PD Dr Gabriele Lohmann | Statistical inference and new approaches to eigenvector centrality mapping for fMRI at 3T and beyond

Gastvortrag

  • Datum: 11.02.2019
  • Uhrzeit: 15:00 - 16:00
  • Vortragende(r): PD Dr Gabriele Lohmann
  • Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
  • Ort: MPI für Kognitions- und Neurowissenschaften
  • Raum: Wilhelm Wundt Raum (A400)
In this talk, I will 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.

In the second part of the talk I will introduce new approaches to eigenvector centrality mapping (ECM).

ECM is a popular technique for analyzing fMRI data of the human brain (Lohmann et al. PLoS ONE, 2010).

It is used to obtain maps of functional hubs in networks of the brain in a manner similar to Google's PageRank algorithm.

Currently, there exist two different implementations ECM, one of which is very fast but

limited to one particular type of correlation metric whose interpretation can be problematic.

The second implementation supports many different metrics, but it is computationally costly

and requires a very large main memory making it infeasible for ultrahigh-resolution imaging.

In this talk, I will introduce two new implementations of the ECM approach that resolve these issues

(Lohmann et al, bioRxiv, 2018). The first technique is based on a new correlation metric that we call

"ReLU correlation (RLC)". The second technique is based on matrix projections.

Both methods are suitable for ultrahigh-resolution imaging.
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