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| 001 | 863645 | ||
| 005 | 20210130002242.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2019.06.037 |2 doi |
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| 245 | _ | _ | |a Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis |
| 260 | _ | _ | |a Orlando, Fla. |c 2019 |b Academic Press |
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| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Nguyen, Minh |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Sevinc, Gunes |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Fox, Peter T. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Spreng, R. Nathan |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 P:(DE-HGF)0 |b 6 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.neuroimage.2019.06.037 |g Vol. 200, p. 142 - 158 |0 PERI:(DE-600)1471418-8 |p 142 - 158 |t NeuroImage |v 200 |y 2019 |x 1053-8119 |
| 856 | 4 | _ | |y Published on 2019-06-20. Available in OpenAccess from 2020-06-20. |u https://juser.fz-juelich.de/record/863645/files/Ngo19.pdf |
| 856 | 4 | _ | |y Published on 2019-06-20. Available in OpenAccess from 2020-06-20. |x pdfa |u https://juser.fz-juelich.de/record/863645/files/Ngo19.pdf?subformat=pdfa |
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