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Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction

ABCD-NP 2019: Adolescent Brain Cognitive Development Neurocognitive Prediction pp 26–34Cite as

Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019

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

Abstract

Brain morphometry derived from structural magnetic resonance imaging is a widely used quantitative biomarker in neuroimaging studies. In this paper, we investigate its usefulness for the Neurocognitive Prediction Challenge 2019.

An in-depth analysis of the features provided by the challenge (anatomical segmentation and volumes for regions of interest according to the SRI24 atlas) motivated us to process the native T1-weighted images with FreeSurfer 6.0, to derive reliable brain morphometry including surface based metrics. A combination of subcortical volumes and cortical thicknesses, curvatures, and surface areas was used as features for a support-vector regressor (SVR) to predict pre-residualized fluid intelligence scores. Results performing only slightly better than the baseline (uniformly predicting the mean) were observed on two internally held-out validation sets, while performance on the official validation set was approximately the same as the baseline.

Despite a large dataset of a specific cohort available for training, this suggests that structural brain morphometry alone has limited power for this challenge, at least with today’s imaging and post-processing methods.

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Acknowledgements

Calculations were performed on UBELIX, the HPC cluster at the University of Bern.

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Authors and Affiliations

  1. Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland

    Michael Rebsamen, Christian Rummel, Roland Wiest & Richard McKinley

  2. Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland

    Michael Rebsamen

  3. University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland

    Ines Mürner-Lavanchy

  4. Healthcare Imaging A.I. Lab, Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland

    Mauricio Reyes

Authors
  1. Michael Rebsamen
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  2. Christian Rummel
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  3. Ines Mürner-Lavanchy
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  4. Mauricio Reyes
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  5. Roland Wiest
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  6. Richard McKinley
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Corresponding author

Correspondence to Michael Rebsamen .

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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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Rebsamen, M., Rummel, C., Mürner-Lavanchy, I., Reyes, M., Wiest, R., McKinley, R. (2019). Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019. 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_4

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  • DOI: https://doi.org/10.1007/978-3-030-31901-4_4

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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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