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, Kristin R. Swanson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fluid intelligence (Gf) has been defined as the ability to reason and solve previously unseen problems. Links to Gf have been found in magnetic resonance imaging (MRI) sequences such as functional MRI and diffusion tensor imaging. As part of the Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019, we sought to predict Gf in children aged 9–10 from T1-weighted (T1 W) MRIs. The data included atlas–aligned volumetric T1 W images, atlas–defined segmented regions, age, and sex for 3739 subjects used for training and internal validation and 415 subjects used for external validation. We trained sex-specific convolutional neural net (CNN) and random forest models to predict Gf. For the convolutional model, skull-stripped volumetric T1 W images aligned to the SRI24 brain atlas were used for training. Volumes of segmented atlas regions along with each subject’s age were used to train the random forest regressor models. Performance was measured using the mean squared error (MSE) of the predictions. Random forest models achieved lower MSEs than CNNs. Further, the external validation data had a better MSE for females than males (60.68 vs. 80.74), with a combined MSE of 70.83. Our results suggest that predictive models of Gf from volumetric T1 W MRI features alone may perform better when trained separately on male and female data. However, the performance of our models indicates that more information is necessary beyond the available data to make accurate predictions of Gf.

Original languageEnglish (US)
Title of host publicationAdolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsKilian M. Pohl, Ehsan Adeli, Wesley K. Thompson, Marius George Linguraru
PublisherSpringer
Pages150-157
Number of pages8
ISBN (Print)9783030319007
DOIs
StatePublished - Jan 1 2019
Event1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11791 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/13/19

Keywords

  • Deep learning
  • Fluid intelligence
  • Sex differences

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ranjbar, S., Singleton, K. W., Curtin, L., Massey, S. C., Hawkins-Daarud, A., Jackson, P. R., & Swanson, K. R. (2019). Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs. In K. M. Pohl, E. Adeli, W. K. Thompson, & M. G. Linguraru (Eds.), Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 150-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11791 LNCS). Springer. https://doi.org/10.1007/978-3-030-31901-4_18