% 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”.

@ARTICLE{Heckner:917545,
      author       = {Heckner, Marisa K and Cieslik, Edna C and Patil, Kaustubh R
                      and Gell, Martin and Eickhoff, Simon B and Hoffstaedter,
                      Felix and Langner, Robert},
      title        = {{P}redicting executive functioning from functional brain
                      connectivity: network specificity and age effects},
      journal      = {Cerebral cortex},
      volume       = {33},
      number       = {11},
      issn         = {1047-3211},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2023-00748},
      pages        = {6495–6507},
      year         = {2023},
      abstract     = {Healthy aging is associated with altered executive
                      functioning (EF). Earlier studies found age-related
                      differences in EF performance to be partially accounted for
                      by changes in resting-state functional connectivity (RSFC)
                      within brain networks associated with EF. However, it
                      remains unclear which role RSFC in EF-associated networks
                      plays as a marker for individual differences in EF
                      performance. Here, we investigated to what degree individual
                      abilities across 3 different EF tasks can be predicted from
                      RSFC within EF-related, perceptuo-motor, whole-brain, and
                      random networks separately in young and old adults.
                      Specifically, we were interested if (i) young and old adults
                      differ in predictability depending on network or EF demand
                      level (high vs. low), (ii) an EF-related network outperforms
                      EF-unspecific networks when predicting EF abilities, and
                      (iii) this pattern changes with demand level. Both our uni-
                      and multivariate analysis frameworks analyzing interactions
                      between age × demand level × networks revealed overall low
                      prediction accuracies and a general lack of specificity
                      regarding neurobiological networks for predicting EF
                      abilities. This questions the idea of finding markers for
                      individual EF performance in RSFC patterns and calls for
                      future research replicating the current approach in
                      different task states, brain modalities, different, larger
                      samples, and with more comprehensive behavioral measures.},
      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       = {36635227},
      UT           = {WOS:000912678900001},
      doi          = {10.1093/cercor/bhac520},
      url          = {https://juser.fz-juelich.de/record/917545},
}