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@ARTICLE{Chen:1006999,
      author       = {Chen, Jianzhong and Ooi, Leon Qi Rong and Tan, Trevor Wei
                      Kiat and Zhang, Shaoshi and Li, Jingwei and Asplund,
                      Christopher L. and Eickhoff, Simon B and Bzdok, Danilo and
                      Holmes, Avram J and Yeo, B. T. Thomas},
      title        = {{R}elationship {B}etween {P}rediction {A}ccuracy and
                      {F}eature {I}mportance {R}eliability: an {E}mpirical and
                      {T}heoretical {S}tudy},
      journal      = {NeuroImage},
      volume       = {274},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-01938},
      pages        = {120115 -},
      year         = {2023},
      abstract     = {There is significant interest in using neuroimaging data to
                      predict behavior. The predictive models are often
                      interpreted by the computation of feature importance, which
                      quantifies the predictive relevance of an imaging feature.
                      Tian and Zalesky (2021) suggest that feature importance
                      estimates exhibit low split-half reliability, as well as a
                      trade-off between prediction accuracy and feature importance
                      reliability across parcellation resolutions. However, it is
                      unclear whether the trade-off between prediction accuracy
                      and feature importance reliability is universal. Here, we
                      demonstrate that, with a sufficient sample size, feature
                      importance (operationalized as Haufe-transformed weights)
                      can achieve fair to excellent split-half reliability. With a
                      sample size of 2600 participants, Haufe-transformed weights
                      achieve average intra-class correlation coefficients of
                      0.75, 0.57 and 0.53 for cognitive, personality and mental
                      health measures respectively. Haufe-transformed weights are
                      much more reliable than original regression weights and
                      univariate FC-behavior correlations. Original regression
                      weights are not reliable even with 2600 participants.
                      Intriguingly, feature importance reliability is strongly
                      positively correlated with prediction accuracy across
                      phenotypes. Within a particular behavioral domain, there is
                      no clear relationship between prediction performance and
                      feature importance reliability across regression models.
                      Furthermore, we show mathematically that feature importance
                      reliability is necessary, but not sufficient, for low
                      feature importance error. In the case of linear models,
                      lower feature importance error is mathematically related to
                      lower prediction error. Therefore, higher feature importance
                      reliability might yield lower feature importance error and
                      higher prediction accuracy. Finally, we discuss how our
                      theoretical results relate with the reliability of imaging
                      features and behavioral measures. Overall, the current study
                      provides empirical and theoretical insights into the
                      relationship between prediction accuracy and feature
                      importance reliability.},
      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       = {37088322},
      UT           = {WOS:001005138000001},
      doi          = {10.1016/j.neuroimage.2023.120115},
      url          = {https://juser.fz-juelich.de/record/1006999},
}