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@ARTICLE{Krmer:911669,
      author       = {Krämer, Camilla and Stumme, Johanna and da Costa Campos,
                      Lucas and Rubbert, Christian and Caspers, Julian and
                      Caspers, Svenja and Jockwitz, Christiane},
      title        = {{C}lassification and prediction of cognitive performance
                      differences in older age based on brain network patterns
                      using a machine learning approach},
      journal      = {Network neuroscience},
      volume       = {7},
      number       = {1},
      issn         = {2472-1751},
      address      = {Cambridge, MA},
      publisher    = {The MIT Press},
      reportid     = {FZJ-2022-04924},
      pages        = {122–147},
      year         = {2023},
      abstract     = {Age-related cognitive decline varies greatly in healthy
                      older adults, which may partly be explained by differences
                      in the functional architecture of brain networks.
                      Resting-state functional connectivity (RSFC) derived network
                      parameters as widely used markers describing this
                      architecture have even been successfully used to support
                      diagnosis of neurodegenerative diseases. The current study
                      aimed at examining whether these parameters may also be
                      useful in classifying and predicting cognitive performance
                      differences in the normally aging brain by using machine
                      learning (ML). Classifiability and predictability of global
                      and domain-specific cognitive performance differences from
                      nodal and network-level RSFC strength measures were examined
                      in healthy older adults from the 1000BRAINS study (age
                      range: 55–85 years). ML performance was systematically
                      evaluated across different analytic choices in a robust
                      cross-validation scheme. Across these analyses,
                      classification performance did not exceed $60\%$ accuracy
                      for global and domain-specific cognition. Prediction
                      performance was equally low with high mean absolute errors
                      (MAEs ≥ 0.75) and low to none explained variance (R2 ≤
                      0.07) for different cognitive targets, feature sets, and
                      pipeline configurations. Current results highlight limited
                      potential of functional network parameters to serve as sole
                      biomarker for cognitive aging and emphasize that predicting
                      cognition from functional network patterns may be
                      challenging.},
      cin          = {INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / JL SMHB - Joint Lab Supercomputing
                      and Modeling for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(EU-Grant)945539 / G:(DE-Juel1)JL
                      SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37339286},
      UT           = {WOS:001444932400007},
      doi          = {10.1162/netn_a_00275},
      url          = {https://juser.fz-juelich.de/record/911669},
}