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@INPROCEEDINGS{Friedrich:1025200,
      author       = {Friedrich, Michel and Stoffels, Gabriele and Filss,
                      Christian and Lohmann, Philipp and Mottaghy, Felix and
                      Lucas, Carolin Weiss and Ruge, Maximilian and Shah, Nadim
                      and Caspers, Svenja and Langen, Karl-Josef and Fink, Gereon
                      and Galldiks, Norbert and Kocher, Martin},
      title        = {{NCOG}-04. {WHOLE}-{BRAIN} {STRUCTURAL} {CONNECTIVITY}
                      {PREDICTS} {COGNITIVE} {DEFICITS} {IN} {PRETREATED}
                      {PATIENTS} {WITH} {CNS} {WHO} {GRADE} 3 {OR} 4 {GLIOMAS}},
      issn         = {1523-5866},
      reportid     = {FZJ-2024-02768},
      year         = {2023},
      abstract     = {BACKGROUNDGlioma patients frequently suffer from cognitive
                      dysfunction potentially caused by tumor invasion or
                      treatment effects. We hypothesized that cognitive
                      functioning in pretreated glioma patients critically depends
                      on the maintained structural connectivity of multiple brain
                      networks. PATIENTS ANDMETHODSThe study included 121
                      pretreated glioma patients (median age, 52 years; median
                      ECOG score, 1; CNS WHO grade, 3 or 4) who had biopsy or
                      resection plus chemoradiation as first-line therapy.
                      Cognitive performance was assessed by ten tests in five main
                      cognitive domains after a median time of 14 months (range,
                      1-214 months) after treatment initiation. Hybrid amino acid
                      PET/MRI using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine,
                      a network-based cortical parcellation, and advanced
                      tractography tools were used to generate whole-brain fiber
                      count-weighted connectivity matrices. These matrices were
                      applied to a machine learning-based model to identify
                      predictive fiber connections, essential cortical nodes, and
                      the networks underlying cognitive performance in the
                      evaluated domains.RESULTSCompared to healthy controls
                      (n=121), the cognitive scores were significantly lower in
                      nine cognitive tests. For each test, a subset of connections
                      between nodes (median number, 254; range, 32-542) was
                      identified whose fiber count sum was related to the actual
                      scores in a linear model (median R2, 0.37; range,
                      0.16-0.44). Leave-one-out cross-validation confirmed the
                      model's generalizability in 7 of 10 tests (median
                      correlation coefficient for predicted vs. observed scores,
                      0.47; range, 0.39-0.57). Critically involved cortical
                      regions (≥ 10 adjacent predictive edges) included
                      predominantly left-sided cortical nodes of the visual,
                      somatomotor, dorsal/ventral attention, and default mode
                      networks. Highly critical nodes (≥ 15-20 edges) included
                      the default-mode network’s left temporal and bilateral
                      posterior cingulate cortex.CONCLUSIONThese results suggest
                      that the cognitive performance of pretreated glioma patients
                      is strongly related to structural connectivity between
                      multiple brain networks and depends on the integrity of
                      known network hubs also involved in other neurological
                      disorders.},
      month         = {Nov},
      date          = {2023-11-16},
      organization  = {Society for Neuro-Oncology’s 28th
                       Annual Scientific Meeting and Education
                       Day, Vancouver (Canada), 16 Nov 2023 -
                       19 Nov 2023},
      cin          = {INM-1 / INM-4 / INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-4-20090406 /
                      I:(DE-Juel1)INM-3-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.1093/neuonc/noad179.0817},
      url          = {https://juser.fz-juelich.de/record/1025200},
}