Journal Article

FZJ-2019-03656

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis

Ngo, G. H. ; Eickhoff, S. B.FZJ* ; Nguyen, M. ; Sevinc, G. ; Fox, P. T. ; Spreng, R. N. ; Yeo, B. T. T. (Corresponding author)

2019
Academic Press Orlando, Fla.

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Please use a persistent id in citations: http://hdl.handle.net/2128/23402  doi:10.1016/j.neuroimage.2019.06.037

Abstract: Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore, ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. Overall, the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)

Appears in the scientific report 2019

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Database coverage:
; ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; Nationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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