% 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{Gell:1022060,
      author       = {Gell, Martin and Eickhoff, Simon B. and Omidvarnia, Amir
                      and Küppers, Vincent and Patil, Kaustubh R. and
                      Satterthwaite, Theodore D. and Müller, Veronika and
                      Langner, Robert},
      title        = {{T}he {B}urden of {R}eliability: {H}ow {M}easurement
                      {N}oise {L}imits {B}rain-{B}ehaviour {P}redictions},
      reportid     = {FZJ-2024-01197},
      year         = {2023},
      abstract     = {Major efforts in human neuroimaging strive to understand
                      individual differences and find biomarkers for clinical
                      applications by predicting behavioural phenotypes from brain
                      imaging data. An essential prerequisite for identifying
                      generalizable and replicable brain-behaviour prediction
                      models is sufficient measurement reliability. However, the
                      selection of prediction targets is predominantly guided by
                      scientific interest or data availability rather than
                      reliability considerations. Here we demonstrate the impact
                      of low phenotypic reliability on out-of-sample prediction
                      performance. Using simulated and empirical data from the
                      Human Connectome Projects, we found that reliability levels
                      common across many phenotypes can markedly limit the ability
                      to link brain and behaviour. Next, using 5000 subjects from
                      the UK Biobank, we show that only highly reliable data can
                      fully benefit from increasing sample sizes from hundreds to
                      thousands of participants. Overall, our findings highlight
                      the importance of measurement reliability for identifying
                      brain–behaviour associations from individual differences.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2023.02.09.527898},
      url          = {https://juser.fz-juelich.de/record/1022060},
}