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@ARTICLE{Stangl:139417,
      author       = {Stangl, Matthias and Shine, Jonathan and Wolbers, Thomas},
      title        = {{T}he {G}rid{CAT}: {A} {T}oolbox for {A}utomated {A}nalysis
                      of {H}uman {G}rid {C}ell {C}odes in f{MRI}.},
      journal      = {Frontiers in neuroinformatics},
      volume       = {11},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {DZNE-2020-05739},
      pages        = {47},
      year         = {2017},
      abstract     = {Human functional magnetic resonance imaging (fMRI) studies
                      examining the putative firing of grid cells (i.e., the grid
                      code) suggest that this cellular mechanism supports not only
                      spatial navigation, but also more abstract cognitive
                      processes. Despite increased interest in this research,
                      there remain relatively few human grid code studies, perhaps
                      due to the complex analysis methods, which are not included
                      in standard fMRI analysis packages. To overcome this, we
                      have developed the Matlab-based open-source Grid Code
                      Analysis Toolbox (GridCAT), which performs all analyses,
                      from the estimation and fitting of the grid code in the
                      general linear model (GLM), to the generation of grid code
                      metrics and plots. The GridCAT, therefore, opens up this
                      cutting-edge research area by allowing users to analyze data
                      by means of a simple and user-friendly graphical user
                      interface (GUI). Researchers confident with programming can
                      edit the open-source code and use example scripts
                      accompanying the GridCAT to implement their own analysis
                      pipelines. Here, we review the current literature in the
                      field of fMRI grid code research with particular focus on
                      the different analysis options that have been implemented,
                      which we describe in detail. Key features of the GridCAT are
                      demonstrated via analysis of an example dataset, which is
                      also provided online together with a detailed manual, so
                      that users can replicate the results presented here, and
                      explore the GridCAT's functionality. By making the GridCAT
                      available to the wider neuroscience community, we believe
                      that it will prove invaluable in elucidating the role of
                      grid codes in higher-order cognitive processes.},
      cin          = {AG Wolbers},
      ddc          = {610},
      cid          = {I:(DE-2719)1310002},
      pnm          = {344 - Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28785214},
      pmc          = {pmc:PMC5519580},
      doi          = {10.3389/fninf.2017.00047},
      url          = {https://pub.dzne.de/record/139417},
}