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@ARTICLE{Kraljevi:1029134,
      author       = {Kraljević, Nevena and Langner, Robert and Küppers,
                      Vincent and Raimondo, Federico and Patil, Kaustubh R. and
                      Eickhoff, Simon B. and Müller, Veronika I.},
      title        = {{N}etwork and state specificity in connectivity‐based
                      predictions of individual behavior},
      journal      = {Human brain mapping},
      volume       = {45},
      number       = {8},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2024-04990},
      pages        = {e26753},
      year         = {2024},
      abstract     = {Predicting individual behavior from brain functional
                      connectivity (FC) patterns can contribute to our
                      understanding of human brain functioning. This may apply in
                      particular if predictions are based on features derived from
                      circumscribed, a priori defined functional networks, which
                      improves interpretability. Furthermore, some evidence
                      suggests that task-based FC data may yield more successful
                      predictions of behavior than resting-state FC data. Here, we
                      comprehensively examined to what extent the correspondence
                      of functional network priors and task states with behavioral
                      target domains influences the predictability of individual
                      performance in cognitive, social, and affective tasks. To
                      this end, we used data from the Human Connectome Project for
                      large-scale out-of-sample predictions of individual
                      abilities in working memory (WM), theory-of-mind cognition
                      (SOCIAL), and emotion processing (EMO) from FC of
                      corresponding and non-corresponding states
                      (WM/SOCIAL/EMO/resting-state) and networks
                      (WM/SOCIAL/EMO/whole-brain connectome). Using root mean
                      squared error and coefficient of determination to evaluate
                      model fit revealed that predictive performance was rather
                      poor overall. Predictions from whole-brain FC were slightly
                      better than those from FC in task-specific networks, and a
                      slight benefit of predictions based on FC from task versus
                      resting state was observed for performance in the WM domain.
                      Beyond that, we did not find any significant effects of a
                      correspondence of network, task state, and performance
                      domains. Together, these results suggest that multivariate
                      FC patterns during both task and resting states contain
                      rather little information on individual performance levels,
                      calling for a reconsideration of how the brain mediates
                      individual differences in mental abilities.Keywords:
                      brain‐based prediction; brain–behavior relationships;
                      fMRI; functional connectivity; interindividual differences;
                      machine learning.},
      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       = {38864353},
      UT           = {WOS:001243999500001},
      doi          = {10.1002/hbm.26753},
      url          = {https://juser.fz-juelich.de/record/1029134},
}