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@INPROCEEDINGS{PaasOliveros:1014932,
      author       = {Paas Oliveros, Lya Katarina and Jung, Kyesam and Hu, Dan
                      and Müller, Veronika and Heckner, Marisa and Pläschke,
                      Rachel and Eickhoff, Simon and Liu, Hesheng and Langner,
                      Robert},
      title        = {{P}redicting dual-task performance from individualized
                      functional and structural netwo},
      reportid     = {FZJ-2023-03487},
      year         = {2023},
      note         = {Acknowledgments: This study was supported by the Deutsche
                      Forschungsgemeinschaft (DFG, LA 3071/3-1, SFB 1451), the
                      National Institute of Mental Health (R01-MH074457), the
                      Helmholtz Portfolio Theme Supercomputing and Modeling for
                      the Human Brain, and the European Union’s Horizon 2020
                      Research and Innovation Program under Grant Agreement No.
                      945539 (HBP SGA3), and a German Academic Exchange Service
                      (DAAD) Scholarship.},
      abstract     = {IntroductionDual-tasking, the ability to perform two tasks
                      simultaneously or in close succession, has been associated
                      with increased fronto-parietal activity [1,2]. Furthermore,
                      functional and structural connectivity (FC and SC) display
                      unique features shown to be relevant to cognition and
                      performance; however, prediction studies show overall low
                      accuracies [3]. Accounting for inter-individual variability
                      in macroscopic brain organization may be informative for
                      elucidating such brain-behavior associations at the
                      individual level [4,5]. Here, we aimed to predict dual-task
                      performance from FC and SC in individualized task-specific
                      and whole-brain (WB) networks.MethodsWe obtained 92
                      individualized discrete and homologous functional regions
                      (Fig. 1A) from 71 healthy adults (33 females; 41 young and
                      30 older) by implementing an iterative cortical parcellation
                      approach based on functional surface-level connectivity
                      patterns [4,5] in combined resting-state and task-based fMRI
                      data. For each participant, Pearson correlations were
                      computed between the individualized parcels' time series to
                      derive FC estimates. The individualized parcels were
                      projected onto processed diffusion-weighted images and
                      normalized between-parcel streamline counts were calculated
                      to derive individualized SC estimates. Besides deriving WB
                      networks, connectomes were additionally obtained from
                      task-related parcels. These were defined through a
                      group-level general linear model of brain activity elicited
                      in a dual-task paradigm with spatially incongruent manual
                      responses to auditory stimuli [6,7]. The incongruent
                      dual-task condition recruited a large fronto-parietal
                      network [2], which overlapped with 25 out of 92 functional
                      regions (Fig. 1B). For comparison, we also computed FC and
                      SC between non-individualized parcels for both WB and
                      task-specific networks. To predict subject-level reaction
                      time in dual-tasking with incongruent responses (615.21 ±
                      118.38 ms), we implemented CPM, PLS, KRR and RFR as
                      incorporated in JuLearn [8] with a 5-times, 5-fold
                      cross-validation (CV) approach based on eight separate
                      feature spaces (i.e., FC vs. SC × task-specific vs. WB
                      networks × indiv. vs. non-indiv. networks). Prediction
                      performance was evaluated by the mean absolute percentage
                      error (MAPE), averaged across all models for all folds and
                      repetitions of the CV test set.ResultsThe best prediction of
                      dual-task performance was achieved by the individualized
                      task-specific and WB structural networks (MAPE = 15.13 ±
                      2.32 $\%,$ and MAPE = 15.34 ± $2.27\%,$ respectively), as
                      well as the individualized WB functional network (MAPE =
                      15.45 ± 3.18 $\%).$ This was followed by the
                      non-individualized WB functional network (MAPE = 15.73 ±
                      3.15 $\%).$ Predictability was lowest for the individualized
                      task-specific functional network (MAPE = 16.73 ±
                      3.00).ConclusionsWhile most brain-behavior prediction
                      studies use group-level brain atlases, our findings indicate
                      a slight improvement in prediction accuracy when accounting
                      for inter-individual variability in the brain's functional
                      organization, despite the overall low predictability and
                      small differences between feature spaces. Our results for
                      predicting dual-task performance align with an improvement
                      in predicting fluid intelligence from individualized
                      functional networks [4]. Counter to expectation, WB
                      functional networks outperformed the dual-task-specific
                      ones, which could be related to the relevance of global
                      brain organizational properties in brain-behavior
                      associations [9,10], whose individual nuances are better
                      captured by the WB functional connectome. Finally, we
                      suggest a replication study with a larger sample and the
                      better predictability from SC (vs. FC) calls for further
                      research integrating multi-modal brain features to explore
                      their unique and combined contribution to cognition [3].},
      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)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-03487},
      url          = {https://juser.fz-juelich.de/record/1014932},
}