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@ARTICLE{Bachmann:851051,
      author       = {Bachmann, Claudia and Jacobs, Heidi I. L. and Porta Mana,
                      PierGianLuca and Dillen, Kim and Richter, Nils and von
                      Reutern, Boris and Dronse, Julian and Onur, Oezguer A. and
                      Langen, Karl-Josef and Fink, Gereon Rudolf and Kukolja,
                      Juraj and Morrison, Abigail},
      title        = {{O}n the {E}xtraction and {A}nalysis of {G}raphs {F}rom
                      {R}esting-{S}tate f{MRI} to {S}upport a {C}orrect and
                      {R}obust {D}iagnostic {T}ool for {A}lzheimer's {D}isease},
      journal      = {Frontiers in neuroscience},
      volume       = {12},
      issn         = {1662-453X},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2018-04764},
      pages        = {528},
      year         = {2018},
      abstract     = {The diagnosis of Alzheimer's disease (AD), especially in
                      the early stage, is still not very reliable and the
                      development of new diagnosis tools is desirable. A diagnosis
                      based on functional magnetic resonance imaging (fMRI) is a
                      suitable candidate, since fMRI is non-invasive, readily
                      available, and indirectly measures synaptic dysfunction,
                      which can be observed even at the earliest stages of AD.
                      However, the results of previous attempts to analyze graph
                      properties of resting state fMRI data are contradictory,
                      presumably caused by methodological differences in graph
                      construction. This comprises two steps: clustering the
                      voxels of the functional image to define the nodes of the
                      graph, and calculating the graph's edge weights based on a
                      functional connectivity measure of the average cluster
                      activities. A variety of methods are available for each
                      step, but the robustness of results to method choice, and
                      the suitability of the methods to support a diagnostic tool,
                      are largely unknown. To address this issue, we employ a
                      range of commonly and rarely used clustering and edge
                      definition methods and analyze their graph theoretic
                      measures (graph weight, shortest path length, clustering
                      coefficient, and weighted degree distribution and
                      modularity) on a small data set of 26 healthy controls, 16
                      subjects with mild cognitive impairment (MCI) and 14 with
                      Alzheimer's disease. We examine the results with respect to
                      statistical significance of the mean difference in graph
                      properties, the sensitivity of the results to model and
                      parameter choices, and relative diagnostic power based on
                      both a statistical model and support vector machines. We
                      find that different combinations of graph construction
                      techniques yield contradicting, but statistically
                      significant, relations of graph properties between health
                      conditions, explaining the discrepancy across previous
                      studies, but casting doubt on such analyses as a method to
                      gain insight into disease effects. The production of
                      significant differences in mean graph properties turns out
                      not to be a good predictor of future diagnostic capacity.
                      Highest predictive power, expressed by largest negative
                      surprise values, are achieved for both atlas-driven and
                      data-driven clustering (Ward clustering), as long as graphs
                      are small and clusters large, in combination with edge
                      definitions based on correlations and mutual information
                      transfer.},
      cin          = {JARA-BRAIN / INM-6 / INM-4 / INM-3},
      ddc          = {610},
      cid          = {$I:(DE-82)080010_20140620$ / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-3-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 573 -
                      Neuroimaging (POF3-573) / 571 - Connectivity and Activity
                      (POF3-571) / 572 - (Dys-)function and Plasticity (POF3-572)
                      / DFG project 233510988 - Mathematische Modellierung der
                      Entstehung und Suppression pathologischer
                      Aktivitätszustände in den Basalganglien-Kortex-Schleifen
                      (233510988)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-573 /
                      G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-572 /
                      G:(GEPRIS)233510988},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30323734},
      UT           = {WOS:000445926200001},
      doi          = {10.3389/fnins.2018.00528},
      url          = {https://juser.fz-juelich.de/record/851051},
}