Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Statistical inference for fMRI at 3T and beyond


Lohmann,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Lohmann, G. (2019). Statistical inference for fMRI at 3T and beyond. Talk presented at Max Planck Institute for Biological Cybernetics. Tübingen, Germany. 2019-10-17.

Cite as: https://hdl.handle.net/21.11116/0000-0004-D532-3
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.