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

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Adolescent Brain Cognitive Development Neurocognitive Prediction (ABCD-NP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11791))

Abstract

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.

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Notes

  1. 1.

    https://nda.nih.gov/edit_collection.html?id=3104.

  2. 2.

    https://abcdstudy.org/images/Protocol_Imaging_Sequences.pdf.

  3. 3.

    See https://nda.nih.gov/data_structure.html?short_name=btsv01 for a full list of volumes.

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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).

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Authors and Affiliations

  1. Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig Maximilian Universität, Munich, Germany

    Sebastian Pölsterl, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy & Christian Wachinger

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  1. Sebastian Pölsterl
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  2. Benjamín Gutiérrez-Becker
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  3. Ignacio Sarasua
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  4. Abhijit Guha Roy
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  5. Christian Wachinger
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Corresponding author

Correspondence to Sebastian Pölsterl .

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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Kilian M. Pohl

  2. University of California, San Diego, La Jolla, CA, USA

    Wesley K. Thompson

  3. Stanford University, Stanford, CA, USA

    Ehsan Adeli

  4. Children’s National Health System, Washington, DC, USA

    Marius George Linguraru

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Pölsterl, S., Gutiérrez-Becker, B., Sarasua, I., Guha Roy, A., Wachinger, C. (2019). An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features. In: Pohl, K., Thompson, W., Adeli, E., Linguraru, M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science(), vol 11791. Springer, Cham. https://doi.org/10.1007/978-3-030-31901-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-31901-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31900-7

  • Online ISBN: 978-3-030-31901-4

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