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@ARTICLE{Hoffstaedter:1038499,
      author       = {Hoffstaedter, Felix and Nieto, Nicolas and Eickhoff, Simon
                      and Patil, Kaustubh},
      title        = {{T}he impact of {MRI} image quality on statistical and
                      predictive analysis on voxel based morphology},
      reportid     = {FZJ-2025-01494},
      year         = {2024},
      abstract     = {Image Quality of MRI brain scans is strongly influenced by
                      within scanner head movements and the resulting image
                      artifacts alter derived measures like brain volume and
                      cortical thickness. Automated image quality assessment is
                      key to controlling for confounding effects of poor image
                      quality. In this study, we systematically test for the
                      influence of image quality on univariate statistics and
                      machine learning classification. We analyzed group effects
                      of sex/gender on local brain volume and made predictions of
                      sex/gender using logistic regression, while correcting for
                      brain size. From three large publicly available datasets,
                      two age and sex-balanced samples were derived to test the
                      generalizability of the effect for pooled sample sizes of
                      n=760 and n=1094. Results of the Bonferroni corrected
                      t-tests over 3747 gray matter features showed a strong
                      influence of low-quality data on the ability to find
                      significant sex/gender differences for the smaller sample.
                      Increasing sample size and more so image quality showed a
                      stark increase in detecting significant effects in
                      univariate group comparisons. For the classification of
                      sex/gender using logistic regression, both increasing sample
                      size and image quality had a marginal effect on the Area
                      under the Receiver Operating Characteristic Curve for most
                      datasets and subsamples. Our results suggest a more
                      stringent quality control for univariate approaches than for
                      multivariate classification with a leaning towards higher
                      quality for classical group statistics and bigger sample
                      sizes for machine learning applications in neuroimaging.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      eBRAIN-Health - eBRAIN-Health - Actionable Multilevel Health
                      Data (101058516)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(EU-Grant)101058516},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.34734/FZJ-2025-01494},
      url          = {https://juser.fz-juelich.de/record/1038499},
}