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Journal Article

LISA improves statistical analysis for fMRI

MPS-Authors
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Mueller,  Karsten
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Kuehn,  Esther
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
German Center for Neurodegenerative Diseases, Magdeburg, Germany;
Center for Behavioral Brain Sciences, Magdeburg, Germany;

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Lohmann_Stelzer_2018.pdf
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Citation

Lohmann, G., Stelzer, J., Lacosse, E., Kumar, V. J., Mueller, K., Kuehn, E., et al. (2018). LISA improves statistical analysis for fMRI. Nature Communications, 9: 4014. doi:10.1038/s41467-018-06304-z.


Cite as: http://hdl.handle.net/21.11116/0000-0002-53AA-1
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).