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LISA improves statistical analysis for fMRI

Author

Listed:
  • Gabriele Lohmann

    (University Hospital Tübingen
    Max-Planck-Institute for Biological Cybernetics)

  • Johannes Stelzer

    (University Hospital Tübingen
    Max-Planck-Institute for Biological Cybernetics)

  • Eric Lacosse

    (Max-Planck-Institute for Biological Cybernetics
    Max-Planck-Institute for Intelligent Systems)

  • Vinod J. Kumar

    (Max-Planck-Institute for Biological Cybernetics)

  • Karsten Mueller

    (Max-Planck-Institute for Human Cognitive and Brain Sciences)

  • Esther Kuehn

    (German Center for Neurodegenerative Diseases (DZNE)
    Center for Behavioral Brain Sciences (CBBS)
    Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A)

  • Wolfgang Grodd

    (Max-Planck-Institute for Biological Cybernetics)

  • Klaus Scheffler

    (University Hospital Tübingen
    Max-Planck-Institute for Biological Cybernetics)

Abstract

One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).

Suggested Citation

  • Gabriele Lohmann & Johannes Stelzer & Eric Lacosse & Vinod J. Kumar & Karsten Mueller & Esther Kuehn & Wolfgang Grodd & Klaus Scheffler, 2018. "LISA improves statistical analysis for fMRI," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06304-z
    DOI: 10.1038/s41467-018-06304-z
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    Cited by:

    1. Telschow, Fabian J.E. & Davenport, Samuel & Schwartzman, Armin, 2022. "Functional delta residuals and applications to simultaneous confidence bands of moment based statistics," Journal of Multivariate Analysis, Elsevier, vol. 192(C).

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