% 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{Bajada:877949,
      author       = {Bajada, Claude J and Campos, Lucas Q Costa and Caspers,
                      Svenja and Muscat, Richard and Parker, Geoff J M and Ralph,
                      Matthew A Lambon and Cloutman, Lauren L and
                      Trujillo-Barreto, Nelson J},
      title        = {{A} tutorial and tool for exploring feature similarity
                      gradients with {MRI} data.},
      journal      = {NeuroImage},
      volume       = {221},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2020-02528},
      pages        = {117140},
      year         = {2020},
      abstract     = {There has been an increasing interest in examining
                      organisational principles of the cerebral cortex (and
                      subcortical regions) using different MRI features such as
                      structural or functional connectivity. Despite the
                      widespread interest, introductory tutorials on the
                      underlying technique targeted for the novice neuroimager are
                      sparse in the literature. Articles that investigate various
                      'neural gradients' (for example based on region studied
                      'cortical gradients,' 'cerebellar gradients,' 'hippocampal
                      gradients' etc … or feature of interest 'functional
                      gradients,' 'cytoarchitectural gradients,'
                      'myeloarchitectural gradients' etc…) have increased in
                      popularity. Thus, we believe that it is opportune to discuss
                      what is generally meant by 'gradient analysis'. We introduce
                      basics concepts in graph theory, such as graphs themselves,
                      the degree matrix, and the adjacency matrix. We discuss how
                      one can think about gradients of feature similarity (the
                      similarity between timeseries in fMRI, or streamline in
                      tractography) using graph theory and we extend this to
                      explore such gradients across the whole MRI scale; from the
                      voxel level to the whole brain level. We proceed to
                      introduce a measure for quantifying the level of similarity
                      in regions of interest. We propose the term 'the Vogt-Bailey
                      index' for such quantification to pay homage to our history
                      as a brain mapping community. We run through the techniques
                      on sample datasets including a brain MRI as an example of
                      the application of the techniques on real data and we
                      provide several appendices that expand upon details. To
                      maximise intuition, the appendices contain a didactic
                      example describing how one could use these techniques to
                      solve a particularly pernicious problem that one may
                      encounter at a wedding. Accompanying the article is a tool,
                      available in both MATLAB and Python, that enables readers to
                      perform the analysis described in this article on their own
                      data. We refer readers to the graphical abstract as an
                      overview of the analysis pipeline presented in this work.},
      cin          = {INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32650053},
      UT           = {WOS:000600795000011},
      doi          = {10.1016/j.neuroimage.2020.117140},
      url          = {https://juser.fz-juelich.de/record/877949},
}