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@ARTICLE{GarciaGarcia:258088,
      author       = {Garcia Garcia, Berta and Mattern, Hendrik and Vockert,
                      Niklas and Yakupov, Renat and Schreiber, Frank and
                      Spallazzi, Marco and Perosa, Valentina and Haghikia, Aiden
                      and Speck, Oliver and Düzel, Emrah and Maass, Anne and
                      Schreiber, Stefanie},
      title        = {{V}essel distance mapping: {A} novel methodology for
                      assessing vascular-induced cognitive resilience.},
      journal      = {NeuroImage},
      volume       = {274},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {DZNE-2023-00550},
      pages        = {120094},
      year         = {2023},
      abstract     = {The association between cerebral blood supply and cognition
                      has been widely discussed in the recent literature. One
                      focus of this discussion has been the anatomical variability
                      of the circle of Willis, with morphological differences
                      being present in more than half of the general population.
                      While previous studies have attempted to classify these
                      differences and explore their contribution to hippocampal
                      blood supply and cognition, results have been controversial.
                      To disentangle these previously inconsistent findings, we
                      introduce Vessel Distance Mapping (VDM) as a novel
                      methodology for evaluating blood supply, which allows for
                      obtaining vessel pattern metrics with respect to the
                      surrounding structures, extending the previously established
                      binary classification into a continuous spectrum. To
                      accomplish this, we manually segmented hippocampal vessels
                      obtained from high-resolution 7T time-of-flight MR
                      angiographic imaging in older adults with and without
                      cerebral small vessel disease, generating vessel distance
                      maps by computing the distances of each voxel to its nearest
                      vessel. Greater values of VDM-metrics, which reflected
                      higher vessel distances, were associated with poorer
                      cognitive outcomes in subjects affected by vascular
                      pathology, while this relation was not observed in healthy
                      controls. Therefore, a mixed contribution of vessel pattern
                      and vessel density is proposed to confer cognitive
                      resilience, consistent with previous research findings. In
                      conclusion, VDM provides a novel platform, based on a
                      statistically robust and quantitative method of vascular
                      mapping, for addressing a variety of clinical research
                      questions.},
      keywords     = {Humans / Aged / Magnetic Resonance Imaging: methods /
                      Cognition / Cerebral Small Vessel Diseases: pathology /
                      Hippocampus: pathology / Cerebral small vessel disease
                      (Other) / Hippocampus (Other) / Vessel distance mapping
                      (Other) / circle of Willis (Other) / cognition (Other)},
      cin          = {AG Schreiber / AG Düzel 3 / AG Reymann ; AG Reymann / AG
                      Maaß / AG Speck},
      ddc          = {610},
      cid          = {I:(DE-2719)1310010 / I:(DE-2719)5000006 /
                      I:(DE-2719)1310005 / I:(DE-2719)1311001 /
                      I:(DE-2719)1340009},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 352 -
                      Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37028734},
      doi          = {10.1016/j.neuroimage.2023.120094},
      url          = {https://pub.dzne.de/record/258088},
}