التفاصيل البيبلوغرافية
| العنوان: |
Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis. |
| المؤلفون: |
Ngo, Gia H.1,2 (AUTHOR), Eickhoff, Simon B.3,4 (AUTHOR), Nguyen, Minh1 (AUTHOR), Sevinc, Gunes5 (AUTHOR), Fox, Peter T.6,7 (AUTHOR), Spreng, R. Nathan8,9 (AUTHOR), Yeo, B.T. Thomas1,10,11,12 (AUTHOR) thomas.yeo@nus.edu.sg |
| المصدر: |
NeuroImage. Oct2019, Vol. 200, p142-158. 17p. |
| مصطلحات موضوعية: |
*INDEPENDENT component analysis, *META-analysis, *HETEROGENEITY, *PATTERNS (Mathematics) |
| مستخلص: |
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_AuthorTopicTest). [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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