PD Dr. Gabriele Lohmann: Statistical inference for fMRI at 3T and beyond

  • Datum: 17.10.2019
  • Uhrzeit: 10:00 - 11:00
  • Vortragende: Gabriele Lohmann
  • Department High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics
  • Ort: Max-Planck-Ring 8
  • Raum: room 203
  • Gastgeber: Zhaoping Li
  • Kontakt: ira.breitkreuz@tuebingen.mpg.de
PD Dr. Gabriele Lohmann: Statistical inference for fMRI at 3T and beyond
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 [revisouly 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.
Zur Redakteursansicht