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@ARTICLE{Plschke:887694,
      author       = {Pläschke, Rachel N. and Patil, Kaustubh R. and Cieslik,
                      Edna C. and Nostro, Alessandra D. and Varikuti, Deepthi P.
                      and Plachti, Anna and Lösche, Patrick and Hoffstaedter,
                      Felix and Kalenscher, Tobias and Langner, Robert and
                      Eickhoff, Simon B.},
      title        = {{A}ge differences in predicting working memory performance
                      from network-based functional connectivity},
      journal      = {Cortex},
      volume       = {132},
      issn         = {0010-9452},
      address      = {New York, NY},
      publisher    = {Elsevier},
      reportid     = {FZJ-2020-04355},
      pages        = {441 - 459},
      year         = {2020},
      note         = {This study was supported by the Deutsche
                      Forschungsgemeinschaft (DFG), contract grantnumbers: EI
                      816/4-1, LA 3071/3-1; the National Institute of Mental
                      Health, contract grantnumber: R01-MH074457; the Helmholtz
                      Association Theme “Supercomputing and Modelingfor the
                      Human Brain”; and the European Union’s Horizon 2020
                      Research and InnovationProgramme, contract grant number:
                      7202070 (HBP SGA1).},
      abstract     = {Deterioration in working memory capacity (WMC) has been
                      associated with normal aging, but it remains unknown how age
                      affects the relationship between WMC and connectivity within
                      functional brain networks. We therefore examined the
                      predictability of WMC from fMRI-based resting-state
                      functional connectivity (RSFC) within eight
                      meta-analytically defined functional brain networks and the
                      connectome in young and old adults using relevance vector
                      machine in a robust cross-validation scheme. Particular
                      brain networks have been associated with mental functions
                      linked to WMC to a varying degree and are associated with
                      age-related differences in performance. Comparing prediction
                      performance between the young and old sample revealed
                      age-specific effects: In young adults, we found a general
                      unpredictability of WMC from RSFC in networks subserving WM,
                      cognitive action control, vigilant attention, theory-of-mind
                      cognition, and semantic memory, whereas in older adults each
                      network significantly predicted WMC. Moreover, both
                      WM-related and WM-unrelated networks were differently
                      predictive in older adults with low versus high WMC. These
                      results indicate that the within-network functional coupling
                      during task-free states is specifically related to
                      individual task performance in advanced age, suggesting
                      neural-level reorganization. In particular, our findings
                      support the notion of a decreased segregation of functional
                      brain networks, deterioration of network integrity within
                      different networks and/or compensation by reorganization as
                      factors driving associations between individual WMC and
                      within-network RSFC in older adults. Thus, using
                      multivariate pattern regression provided novel insights into
                      age-related brain reorganization by linking cognitive
                      capacity to brain network integrity.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain Project
                      Specific Grant Agreement 1 (720270)},
      pid          = {G:(DE-HGF)POF3-572 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)720270},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33065515},
      UT           = {WOS:000588059000032},
      doi          = {10.1016/j.cortex.2020.08.012},
      url          = {https://juser.fz-juelich.de/record/887694},
}