@article{e37a13b4692646869fca2bceb97a8eb8,
title = "Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis",
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).",
keywords = "Attentional control, Autobiographical memory, Executive function, Inhibition, Mental disorder subtypes, Theory of mind",
author = "Ngo, {Gia H.} and Eickhoff, {Simon B.} and Minh Nguyen and Gunes Sevinc and Fox, {Peter T.} and Spreng, {R. Nathan} and Yeo, {B. T.Thomas}",
note = "Funding Information: This work was supported by Singapore MOE Tier 2 ( MOE2014-T2-2-016 ), NUS Strategic Research ( DPRT/944/09/14 ), NUS SOM Aspiration Fund ( R185000271720 ), Singapore NMRC ( CBRG/0088/2015 ), NUS YIA and the Singapore National Research Foundation Fellowship (Class of 2017). Simon Eickhoff is supported by the National Institute of Mental Health ( R01-MH074457 ), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 7202070 (HBP SGA1). R. Nathan Spreng is supported by the Natural Sciences and Engineering Research Council of Canada , the Canadian Institutes of Health Research , and received salary support from the Fonds de la Recherche du Quebec – Sant{\'e} (FRQS) . Comprehensive access to the BrainMap database was authorized by a collaborative-use license agreement ( http://www.brainmap.org/collaborations.html ). BrainMap database development is supported by NIH/NIMH R01 MH074457. Our research also utilized resources provided by the Center for Functional Neuroimaging Technologies, NIH P41EB015896 and instruments supported by NIH 1S10RR023401, NIH 1S10RR019307, and NIH 1S10RR023043 from the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. Our computational work was partially performed on resources of the National Supercomputing Centre, Singapore ( https://www.nscc.sg ). Funding Information: This work was supported by Singapore MOE Tier 2 (MOE2014-T2-2-016), NUS Strategic Research (DPRT/944/09/14), NUS SOM Aspiration Fund (R185000271720), Singapore NMRC (CBRG/0088/2015), NUS YIA and the Singapore National Research Foundation Fellowship (Class of 2017). Simon Eickhoff is supported by the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 7202070 (HBP SGA1). R. Nathan Spreng is supported by the Natural Sciences and Engineering Research Council of Canada, the Canadian Institutes of Health Research, and received salary support from the Fonds de la Recherche du Quebec – Sant{\'e} (FRQS). Comprehensive access to the BrainMap database was authorized by a collaborative-use license agreement (http://www.brainmap.org/collaborations.html). BrainMap database development is supported by NIH/NIMH R01 MH074457. Our research also utilized resources provided by the Center for Functional Neuroimaging Technologies, NIH P41EB015896 and instruments supported by NIH 1S10RR023401, NIH 1S10RR019307, and NIH 1S10RR023043 from the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. Our computational work was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg). Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",
year = "2019",
month = oct,
day = "15",
doi = "10.1016/j.neuroimage.2019.06.037",
language = "English (US)",
volume = "200",
pages = "142--158",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
}