Cluster extent inference revisited: quantification and localization of brain activity

arXiv (Cornell University)

Abstract

Cluster inference based on spatial extent thresholding is the most popular analysis method for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding brain regions with some activation, the method as currently defined does not allow any further quantification or localization of signal. In this paper we repair this gap. We show that cluster-extent inference can be used (1.) to infer the presence of signal in anatomical regions of interest and (2.) to quantify the percentage of active voxels in any cluster or region of interest. These additional inferences come for free, i.e. they do not require any further adjustment of the alpha-level of tests, while retaining full familywise error control. We achieve this extension of the possibilities of cluster inference by an embedding of the method into a closed testing procedure, and solving the graph-theoretic k-separator problem that results from this embedding. The new method can be used in combination with random field theory or permutations. We demonstrate the usefulness of the method in a large-scale application to neuroimaging data from the Neurovault database.

Authors 6

1. Jelle J. Goeman Aachen Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Biomedical Data Sciences , Leiden University Medical Center , Leiden , The Netherlands

2. Paweł Górecki

   University of Warsaw

   Affiliation as printed

   Institute of Informatics , Faculty of Mathematics, Informatics and Mechanics , University of Warsaw , Poland

3. Ramin Monajemi Aachen Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Biomedical Data Sciences , Leiden University Medical Center , Leiden , The Netherlands

4. Xu Chen Aachen Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Biomedical Data Sciences , Leiden University Medical Center , Leiden , The Netherlands

5. Thomas E. Nichols

   University of Oxford · Wellcome Centre for Integrative Neuroimaging · Nuffield Health

   Affiliation as printed

   Big Data Institute , Li Ka Shing Centre for Health Information and Discovery , Nuffield Department of Population Health , University of Oxford , UK

   Wellcome Centre for Integrative Neuroimaging , FMRIB , Nuffield Department of Clinical Neurosciences , University of Oxford , UK

6. Wouter D. Weeda Aachen Methodology and Statistics

   Leiden University

   Affiliation as printed

   Methodology and Statistics, Psychology , Leiden University , The Netherlands