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@ARTICLE{Yeung:907986,
      author       = {Yeung, Andy Wai Kan and More, Shammi and Wu, Jianxiao and
                      Eickhoff, Simon},
      title        = {{R}eporting details of neuroimaging studies on individual
                      traits prediction: {A} literature survey},
      journal      = {NeuroImage},
      volume       = {256},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2022-02310},
      pages        = {119275 -},
      year         = {2022},
      abstract     = {Using machine-learning tools to predict individual
                      phenotypes from neuroimaging data is one of the most
                      promising and hence dynamic fields in systems neuroscience.
                      Here, we perform a literature survey of the rapidly work on
                      phenotype prediction in healthy subjects or general
                      population to sketch out the current state and ongoing
                      developments in terms of data, analysis methods and
                      reporting. Excluding papers on age-prediction and clinical
                      applications, which form a distinct literature, we
                      identified a total 108 papers published since 2007. In
                      these, memory, fluid intelligence and attention were most
                      common phenotypes to be predicted, which resonates with the
                      observation that roughly a quarter of the papers used data
                      from the Human Connectome Project, even though another half
                      recruited their own cohort. Sample size (in terms of
                      training and external test sets) and prediction accuracy
                      (from internal and external validation respectively) did not
                      show significant temporal trends. Prediction accuracy was
                      negatively correlated with sample size of the training set,
                      but not the external test set. While known to be optimistic,
                      leave-one-out cross-validation (LOO CV) was the prevalent
                      strategy for model validation (n = 48). Meanwhile, 27
                      studies used external validation with external test set.
                      Both numbers showed no significant temporal trends. The most
                      popular learning algorithm was connectome-based predictive
                      modeling introduced by the Yale team. Other common learning
                      algorithms were linear regression, relevance vector
                      regression (RVR), support vector regression (SVR), least
                      absolute shrinkage and selection operator (LASSO), and
                      elastic net. Meanwhile, the amount of data from
                      self-recruiting studies (but not studies using open, shared
                      dataset) was positively correlated with internal validation
                      prediction accuracy. At the same time, self-recruiting
                      studies also reported a significantly higher internal
                      validation prediction accuracy than those using open, shared
                      datasets. Data type and participant age did not
                      significantly influence prediction accuracy. Confound
                      control also did not influence prediction accuracy after
                      adjusted for other factors. To conclude, most of the current
                      literature is probably quite optimistic with internal
                      validation using LOO CV. More efforts should be made to
                      encourage the use of external validation with external test
                      sets to further improve generalizability of the
                      models.Keywords: Individual trait; Neuroimaging; Prediction;
                      Predictive modeling; Survey.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / DFG
                      project 432015680 - Automatisierte Gehirnalterung-Vorhersage
                      und deren Interpretation},
      pid          = {G:(DE-HGF)POF4-5252 / G:(GEPRIS)432015680},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35513295},
      UT           = {WOS:000830858700007},
      doi          = {10.1016/j.neuroimage.2022.119275},
      url          = {https://juser.fz-juelich.de/record/907986},
}