussian Naive Bayes Is Not Naive for Multi-Subject Searchlight Studies," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-10, July.
  • Handle: RePEc:plo:pone00:0069566
    DOI: 10.1371/journal.pone.0069566
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    References listed on IDEAS

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    1. David J. Hand & Keming Yu, 2001. "Idiot's Bayes—Not So Stupid After All?," International Statistical Review, International Statistical Institute, vol. 69(3), pages 385-398, December.
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    Cited by:

    1. Ahmad Saeed Mohammad & Musab T. S. Al-Kaltakchi & Jabir Alshehabi Al-Ani & Jonathon A. Chambers, 2023. "Comprehensive Evaluations of Student Performance Estimation via Machine Learning," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
    2. Guerreiro, Lucas & Silva, Filipi N. & Amancio, Diego R., 2024. "Recovering network topology and dynamics from sequences: A machine learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    3. Afrah Salman Dawood, 2023. "Performance Evaluation of Machine Learning Nave Bayes Algorithms for Network Traffic Classification," Technium, Technium Science, vol. 13(1), pages 12-26.

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