% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Mohammadzadeh:1006859,
      author       = {Mohammadzadeh, Morteza and Tahmasian, Masoud and Rasekhi,
                      Aliakbar},
      title        = {{O}n {T}he {S}earch for {C}onvergence of {F}unctional
                      {B}rain {P}atterns across {N}euroimaging {S}tudies: {A}
                      {C}oordinate-{B}ased {M}eta-{A}nalysis {U}sing {G}ibbs
                      {P}oint {P}rocess},
      journal      = {Journal of Biostatistics and Epidemiology},
      volume       = {8},
      number       = {3},
      issn         = {2383-4196},
      address      = {Tehran},
      publisher    = {Tehran University of Medical Sciences},
      reportid     = {FZJ-2023-01901},
      pages        = {.},
      year         = {2022},
      abstract     = {Introduction: Coordinate-based meta-analysis (CBMA) is a
                      standard method for integrating brain functional patterns in
                      neuroimaging studies. CBMA aims to identify convergency in
                      activated brain regions across studies using coordinates of
                      the peak activation (foci). Here, we aimed to introduce a
                      new application of the Gibbs models for the meta-regression
                      of the neuroimaging studies.Methods: We used a dataset
                      acquired from 31 studies by previous work. For each study as
                      well as foci, study features such as SD duration and the
                      average age were extracted. Two widely Gibbs models,
                      Area-interaction and Geyer saturation were fitted on the
                      foci. These models can quantify and test evidence for
                      clusters in foci using an interaction parameter. We included
                      study features in the models to identify their contribution
                      to foci distribution and hence determine sources of the
                      heterogeneity.Results: Our results revealed that latent
                      study-specific features have a moderate contribution to the
                      heterogeneity of foci distribution. However, the effect of
                      age and SD duration was not significant (p<0.001).
                      Additionally, the estimated interaction parameter was 1.34
                      (p<0.001) which denotes strong evidence of clusters in
                      foci.Conclusions: Overall, this study highlighted the role
                      of the interaction parameter in CBMA. The results of this
                      work suggest that Gibbs models can be considered as a
                      promising tool for neuroimaging meta-analysis},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.18502/jbe.v8i3.12305},
      url          = {https://juser.fz-juelich.de/record/1006859},
}