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@ARTICLE{Hoenig:842338,
      author       = {Hoenig, Merle C and Bischof, Gerard Nisal and Seemiller,
                      Joseph and Hammes, Jochen and Kukolja, Juraj and Onur,
                      Özgür and Jessen, Frank and Fliessbach, Klaus and
                      Neumaier, Bernd and Fink, Gereon Rudolf and van Eimeren,
                      Thilo and Drzezga, Alexander},
      title        = {{N}etworks of tau distribution in {A}lzheimer’s disease},
      journal      = {Brain},
      volume       = {141},
      number       = {2},
      issn         = {1460-2156},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2018-00580},
      pages        = {568–581},
      year         = {2018},
      abstract     = {See Whitwell (doi:10.1093/brain/awy001) for a scientific
                      commentary on this article.A stereotypical anatomical
                      propagation of tau pathology has been described in
                      Alzheimer’s disease. According to recent concepts (network
                      degeneration hypothesis), this propagation is thought to be
                      indicative of misfolded tau proteins possibly spreading
                      along functional networks. If true, tau pathology
                      accumulation should correlate in functionally connected
                      brain regions. Therefore, we examined whether independent
                      components could be identified in the distribution pattern
                      of in vivo tau pathology and whether these components
                      correspond with specific functional connectivity networks.
                      Twenty-two 18F-AV-1451 PET scans of patients with amnestic
                      Alzheimer’s disease (mean age = 66.00 ± 7.22 years, 14
                      males/eight females) were spatially normalized, intensity
                      standardized to the cerebellum, and z-transformed using the
                      mean and deviation image of a healthy control sample to
                      assess Alzheimer’s disease-related tau pathology. First,
                      to detect distinct tau pathology networks, the deviation
                      maps were subjected to an independent component analysis.
                      Second, to investigate if regions of high tau burden are
                      associated with functional connectivity networks, we
                      extracted the region with the maximum z-value in each of the
                      generated tau pathology networks and used them as seeds in a
                      subsequent resting-state functional MRI analysis, conducted
                      in a group of healthy adults (n = 26) who were part of the
                      1000 Functional Connectomes Project. Third, to examine if
                      tau pathology co-localizes with functional connectivity
                      networks, we quantified the spatial overlap between the
                      seed-based networks and the corresponding tau pathology
                      network by calculating the Dice similarity coefficient.
                      Additionally, we assessed if the tau-dependent seed-based
                      networks correspond with known functional resting-state
                      networks. Finally, we examined the relevance of the
                      identified components in regard to the neuropathological
                      Braak stages. We identified 10 independently coherent tau
                      pathology networks with the majority showing a symmetrical
                      bi-hemispheric expansion and coinciding with highly
                      functionally connected brain regions such as the precuneus
                      and cingulate cortex. A fair-to-moderate overlap was
                      observed between the tau pathology networks and
                      corresponding seed-based networks (Dice range: 0.13–0.57),
                      which in turn resembled known resting-state networks,
                      particularly the default mode network (Dice range:
                      0.42–0.56). Moreover, greater tau burden in the tau
                      pathology networks was associated with more advanced Braak
                      stages. Using the data-driven approach of an independent
                      component analysis, we observed a set of independently
                      coherent tau pathology networks in Alzheimer’s disease,
                      which were associated with disease progression and coincided
                      with functional networks previously reported to be impaired
                      in Alzheimer’s disease. Together, our results provide
                      novel information regarding the impact of tau pathology
                      networks on the mechanistic pathway of Alzheimer’s
                      disease.},
      cin          = {INM-3 / INM-5},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-5-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29315361},
      UT           = {WOS:000424217900028},
      doi          = {10.1093/brain/awx353},
      url          = {https://juser.fz-juelich.de/record/842338},
}