Abstract
We study predicting fluid intelligence of 9–10 year old children from T1-weighted magnetic resonance images. We extract volume and thickness measurements from MRI scans using FreeSurfer and the SRI24 atlas. We empirically compare two predictive models: (i) an ensemble of gradient boosted trees and (ii) a linear ridge regression model. For both, a Bayesian black-box optimizer for finding the best suitable prediction model is used. To systematically analyze feature importance our model, we employ results from game theory in the form of Shapley values. Our model with gradient boosting and FreeSurfer measures ranked third place among 24 submissions to the ABCD Neurocognitive Prediction Challenge. Our results on feature importance could be used to guide future research on the neurobiological mechanisms behind fluid intelligence in children.
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See https://nda.nih.gov/data_structure.html?short_name=btsv01 for a full list of volumes.
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References
Blair, C.: How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. Behav. Brain Sci. 29, 109–125; Discussion 125–160 (2006)
Bron, E.E., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the caddementia challenge. NeuroImage 111, 562–579 (2015)
Carroll, J.B.: Human Cognitive Abilities. Cambridge University Press, Cambridge (1993)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Ferrer, E.: Fluid reasoning and the developing brain. Front. Neurosci. 3(1) (2009)
Fischl, B.: FreeSurfer. NeuroImage 62(2), 774–781 (2012)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)
Gray, J.R., Chabris, C.F., Braver, T.S.: Neural mechanisms of general fluid intelligence. Nat. Neurosci. 6(3), 316–322 (2003)
Haier, R.J., Jung, R.E., Yeo, R.A., Head, K., Alkire, M.T.: Structural brain variation and general intelligence. NeuroImage 23(1), 425–433 (2004)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)
Kozachenko, L.F., Leonenko, N.N.: Sample estimate of the entropy of a random vector. Problemy Peredachi Informatsii 23(2), 9–16 (1987)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017)
Narr, K.L., et al.: Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cereb. Cortex 17(9), 2163–2171 (2006)
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 (2018)
Pölsterl, S., Gutiérrez-Becker, B., Sarasua, I., Guha Roy, A., Wachinger, C.: An auto-ML approach for the prediction of fluid intelligence from MRI-derived features. In: Pohl, K.M., et al. (eds.) Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge), ABCD-NP 2019. LNCS, vol. 11791, pp. 99–107 (2019)
Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2010)
Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148–175 (2016)
Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307–317 (1953)
Shaw, P., et al.: Intellectual ability and cortical development in children and adolescents. Nature 440(7084), 676–679 (2006)
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25, 2951–2959 (2012)
Štrumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41(3), 647–665 (2014)
Wachinger, C., Reuter, M., Alzheimer’s Disease Neuroimaging Initiative, et al.: Domain adaptation for Alzheimer’s disease diagnostics. Neuroimage 139, 470–479 (2016)
Wright, S., Matlen, B., Baym, C., Ferrer, E., Bunge, S.: Neural correlates of fluid reasoning in children and adults. Front. Hum. Neurosci. 2, 8 (2008)
Zhang, C., Liu, C., Zhang, X., Almpanidis, G.: An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst. Appl. 82, 128–150 (2017)
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This research was partially supported by the Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B).
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Pölsterl, S., Gutiérrez-Becker, B., Sarasua, I., Guha Roy, A., Wachinger, C. (2019). Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images. 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_5
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