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@INPROCEEDINGS{Heckner:1010403,
      author       = {Heckner, Marisa and Cieslik, Edna and Paas Oliveros, Lya
                      Katarina and Eickhoff, Simon and Patil, Kaustubh and
                      Langner, Robert},
      title        = {{P}redicting {E}xecutive {F}unctioning from {B}rain
                      {N}etworks: {M}odality {S}pecificity and {A}ge {E}ffects},
      reportid     = {FZJ-2023-03043},
      year         = {2023},
      note         = {Acknowledgements: This study was supported by the Deutsche
                      Forschungsgemeinschaft (DFG, EI 816/11-1, PA 3634/1-1, EI
                      816/21-1, SPP2041, CRC), the National Institute of Mental
                      Health (R01-MH074457), the Helmholtz Portfolio Theme
                      “Supercomputing and Modeling for the Human Brain”, the
                      European Union’s Horizon 2020 Research and Innovation
                      Programme under Grant Agreement No. 720270 (HBP SGA1),
                      785907 (HBP SGA2), and 945539 (HBP SGA3).},
      abstract     = {Healthy aging is associated with structural and functional
                      brain changes, which have beenlinked to executive
                      functioning (EF) decline. Despite a known association
                      between resting-statefunctional connectivity (RSFC) and EF
                      [1], its potential for capturing individual differences inEF
                      performance has been questioned [2]. Therefore, we examined
                      to what degree individual EFabilities may be predicted from
                      different brain modalities: RSFC, grey-matter volume
                      (GMV),regional homogeneity (ReHo), and fractional amplitude
                      of low frequency fluctuations (fALFF).This was done in an
                      EF-associated (EFN), a perceptuomotor (PercMot) and a
                      whole-brainnetwork in young and old adults.We
                      meta-analytically defined an EFN [3] and a perceptuomotor
                      (linked to visual,auditory, and motor processing) network
                      [4] and used Power et al.’s [5] graph of
                      putativefunctional areas as a whole-brain control. Imaging
                      and behavioral data of 116 younger (age: 20–40 years, 64
                      females) and 111 older (age: 60–80 years, 72 females)
                      healthy adults were obtainedfrom the enhanced NKI sample
                      [6]. Targets for prediction comprised performance in
                      highlydemanding (HD) and less demanding (LD) conditions of
                      each of 3 classic EF tasks: Color-WordInterference, Trail
                      Making, and N-Back. Individual z-transformed performance
                      scores were thenpredicted from the characteristics of each
                      network’s edges (RSFC) or nodes (GMV, ReHo,fALFF) using
                      partial least-squares regression with 100 repetitions of a
                      10-fold cross-validationscheme. Prediction accuracy as
                      indicated by the root mean square error (RMSE) for the
                      100repetitions was then submitted to a 2(age group) ×
                      3(network) × 2(task demand level) ×4(modalities)
                      mixed-measures ANOVA.Overall, prediction accuracy (i.e.,
                      RMSE) was rather low to moderate. Similar toprevious studies
                      [2;7], prediction accuracy was better for older [M = .784]
                      (vs. younger [M =.794]) adults, indicating that
                      brain–behavior association strength increases with
                      advancing age.This might be due to overall age-related
                      neural decline such as atrophy or white-matterdegeneration,
                      changes that have been associated with network integrity [8]
                      and reorganization aswell as EF decline. Interestingly,
                      prediction accuracy for younger adults was better
                      whenpredicting HD [M = .757] (vs. LD [M = .831]) conditions,
                      while for older adults, predictionaccuracy was better when
                      predicting LD [M = .771] (vs. HD [M = .797]) conditions.
                      Possibly,age-related effects on the network level might
                      still be compensated for in LD by some form ofcompensation
                      such as the additional recruitment of domain-general
                      resources [9] subserved bythe networks investigated here,
                      but not in HD conditions. Prediction accuracy for younger
                      adultswas best with fALFF [M = .751] as compared to the
                      other modalities [MGMV = .804, MRSFC =.802, MReHo = .818],
                      while for older adults, the highest accuracy was achieved
                      with GMV [M =.754] as compared to the other modalities
                      [MfALFF = .783, MRSFC = .795, MReHo = .803]. GMV isstrongly
                      linked to atrophy and its pattern of decline is thought to
                      be rather consistent across olderadults [10]. Together with
                      an age-related decrease in performance, this suggests that
                      GMV is amore sensitive marker for individual EF abilities in
                      older adults. For younger adults, fALFF, ameasure of
                      functional variability, might be the more sensitive marker,
                      possibly mediated via itslink to cognitive flexibility
                      [11].Summing up, our results show modality- as well as
                      demand-level specificity. However,overall low
                      brain–behavior associations as well as the missing network
                      specificity, question thepotential of the applied single
                      metrics as markers for individual differences in EF
                      performance.Rather, our findings suggest that future
                      research may need to analyze more global properties ofthe
                      brain, possibly combining different structural and
                      functional metrics or task states as well asage-adapted
                      behavioral testing to result in sensitive predictors for
                      young and old adults,respectively.},
      month         = {Jul},
      date          = {2023-07-22},
      organization  = {Organization for Human Brain Mapping
                       (OHBM), Montreal (Canada), 22 Jul 2023
                       - 26 Jul 2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / JL
                      SMHB - Joint Lab Supercomputing and Modeling for the Human
                      Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-03043},
      url          = {https://juser.fz-juelich.de/record/1010403},
}