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)


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.



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).


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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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