Statistical Inference for fMRI Data

Reproducibility across 100 tests based on randomly drawn samples of size twenty. The colors represent reproducibility scores, i.e. the number of tests in which a given voxel consistently passed the significance threshold. The underlying blue areas show the reference map which was derived using all 400 subjects. — **Reproducibility across 100 tests based on randomly drawn samples of size twenty.** The colors represent reproducibility scores, i.e. the number of tests in which a given voxel consistently passed the significance threshold. The underlying blue areas show the reference map which was derived using all 400 subjects.

**Reproducibility across 100 tests based on randomly drawn samples of size twenty.** The colors represent reproducibility scores, i.e. the number of tests in which a given voxel consistently passed the significance threshold. The underlying blue areas show the reference map which was derived using all 400 subjects.

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. In this study, 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).

Lohmann, G., Stelzer, J., Lacosse, E. et al.

LISA improves statistical analysis for fMRI.

Nature Comm (9):4014 (2018).

DOI