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

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


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 (


  • T1-weighted MRI
  • Fluid intelligence (Gf)
  • Machine learning
  • StackNet

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  1. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    CrossRef  Google Scholar 

  2. 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)

    Google Scholar 

  3. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)

    CrossRef  MathSciNet  Google Scholar 

  4. Garavan, H., et al.: Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16–22 (2018)

    CrossRef  Google Scholar 

  5. Geurts, P., et al.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    CrossRef  Google Scholar 

  6. Hagler, D.J., et al.: Image processing and analysis methods for the adolescent brain cognitive development study. bioRxiv (2018).

  7. Jaeggi, S.M., et al.: Improving fluid intelligence with training on working memory. Proc. Natl. Acad. Sci. 105(19), 6829–6833 (2008)

    CrossRef  Google Scholar 

  8. 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)

    CrossRef  Google Scholar 

  9. MacKay, D.J.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)

    CrossRef  Google Scholar 

  10. Michailidis, M.: StackNet, meta modelling framework (2017).

  11. Minka, T.P.: Automatic choice of dimensionality for PCA. In: Advances in Neural Information Processing Systems, pp. 598–604 (2001)

    Google Scholar 

  12. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge (2012)

    MATH  Google Scholar 

  13. Paul, E.J., et al.: Dissociable brain biomarkers of fluid intelligence. NeuroImage 137, 201–211 (2016)

    CrossRef  Google Scholar 

  14. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  15. Pfefferbaum, A., et al.: Altered brain developmental trajectories in adolescents after initiating drinking. Am. J. Psychiatry 175(4), 370–380 (2017)

    CrossRef  Google Scholar 

  16. Rohlfing, T., et al.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2010)

    CrossRef  Google Scholar 

  17. Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 61(3), 611–622 (1999)

    CrossRef  MathSciNet  Google Scholar 

  18. 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)

    CrossRef  Google Scholar 

  19. Wang, L., et al.: MRI-based intelligence quotient (IQ) estimation with sparse learning. PloS one 10(3), e0117295 (2015)

    CrossRef  Google Scholar 

  20. Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)

    CrossRef  Google Scholar 

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This research was partially supported by a National Institutes of Health (NIH) award # 5R01NS103774-02.

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Correspondence to Po-Yu Kao or B. S. Manjunath .

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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.

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