An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features

  • 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 propose an AutoML approach for the prediction of fluid intelligence from T1-weighted magnetic resonance images. We extracted 122 features from MRI scans and employed Sequential Model-based Algorithm Configuration to search for the best prediction pipeline, including the best data pre-processing and regression model. In total, we evaluated over 2600 prediction pipelines. We studied our final model by employing results from game theory in the form of Shapley values. Results indicate that predicting fluid intelligence from volume measurements is a challenging task with many challenges. We found that our final ensemble of 50 prediction pipelines associated larger parahippocampal gyrus volumes with lower fluid intelligence, and higher pons white matter volume with higher fluid intelligence.



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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