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@ARTICLE{Heckner:1016751,
      author       = {Heckner, Marisa K and Cieslik, Edna C and Paas Oliveros,
                      Lya K and Eickhoff, Simon B and Patil, Kaustubh R and
                      Langner, Robert},
      title        = {{P}redicting executive functioning from brain networks:
                      modality specificity and age effects},
      journal      = {Cerebral cortex},
      volume       = {33},
      number       = {22},
      issn         = {1047-3211},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2023-03737},
      pages        = {10997–11009},
      year         = {2023},
      abstract     = {Healthy aging is associated with structural and functional
                      network changes in the brain, which have been linked to
                      deterioration in executive functioning (EF), while their
                      neural implementation at the individual level remains
                      unclear. As the biomarker potential of individual
                      resting-state functional connectivity (RSFC) patterns has
                      been questioned, we investigated to what degree individual
                      EF abilities can be predicted from the gray-matter volume
                      (GMV), regional homogeneity, fractional amplitude of
                      low-frequency fluctuations (fALFF), and RSFC within
                      EF-related, perceptuo-motor, and whole-brain networks in
                      young and old adults. We examined whether the differences in
                      out-of-sample prediction accuracy were modality-specific and
                      depended on age or task-demand levels. Both uni- and
                      multivariate analysis frameworks revealed overall low
                      prediction accuracies and moderate-to-weak brain–behavior
                      associations (R2 < 0.07, r < 0.28), further challenging
                      the idea of finding meaningful markers for individual EF
                      performance with the metrics used. Regional GMV, well linked
                      to overall atrophy, carried the strongest information about
                      individual EF differences in older adults, whereas fALFF,
                      measuring functional variability, did so for younger adults.
                      Our study calls for future research analyzing more global
                      properties of the brain, different task-states and applying
                      adaptive behavioral testing to result in sensitive
                      predictors for young and older adults, respectively.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37782935},
      UT           = {WOS:001187539200001},
      doi          = {10.1093/cercor/bhad338},
      url          = {https://juser.fz-juelich.de/record/1016751},
}