% 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{Zhang:1038813,
      author       = {Zhang, Shufei and Jung, Kyesam and Langner, Robert and
                      Florin, Esther and Eickhoff, Simon and Popovych, Oleksandr},
      title        = {{P}redicting response speed and age from task-evoked
                      effective connectivity},
      journal      = {Network neuroscience},
      volume       = {.},
      issn         = {2472-1751},
      address      = {Cambridge, MA},
      publisher    = {The MIT Press},
      reportid     = {FZJ-2025-01650},
      pages        = {1-57},
      year         = {2025},
      abstract     = {Recent neuroimaging studies demonstrated that task-evoked
                      functional connectivity (FC) may better predict individual
                      traits than resting-state FC. However, the prediction
                      properties of task-evoked effective connectivity (EC) remain
                      unexplored. We investigated this by predicting individual
                      reaction time (RT) performance in the stimulus-response
                      compatibility task and age, using intrinsic EC (I-EC,
                      calculated at baseline) and task-modulated EC (M-EC, induced
                      by experimental conditions) with dynamic causal modeling
                      (DCM) across various data-processing conditions, including
                      different general linear model (GLM) designs, Bayesian model
                      reduction, and different cross-validation schemes and
                      prediction models. We report evident differences in
                      predicting RT and age between I-EC and M-EC, as well as
                      between event-related and block-based GLM and DCM designs.
                      M-EC outperformed both I-EC and task-evoked FC in RT
                      prediction, while all types of connectivity performed
                      similarly for age. Event-related GLM and DCM designs
                      performed better than block-based designs. Our findings
                      suggest that task-evoked I-EC and M-EC may capture different
                      phenotypic attributes, with performance influenced by data
                      processing and modeling choices, particularly the GLM-DCM
                      design. This evaluation of methods for behavior prediction
                      from brain EC may contribute to a meta-scientific
                      understanding of how data processing and modeling frameworks
                      influence neuroimaging-based predictions, offering insights
                      for improving their robustness and efficacy.Keywords: task
                      fMRI, dynamic causal modeling, analytic flexibility, machine
                      learning, brain-based prediction, stimulus-response
                      compatibility, functional connectivity},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5252 - Brain Dysfunction and Plasticity
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001489278300003},
      doi          = {10.1162/netn_a_00447},
      url          = {https://juser.fz-juelich.de/record/1038813},
}