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@ARTICLE{Ngo:863645,
      author       = {Ngo, Gia H. and Eickhoff, Simon B. and Nguyen, Minh and
                      Sevinc, Gunes and Fox, Peter T. and Spreng, R. Nathan and
                      Yeo, B. T. Thomas},
      title        = {{B}eyond consensus: {E}mbracing heterogeneity in curated
                      neuroimaging meta-analysis},
      journal      = {NeuroImage},
      volume       = {200},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2019-03656},
      pages        = {142 - 158},
      year         = {2019},
      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
                      $(https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).$},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31229658},
      UT           = {WOS:000481579300012},
      doi          = {10.1016/j.neuroimage.2019.06.037},
      url          = {https://juser.fz-juelich.de/record/863645},
}