Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference

  • Christian WachingerEmail author
  • Benjamin Gutierrez Becker
  • Anna Rieckmann
  • Sebastian Pölsterl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11767)


Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex machine learning models. A potential solution is to increase sample size by pooling scans from several datasets. In this work, we combine 12,207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data. First, we systematically define these biases. Second, we detect bias by experimentally showing that scans can be correctly assigned to their respective dataset with 73.3% accuracy. Finally, we propose to tell causal from confounding factors by quantifying the extent of confounding and causality in a single dataset using causal inference. We achieve this by finding the simplest graphical model in terms of Kolmogorov complexity. As Kolmogorov complexity is not directly computable, we employ the minimum description length to approximate it. We empirically show that our approach is able to estimate plausible causal relationships from real neuroimaging data.



This research was partially supported by the Bavarian State Ministry of Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B).


  1. 1.
    Alexander, L.M., Escalera, J., et al.: An open resource for transdiagnostic research in pediatric mental health and learning disorders. bioRxiv, p. 149369 (2017)Google Scholar
  2. 2.
    Buckner, R., et al.: The brain genomics superstruct project. HDN (2012)Google Scholar
  3. 3.
    Di Martino, A., Yan, C., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659–667 (2014)CrossRefGoogle Scholar
  4. 4.
    Dukart, J., Schroeter, M.L., Mueller, K.: Age correction in dementia-matching to a healthy brain. PLoS ONE 6(7), e22193 (2011)CrossRefGoogle Scholar
  5. 5.
    Ellis, K., Bush, A., Darby, D., et al.: The australian imaging, biomarkers and lifestyle (AIBL) study of aging. Int. Psychogeriatr. 21(04), 672–687 (2009)CrossRefGoogle Scholar
  6. 6.
    Fischl, B., Salat, D.H., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)CrossRefGoogle Scholar
  7. 7.
    Fortin, J.P., Cullen, N., et al.: Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167, 104–120 (2018)CrossRefGoogle Scholar
  8. 8.
    Gollub, R.L., et al.: The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics 11(3), 367–388 (2013)CrossRefGoogle Scholar
  9. 9.
    Guadalupe, T., Mathias, S.R., Theo, G., et al.: Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex. Brain Imaging Behav. 11(5), 1497–1514 (2017)CrossRefGoogle Scholar
  10. 10.
    Han, X., Fischl, B.: Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE TMI 26(4), 479–486 (2007)Google Scholar
  11. 11.
    Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008)CrossRefGoogle Scholar
  12. 12.
    Kaltenpoth, D., Vreeken, J.: We are not your real parents: telling causal from confounded by MDL. In: SIAM International Conference on Data Mining (2019)Google Scholar
  13. 13.
    Kruggel, F., Turner, J., Muftuler, L.T.: Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort. Neuroimage 49(3), 2123–2133 (2010)CrossRefGoogle Scholar
  14. 14.
    Kucukelbir, A., Tran, D., et al.: Automatic differentiation variational inference. J. Mach. Learn. Res. 18(1), 430–474 (2017)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Linn, K.A., Gaonkar, B., Doshi, J., Davatzikos, C., Shinohara, R.T.: Addressing confounding in predictive models with an application to neuroimaging. Int. J. Biostatistics 12(1), 31–44 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J. Cognitive Neurosci. 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  17. 17.
    Marek, K., et al.: The parkinson progression marker initiative (PPMI). Progress Neurobiol. 95(4), 629–635 (2011)CrossRefGoogle Scholar
  18. 18.
    Mayer, A., et al.: Functional imaging of the hemodynamic sensory gating response in schizophrenia. Hum. Brain Mapp. 34(9), 2302–2312 (2013)CrossRefGoogle Scholar
  19. 19.
    Milham, M.P., Fair, D., Mennes, M., Mostofsky, S.H., et al.: The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012)Google Scholar
  20. 20.
    Nooner, K.B., et al.: The NKI-rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front. Neurosci. 6, 152 (2012)CrossRefGoogle Scholar
  21. 21.
    Rao, A., Monteiro, J.M., Mourao-Miranda, J.: Predictive modelling using neuroimaging data in the presence of confounds. NeuroImage 150, 23–49 (2017)CrossRefGoogle Scholar
  22. 22.
    Smith, S.M., Nichols, T.E.: Statistical challenges in “big data” human neuroimaging. Neuron 97(2), 263–268 (2018)CrossRefGoogle Scholar
  23. 23.
    Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: Computer Vision and Pattern Recognition (CVPR), pp. 1521–1528 (2011)Google Scholar
  24. 24.
    Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)CrossRefGoogle Scholar
  25. 25.
    Wachinger, C., Reuter, M.: Domain adaptation for Alzheimer’s disease diagnostics. Neuroimage 139, 470–479 (2016)CrossRefGoogle Scholar
  26. 26.
    Zuo, X.N., Anderson, J.S., Bellec, P., et al.: An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1, 140049 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christian Wachinger
    • 1
    Email author
  • Benjamin Gutierrez Becker
    • 1
  • Anna Rieckmann
    • 2
  • Sebastian Pölsterl
    • 1
  1. 1.Artificial Intelligence in Medical Imaging (AI-Med), KJPLMU MünchenMunichGermany
  2. 2.Department of Radiation SciencesUmeå UniversityUmeåSweden

Personalised recommendations