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@ARTICLE{Friedrich:1018031,
      author       = {Friedrich, Michel and Filss, Christian P. and Lohmann,
                      Philipp and Mottaghy, Felix M. and Stoffels, Gabriele and
                      Lucas, Carolin Weiss and Ruge, Maximilian I. and Jon Shah,
                      N. and Caspers, Svenja and Langen, Karl-Josef and Fink,
                      Gereon R. and Galldiks, Norbert and Kocher, Martin},
      title        = {{S}tructural connectome-based predictive modeling of
                      cognitive deficits in treated glioma patients},
      journal      = {Neuro-oncology advances},
      volume       = {6},
      number       = {1},
      issn         = {2632-2498},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {FZJ-2023-04494},
      pages        = {vdad151},
      year         = {2024},
      abstract     = {AbstractBackground. In glioma patients, tumor growth and
                      subsequent treatments are associated with various types
                      ofbrain lesions. We hypothesized that cognitive functioning
                      in these patients critically depends on the
                      maintainedstructural connectivity of multiple brain
                      networks.Methods. The study included 121 glioma patients
                      (median age, 52 years; median Eastern Cooperative
                      OncologyGroup performance score 1; CNS-WHO Grade 3 or 4)
                      after multimodal therapy. Cognitive performance was
                      assessedby 10 tests in 5 cognitive domains at a median of 14
                      months after treatment initiation. Hybrid aminoacid PET/MRI
                      using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine, a
                      network-based cortical parcellation, and
                      advancedtractography were used to generate whole-brain fiber
                      count-weighted connectivity matrices. The matrices were
                      appliedto a cross-validated machine-learning model to
                      identify predictive fiber connections (edges), critical
                      corticalregions (nodes), and the networks underlying
                      cognitive performance.Results. Compared to healthy controls
                      (n = 121), patients’ cognitive scores were significantly
                      lower in 9 cognitivetests. The models predicted the scores
                      of 7/10 tests (median correlation coefficient, 0.47; range,
                      0.39–0.57) $from0.6\%$ to $5.4\%$ of the matrix entries;
                      $84\%$ of the predictive edges were between nodes of
                      different networks. Criticallyinvolved cortical regions
                      (≥10 adjacent edges) included predominantly left-sided
                      nodes of the visual, somatomotor,dorsal/ventral attention,
                      and default mode networks. Highly critical nodes (≥15
                      edges) included the default modenetwork’s left temporal
                      and bilateral posterior cingulate cortex.Conclusions. These
                      results suggest that the cognitive performance of pretreated
                      glioma patients is strongly relatedto structural
                      connectivity between multiple brain networks and depends on
                      the integrity of known networkhubs also involved in other
                      neurological disorders.},
      cin          = {INM-3 / INM-4},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-4-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / DFG
                      project 491111487 - Open-Access-Publikationskosten / 2022 -
                      2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38196739},
      UT           = {WOS:001138535600001},
      doi          = {10.1093/noajnl/vdad151},
      url          = {https://juser.fz-juelich.de/record/1018031},
}