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Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets

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

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

Abstract

We present a novel framework using 3D convolutional neural networks to predict residualized fluid intelligence scores in the MICCAI 2019 Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge datasets. Using gray matter segmentations from T1-weighted MRI volumes as inputs, our framework identified several cortical and subcortical brain regions where the predicted errors were lower than random guessing in the validation set (mean squared error = 71.5252), and our final outcomes (mean squared error = 70.5787 in the validation set, 92.7407 in the test set) were comprised of the median scores predicted from these regions.

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Acknowledgement

We gratefully acknowledge Prof. Michael Zhu for facilitating centralized data storage, and Institute of Inflammation, Immunology and Infectious Disease for granting access to cluster computing resources provided by Information Technology at Purdue, West Lafayette, Indiana.

The data used in this report came from the ABCD Study Collection 3104 (https://nda.nih.gov/edit_collection.html?id=3104, accessed on or before March 24, 2019). Data access was in compliance with the NDA Data Use Certification and approved by the Institutional Review Board at Purdue University. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award number U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025.

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Authors and Affiliations

  1. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA

    Yukai Zou, Jinxia Yao & Joseph V. Rispoli

  2. College of Veterinary Medicine, Purdue University, West Lafayette, USA

    Yukai Zou

  3. School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA

    Ikbeom Jang & Joseph V. Rispoli

  4. Department of Statistics, Purdue University, West Lafayette, USA

    Timothy G. Reese & Wenbin Zhu

  5. Institute of Inflammation, Immunology and Infectious Disease, Purdue University, West Lafayette, USA

    Joseph V. Rispoli

Authors
  1. Yukai Zou
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  2. Ikbeom Jang
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  3. Timothy G. Reese
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  4. Jinxia Yao
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  5. Wenbin Zhu
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  6. Joseph V. Rispoli
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Corresponding author

Correspondence to Yukai Zou .

Editor information

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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Zou, Y., Jang, I., Reese, T.G., Yao, J., Zhu, W., Rispoli, J.V. (2019). Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets. 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_21

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  • DOI: https://doi.org/10.1007/978-3-030-31901-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31900-7

  • Online ISBN: 978-3-030-31901-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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