LISA improves statistical analysis for fMRI

Gabriele Lohmann (), Johannes Stelzer, Eric Lacosse, Vinod J. Kumar, Karsten Mueller, Esther Kuehn, Wolfgang Grodd and Klaus Scheffler
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Gabriele Lohmann: University Hospital Tübingen
Johannes Stelzer: University Hospital Tübingen
Eric Lacosse: Max-Planck-Institute for Biological Cybernetics
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)
Wolfgang Grodd: Max-Planck-Institute for Biological Cybernetics
Klaus Scheffler: University Hospital Tübingen

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: 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).

Date: 2018
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DOI: 10.1038/s41467-018-06304-z

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