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Adolescent Brain Cognitive Development Neurocognitive Prediction

First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Kilian M. Pohl

    1. Stanford University, Stanford, USA

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  2. Wesley K. Thompson

    1. University of California, San Diego, La Jolla, USA

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  3. Ehsan Adeli

    1. Stanford University, Stanford, USA

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  4. Marius George Linguraru

    1. Children’s National Health System, Washington, USA

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

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: ABCD-NP 2019.

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Table of contents (21 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction

    • Yeeleng S. Vang, Yingxin Cao, Xiaohui Xie
    Pages 1-8

  3. Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNet

    • Po-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, B. S. Manjunath
    Pages 9-16

  4. Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction

    • Luke Guerdan, Peng Sun, Connor Rowland, Logan Harrison, Zhicheng Tang, Nickolas Wergeles et al.
    Pages 17-25

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

    • Michael Rebsamen, Christian Rummel, Ines Mürner-Lavanchy, Mauricio Reyes, Roland Wiest, Richard McKinley
    Pages 26-34

  6. Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images

    • Sebastian Pölsterl, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy, Christian Wachinger
    Pages 35-46

  7. Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI

    • Jose Tamez-Pena, Jorge Orozco, Patricia Sosa, Alejandro Valdes, Fahimeh Nezhadmoghadam
    Pages 47-56

  8. Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry

    • Juan Miguel Valverde, Vandad Imani, John D. Lewis, Jussi Tohka
    Pages 57-65

  9. Predict Fluid Intelligence of Adolescent Using Ensemble Learning

    • Huijing Ren, Xuelin Wang, Sheng Wang, Zhengwu Zhang
    Pages 66-73

  10. Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach

    • Shikhar Srivastava, Fabian Eitel, Kerstin Ritter
    Pages 74-82

  11. Predicting Fluid Intelligence from Structural MRI Using Random Forest regression

    • Agata Wlaszczyk, Agnieszka Kaminska, Agnieszka Pietraszek, Jakub Dabrowski, Mikolaj A. Pawlak, Hanna Nowicka
    Pages 83-91

  12. Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data

    • Yanli Zhang-James, Stephen J. Glatt, Stephen V. Faraone
    Pages 92-98

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

    • Sebastian Pölsterl, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy, Christian Wachinger
    Pages 99-107

  14. Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization

    • Lihao Liu, Lequan Yu, Shujun Wang, Pheng-Ann Heng
    Pages 108-113

  15. ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology

    • Neil P. Oxtoby, Fabio S. Ferreira, Agoston Mihalik, Tong Wu, Mikael Brudfors, Hongxiang Lin et al.
    Pages 114-123

  16. Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes

    • Leo Brueggeman, Tanner Koomar, Yongchao Huang, Brady Hoskins, Tien Tong, James Kent et al.
    Pages 124-132

  17. ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression

    • Agoston Mihalik, Mikael Brudfors, Maria Robu, Fabio S. Ferreira, Hongxiang Lin, Anita Rau et al.
    Pages 133-142

  18. Predicting Fluid Intelligence Using Anatomical Measures Within Functionally Defined Brain Networks

    • Jeffrey N. Chiang, Nicco Reggente, John Dell’Italia, Zhong Sheng Zheng, Evan S. Lutkenhoff
    Pages 143-149

  19. Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs

    • Sara Ranjbar, Kyle W. Singleton, Lee Curtin, Susan Christine Massey, Andrea Hawkins-Daarud, Pamela R. Jackson et al.
    Pages 150-157

  20. Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction

    • Marina Pominova, Anna Kuzina, Ekaterina Kondrateva, Svetlana Sushchinskaya, Evgeny Burnaev, Vyacheslav Yarkin et al.
    Pages 158-166
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About this book

This book constitutes the refereed proceedings of the First Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

29 submissions were carefully reviewed and 24 of them were accepted. Some of the 24 submissions were merged and resulted in the 21 papers that are presented in this book. The papers explore methods for predicting fluid intelligence from T1-weighed MRI of 8669 children (age 9-10 years) recruited by the Adolescent Brain Cognitive Development Study (ABCD) study; the largest long-term study of brain development and child health in the United States to date.

Editors and Affiliations

  • Stanford University, Stanford, USA

    Kilian M. Pohl, Ehsan Adeli

  • University of California, San Diego, La Jolla, USA

    Wesley K. Thompson

  • Children’s National Health System, Washington, USA

    Marius George Linguraru

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