TY  - JOUR
AU  - Ngo, Gia H.
AU  - Eickhoff, Simon B.
AU  - Nguyen, Minh
AU  - Sevinc, Gunes
AU  - Fox, Peter T.
AU  - Spreng, R. Nathan
AU  - Yeo, B. T. Thomas
TI  - Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis
JO  - NeuroImage
VL  - 200
SN  - 1053-8119
CY  - Orlando, Fla.
PB  - Academic Press
M1  - FZJ-2019-03656
SP  - 142 - 158
PY  - 2019
AB  - 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).
LB  - PUB:(DE-HGF)16
C6  - pmid:31229658
UR  - <Go to ISI:>//WOS:000481579300012
DO  - DOI:10.1016/j.neuroimage.2019.06.037
UR  - https://juser.fz-juelich.de/record/863645
ER  -