% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Friedrich:1025205,
      author       = {Friedrich, M. and Stoffels, G. and Filss, C. P. and
                      Lohmann, P. and Mottaghy, F. M. and Weiss Lucas, C. and
                      Ruge, M. I. and Shah, N. J. and Caspers, S. and Langen, K.
                      J. and Fink, G. R. and Galldiks, N. and Kocher, M.},
      title        = {{P}01.02.{A} {WHOLE}-{BRAIN} {STRUCTURAL} {CONNECTIVITY}
                      {PREDICTS} {COGNITIVE} {OUTCOMES} {IN} {PRETREATED}
                      {PATIENTS} {WITH} {WHO} {GRADE} 3 {OR} 4 {GLIOMAS}},
      issn         = {1523-5866},
      reportid     = {FZJ-2024-02773},
      year         = {2023},
      abstract     = {BACKGROUNDGlioma patients frequently suffer from cognitive
                      dysfunction potentially related to brain damage caused by
                      tumor invasion or therapeutic intervention. We hypothesized
                      that, as in other neurological disorders, the long-term
                      outcome of cognitive functions in glioma patients critically
                      depends on maintaining the structural connectivity of
                      multiple interconnected networks.PATIENTS AND METHODSOne
                      hundred and twenty-one glioma patients (median age, 52
                      years; median ECOG score, 1) with histomolecularly
                      characterized CNS WHO grade 3 or 4 gliomas were investigated
                      after a median time of 14 months (range, 1-214 months)
                      following first-line therapy, including resection and
                      chemoradiation with alkylating agents. Hybrid amino acid
                      PET/MR imaging using the tracer
                      O-(2-[18F]fluoroethyl)-L-tyrosine, a brain-networks-based
                      cortical parcellation into nodes, and advanced fiber
                      tractography tools were used for constructing
                      fiber-count-weighted connectivity matrices. Cognitive
                      performance was measured by ten different tests concerning
                      various cognitive domains. The connectivity matrices and the
                      patients' test scores were applied to a machine
                      learning-based model that identified critical fiber
                      connections, cortical nodes, and involved networks for
                      predicting cognitive performance in the assessed
                      domains.RESULTSCompared to a matched cohort of healthy
                      subjects, the cognitive scores of the patients were
                      significantly lower in 9 of 10 cognitive tests. The most
                      affected domains were executive function/concept-shifting
                      $(-72\%),$ attention/processing speed (relative performance,
                      $-53\%),$ followed by semantic word fluency $(-25\%),$
                      verbal working and semantic memory (-8 and $-22\%,$
                      respectively), and visual working memory (-11 to $-20\%).$
                      For each cognitive test, a subset of connections (median
                      number, 254; range, 32-542) was identified whose fiber count
                      sum showed a correlation with 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
                      compared to observed scores, 0.47; range 0.39-0.57). The
                      distribution of critical cortical regions (node degree of
                      ≥ 5 predictive edges), found in $90\%$ of the
                      cross-validation iterations, varied considerably between
                      domains. These included predominantly left-sided cortical
                      nodes of the visual, somatomotor, dorsal/ventral attention,
                      and default mode networks. Highly critical nodes (degree of
                      15-20 predictive 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 the structural connectivity within multiple brain
                      networks and depends on the integrity of known network
                      hubs.},
      month         = {Sep},
      date          = {2023-09-21},
      organization  = {18th Meeting of the European
                       Association of Neuro-Oncology,
                       Rotterdam (Netherlands), 21 Sep 2023 -
                       24 Sep 2023},
      cin          = {INM-1 / INM-3 / INM-4},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-3-20090406 /
                      I:(DE-Juel1)INM-4-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)1},
      UT           = {WOS:001300535600077},
      doi          = {10.1093/neuonc/noad137.076},
      url          = {https://juser.fz-juelich.de/record/1025205},
}