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@ARTICLE{Han:910707,
      author       = {Han, Hongfang and Li, Xuan and Gan, John Q. and Yu, Hua and
                      Wang, Haixian},
      title        = {{B}iomarkers {D}erived from {A}lterations in {O}verlapping
                      {C}ommunity {S}tructure of {R}esting-state {B}rain
                      {F}unctional {N}etworks for {D}etecting {A}lzheimer’s
                      {D}isease},
      journal      = {Neuroscience},
      volume       = {484},
      issn         = {0306-4522},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-04078},
      pages        = {38 - 52},
      year         = {2022},
      abstract     = {Recent studies show that overlapping community structure is
                      an important feature of the brain functional network.
                      However, alterations in such overlapping community structure
                      in Alzheimer’s disease (AD) patients have not been
                      examined yet. In this study, we investigate the overlapping
                      community structure in AD by using resting-state functional
                      magnetic resonance imaging (rs-fMRI) data. The collective
                      sparse symmetric non-negative matrix factorization (cssNMF)
                      is adopted to detect the overlapping community structure.
                      Experimental results on 28 AD patients and 32 normal
                      controls (NCs) from the ADNI2 dataset show that the two
                      groups have remarkable differences in terms of the optimal
                      number of communities, the hierarchy of communities detected
                      at different scales, network functional segregation, and
                      nodal functional diversity. In particular, the
                      frontal-parietal and basal ganglia networks exhibit
                      significant differences between the two groups. A machine
                      learning framework proposed in this paper for AD detection
                      achieved an accuracy of $76.7\%$ when using the detected
                      community strengths of the frontal-parietal and basal
                      ganglia networks only as input features. These findings
                      provide novel insights into the understanding of
                      pathological changes in the brain functional network
                      organization of AD and show the potential of the community
                      structure-related features for AD detection.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34973385},
      UT           = {WOS:000789605100005},
      doi          = {10.1016/j.neuroscience.2021.12.031},
      url          = {https://juser.fz-juelich.de/record/910707},
}