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Abstract

In this talk, I will begin by giving an overview of statistical inference in fMRI.

I will then describe a new approach called “LISA'' for statistical inference of fMRI data.

LISA incorporates spatial context via a nonlinear filter so that no initial cluster-forming threshold is needed, and spatial precision is largely preserved making it suitable for ultrahigh-resolution imaging. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. In a first publication (Lohmann et al, Nature Communications, 2018), we have previously described this method for first-level (single-subject) designs using precoloring as a technique for incorporating temporal autocorrelation, and for simple second-level designs (onesample and twosample group studies).

We have now extended this method so that most scenarios in fMRI-based research are covered.

Specifically, LISA can now also use prewhitening for single-subject analyses, and it can handle arbitrary 2nd-level designs matrices. LISA can thus serve as a general tool for statistical inference in fMRI.