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Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images

  • Sebastian PölsterlEmail author
  • Benjamín Gutiérrez-Becker
  • Ignacio Sarasua
  • Abhijit Guha Roy
  • Christian Wachinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11791)

Abstract

We study predicting fluid intelligence of 9–10 year old children from T1-weighted magnetic resonance images. We extract volume and thickness measurements from MRI scans using FreeSurfer and the SRI24 atlas. We empirically compare two predictive models: (i) an ensemble of gradient boosted trees and (ii) a linear ridge regression model. For both, a Bayesian black-box optimizer for finding the best suitable prediction model is used. To systematically analyze feature importance our model, we employ results from game theory in the form of Shapley values. Our model with gradient boosting and FreeSurfer measures ranked third place among 24 submissions to the ABCD Neurocognitive Prediction Challenge. Our results on feature importance could be used to guide future research on the neurobiological mechanisms behind fluid intelligence in children.

Notes

Acknowledgements

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

References

  1. 1.
    Blair, C.: How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. Behav. Brain Sci. 29, 109–125; Discussion 125–160 (2006)CrossRefGoogle Scholar
  2. 2.
    Bron, E.E., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the caddementia challenge. NeuroImage 111, 562–579 (2015)CrossRefGoogle Scholar
  3. 3.
    Carroll, J.B.: Human Cognitive Abilities. Cambridge University Press, Cambridge (1993)CrossRefGoogle Scholar
  4. 4.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)Google Scholar
  5. 5.
    Ferrer, E.: Fluid reasoning and the developing brain. Front. Neurosci. 3(1) (2009)Google Scholar
  6. 6.
    Fischl, B.: FreeSurfer. NeuroImage 62(2), 774–781 (2012)CrossRefGoogle Scholar
  7. 7.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gray, J.R., Chabris, C.F., Braver, T.S.: Neural mechanisms of general fluid intelligence. Nat. Neurosci. 6(3), 316–322 (2003)CrossRefGoogle Scholar
  10. 10.
    Haier, R.J., Jung, R.E., Yeo, R.A., Head, K., Alkire, M.T.: Structural brain variation and general intelligence. NeuroImage 23(1), 425–433 (2004)CrossRefGoogle Scholar
  11. 11.
    Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)CrossRefGoogle Scholar
  12. 12.
    Kozachenko, L.F., Leonenko, N.N.: Sample estimate of the entropy of a random vector. Problemy Peredachi Informatsii 23(2), 9–16 (1987)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017)Google Scholar
  14. 14.
    Narr, K.L., et al.: Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cereb. Cortex 17(9), 2163–2171 (2006)CrossRefGoogle Scholar
  15. 15.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Pfefferbaum, A., et al.: Altered brain developmental trajectories in adolescents after initiating drinking. Am. J. Psychiatry 175(4), 370–380 (2018)CrossRefGoogle Scholar
  17. 17.
    Pölsterl, S., Gutiérrez-Becker, B., Sarasua, I., Guha Roy, A., Wachinger, C.: An auto-ML approach for the prediction of fluid intelligence from MRI-derived features. In: Pohl, K.M., et al. (eds.) Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge), ABCD-NP 2019. LNCS, vol. 11791, pp. 99–107 (2019)Google Scholar
  18. 18.
    Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2010)CrossRefGoogle Scholar
  19. 19.
    Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148–175 (2016)CrossRefGoogle Scholar
  20. 20.
    Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307–317 (1953)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Shaw, P., et al.: Intellectual ability and cortical development in children and adolescents. Nature 440(7084), 676–679 (2006)CrossRefGoogle Scholar
  22. 22.
    Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25, 2951–2959 (2012)Google Scholar
  23. 23.
    Štrumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41(3), 647–665 (2014)CrossRefGoogle Scholar
  24. 24.
    Wachinger, C., Reuter, M., Alzheimer’s Disease Neuroimaging Initiative, et al.: Domain adaptation for Alzheimer’s disease diagnostics. Neuroimage 139, 470–479 (2016)CrossRefGoogle Scholar
  25. 25.
    Wright, S., Matlen, B., Baym, C., Ferrer, E., Bunge, S.: Neural correlates of fluid reasoning in children and adults. Front. Hum. Neurosci. 2, 8 (2008)Google Scholar
  26. 26.
    Zhang, C., Liu, C., Zhang, X., Almpanidis, G.: An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst. Appl. 82, 128–150 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sebastian Pölsterl
    • 1
    Email author
  • Benjamín Gutiérrez-Becker
    • 1
  • Ignacio Sarasua
    • 1
  • Abhijit Guha Roy
    • 1
  • Christian Wachinger
    • 1
  1. 1.Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent PsychiatryLudwig Maximilian UniversitätMunichGermany

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