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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Merrifield, P.R., Cattell, R.B.: Abilities: their structure, growth, and action (1975). https://doi.org/10.2307/1162752
Colom, R., et al.: Gray matter correlates of fluid, crystallized, and spatial intelligence: testing the P-FIT model (2009). https://doi.org/10.1016/j.intell.2008.07.007
Chamorro-Premuzic, T., Furnham, A.: Personality, intelligence and approaches to learning as predictors of academic performance (2008). https://doi.org/10.1016/j.paid.2008.01.003
Akshoomoff, N., et al.: VIII. NIH Toolbox Cognition Battery (Cb): Composite Scores of Crystallized, Fluid, and Overall Cognition (2013). https://doi.org/10.1111/mono.12038
Horn, J.L., Cattell, R.B.: Age differences in fluid and crystallized intelligence (1967). https://doi.org/10.1016/0001-6918(67)90011-x
Kievit, R.A., Davis, S.W., Griffiths, J., Correia, M.M., Cam-Can, Henson, R.N.: A watershed model of individual differences in fluid intelligence. Neuropsychologia 91, 186–198 (2016)
Fry, A.F., Hale, S.: Relationships among processing speed, working memory, and fluid intelligence in children. Biol. Psychol. 54, 1–34 (2000)
Finn, E.S., et al.: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015)
Paul, E.J., et al.: Dissociable brain biomarkers of fluid intelligence. Neuroimage 137, 201–211 (2016)
Cole, J.H., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker (2017). https://doi.org/10.1016/j.neuroimage.2017.07.059
Yang, C., Rangarajan, A., Ranka, S.: Visual explanations from deep 3D convolutional neural networks for alzheimer’s disease classification. In: AMIA Annual Symposium Proceedings, pp. 1571–1580 (2018)
Shi, J., Zheng, X., Li, Y., Zhang, Q., Ying, S.: Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer’s Disease. IEEE J Biomed Health Inform. 22, 173–183 (2018)
Shansky, R.M., Woolley, C.S.: Considering sex as a biological variable will be valuable for neuroscience research. J. Neurosci. 36, 11817–11822 (2016)
Sowell, E.R., Trauner, D.A., Gamst, A., Jernigan, T.L.: Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study. Dev. Med. Child Neurol. 44, 4–16 (2002)
Giedd, J.N., Rapoport, J.L.: Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron 67, 728–734 (2010)
Pfefferbaum, A., et al.: Altered brain developmental trajectories in adolescents after initiating drinking. Am. J. Psychiatry 175, 370–380 (2018)
Hagler, D.J., et al.: Image processing and analysis methods for the adolescent brain cognitive development study (2018). https://www.biorxiv.org/content/early/2018/11/04/457739
Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31, 798–819 (2010)
Burgaleta, M., et al.: Subcortical regional morphology correlates with fluid and spatial intelligence. Hum. Brain Mapp. 35, 1957–1968 (2014)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Saladin, K.S.: Anatomy & Physiology: The Unity of Form and Function. McGraw-Hill Science, Engineering & Mathematics, New York (2007)
VanElzakker, M., Fevurly, R.D., Breindel, T., Spencer, R.L.: Environmental novelty is associated with a selective increase in Fos expression in the output elements of the hippocampal formation and the perirhinal cortex. Learn. Mem. 15, 899–908 (2008)
Duarte, I.C., Ferreira, C., Marques, J., Castelo-Branco, M.: Anterior/posterior competitive deactivation/activation dichotomy in the human hippocampus as revealed by a 3D navigation task. PLoS ONE 9, e86213 (2014)
Maguire, E.A., et al.: Navigation-related structural change in the hippocampi of taxi drivers. Proc. Natl. Acad. Sci. U.S.A. 97, 4398–4403 (2000)
Cattell, R.B.: Abilities: Their Structure, Growth, and Action. Houghton Mifflin Harcourt (HMH), Boston (1971)
Raven, J.C., Court, J.H.: Manual for Raven’s progressive matrices and vocabulary scales: advanced progressive matrices (1998)
Acknowledgements
The authors would like to thank the Challenge Organizers and ABCD Study Researchers for the opportunity to participate and utilize their data. We also thank Kevin Flores, Erica Rutter, and John Nardini for many helpful discussions. Further, we acknowledge the following funding sources: James S. McDonnell Foundation, U54CA210180, U54CA193489, 3U54CA193489-04S3, and U01CA220378.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ranjbar, S. et al. (2019). Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs. 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_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-31901-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31900-7
Online ISBN: 978-3-030-31901-4
eBook Packages: Computer ScienceComputer Science (R0)