Abstract
In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. The proposed StackNet achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub (https://github.com/UCSB-VRL/ABCD-MICCAI2019).
Keywords
- T1-weighted MRI
- Fluid intelligence (Gf)
- Machine learning
- StackNet
This is a preview of subscription content, access via your institution.
Buying options
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Garavan, H., et al.: Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16–22 (2018)
Geurts, P., et al.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Hagler, D.J., et al.: Image processing and analysis methods for the adolescent brain cognitive development study. bioRxiv (2018). https://doi.org/10.1101/457739
Jaeggi, S.M., et al.: Improving fluid intelligence with training on working memory. Proc. Natl. Acad. Sci. 105(19), 6829–6833 (2008)
Luciana, M., et al.: Adolescent neurocognitive development and impacts of substance use: overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Dev. Cogn. Neurosci. 32, 67–79 (2018)
MacKay, D.J.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)
Michailidis, M.: StackNet, meta modelling framework (2017). https://github.com/kaz-Anova/StackNet
Minka, T.P.: Automatic choice of dimensionality for PCA. In: Advances in Neural Information Processing Systems, pp. 598–604 (2001)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge (2012)
Paul, E.J., et al.: Dissociable brain biomarkers of fluid intelligence. NeuroImage 137, 201–211 (2016)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pfefferbaum, A., et al.: Altered brain developmental trajectories in adolescents after initiating drinking. Am. J. Psychiatry 175(4), 370–380 (2017)
Rohlfing, T., et al.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2010)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 61(3), 611–622 (1999)
Volkow, N.D., et al.: The conception of the ABCD study: from substance use to a broad nih collaboration. Dev. Cogn. Neurosci. 32, 4–7 (2018)
Wang, L., et al.: MRI-based intelligence quotient (IQ) estimation with sparse learning. PloS one 10(3), e0117295 (2015)
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
Acknowledgement
This research was partially supported by a National Institutes of Health (NIH) award # 5R01NS103774-02.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kao, PY., Zhang, A., Goebel, M., Chen, J.W., Manjunath, B.S. (2019). Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNet. 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_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-31901-4_2
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)