Abstract
Fluid intelligence (FI) indicates a set of general abilities like pattern recognition, abstract thinking, and problem-solving. FI is related to inherent, biological factors. We present a method to predict the fluid intelligence score in children (9–10 y/o) from their structural brain scans. For the purposes of this work, we used features derived from the T1-weighted Magnetic Resonance scans from the ABCD study. We used data from 3739 subjects for training and 415 for validation of the model. As features we used the volumes of gray matter regions of interest provided by the challenge organizers, as well as three additional groups of features. These include signal intensity features based on the ROIs, as well as shape-based features derived from the anterior and posterior cross sectional area of the corpus callosum. We used the random forest regressor model for prediction. We compare its performance to other regression-based models (XGBoost Regression and Support Vector Regression). Additionally, we ran a mean decrease accuracy (MDA) algorithm to select features that had high influence on the prediction results. The results we have obtained for the validation set were as follows: MSE = 67.39, R-squared = 0.0762. The proposed method showed promising results and has the potential to provide a good prediction of fluid intelligence based on structural brain scans.
Majority of the work was done at the BrainHack Warsaw hackathon.
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References
Cattell, R.B.: Abilities: Their Structure, Growth, and Action. Houghton Mifflin, Oxford (1971)
Conway, A.R.A., Kovacs, K.: New and emerging models of human intelligence. Wiley Interdisc. Rev.: Cognitive Sci. 6(5), 419–426 (2015)
Kaya, F., Stough, L.M., Juntune, J.: Verbal and nonverbal intelligence scores within the context of poverty. Gifted Educ. Int. 33(3), 257–272 (2016)
Rindermann, H., Flores-Mendoza, C., Mansur-Alves, M.: Reciprocal effects between fluid and crystallized intelligence and their dependence on parents’ socioeconomic status and education. Learn. Individ. Differ. 20(5), 544–548 (2010)
Gottfredson, L.S.: Hans Eysenck’s theory of intelligence, and what it reveals about him. Personality Individ. Differ. 103, 116–127 (2016)
Plomin, R., Stumm, S.V.: The new genetics of intelligence. Nat. Rev. Genet. 19, 148–159 (2018)
Ritchie, S.J., et al.: Beyond a bigger brain: multivariable structural brain imaging and intelligence. Intelligence 51, 47–56 (2015)
Cole, M.W., Yarkoni, T., Repovs, G., Anticevic, A., Braver, T.S.: Global connectivity of prefrontal cortex predicts cognitive control and intelligence. J. Neurosci. 32(26), 8988–8999 (2012)
Choi, Y.Y., et al.: Multiple bases of human intelligence revealed by cortical thickness and neural activation. J. Neurosci. 28(41), 10323–10329 (2008)
Gray, J.R., Chabris, C.F., Braver, T.S.: Neural mechanisms of general fluid intelligence. Nat. Neurosci. 6(3), 316 (2003)
Akshoomoff, N., et al.: NIH toolbox cognition battery (cb): Composite scores of crystallized, fluid, and overall cognition. Monogr. Soc. Res. Child Dev. 78(4), 119–132 (2013)
McDaniel, M.A.: Big-brained people are smarter: a meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence 33, 337–346 (2005)
Luders, E., Thompson, P.M., Narr, K.L., Zamanyan, A., Chou, Y.Y., Gutman, B., Dinov, I.D., Toga, A.W.: The link between callosal thickness and intelligence in healthy children and adolescents. Neuroimage 54(3), 1823–30 (2011)
Westerhausen, R., et al.: The corpus callosum as anatomical marker of intelligence? A critical examination in a large-scale developmental study. Brain Struct Funct. 223(1), 285–296 (2018)
Haász, J., Westlye, E.T., Fjær, S., Espeseth, T., Lundervold, A., Lundervold, A.J.: General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults. Neuroimage 83, 372–383 (2013)
ABCD study website abcdstudy.org. Accessed 4 Apr 2019
Data Supplement of Pfefferbaum et al.: Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am. J. Psychiatry 175(4), 370–380 (2018)
Glasser, M.F., Van Essen, D.C.: Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 31(32), 11597–11616 (2011)
Ganzetti, M., Wenderoth, N., Mantini, D.: Whole brain myelin mapping using T1- and T2-weighted MR imaging data. Front Hum Neurosci. 8, 671 (2014)
Koenig, S.H.: Cholesterol of myelin is the determinant of gray-white contrast in MRI of brain. Magn. Reson. Med. 20(2), 285–291 (1991)
Sigalovsky, I.S., Fischl, B., Melcher, J.R.: Mapping an intrinsic MR property of gray matter in auditory cortex of living humans: a possible marker for primary cortex and hemispheric differences. Neuroimage 32(4), 1524–37 (2006)
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62, 782–90 (2012)
Zhang, X., Yang, Y.H., Han, Z., Wang, H., Gao, C.: Object class detection: a survey. ACM Comput. Surv. (CSUR) 46(1) (2013)
Automatic Registration Toolbox. https://www.nitrc.org/projects/art/. Accessed 4 Apr 2019
Ardekani, B.A., Figarsky, K., Sidtis, J.J.: Sexual dimorphism in the human corpus callosum: an MRI study using the OASIS brain database. Cereb. Cortex 23(10), 2514–2520 (2012)
Han, H., Guo, X., Yu, H.: Variable selection using mean decrease accuracy and mean decrease gini based on random forest. In: 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 219–224. IEEE (2016)
Website of the challenge. https://sibis.sri.com/abcd-np-challenge/. Accessed 4 Apr 2019
Cole, J.H., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 163, 115–124 (2017)
Abdollahi, R.O., et al.: Correspondences between retinotopic areas and myelin maps in human visual cortex. Neuroimage 99, 509–524 (2014)
Ganzetti, M., Wenderoth, N., Mantini, D.: Whole brain myelin mapping using T1-and T2-weighted MR imaging data. Frontiers Hum. Neurosci. 8, 671 (2014)
Burgaleta, M., et al.: Subcortical regional morphology correlates with fluid and spatial intelligence. Hum. Brain Mapp. 35(5), 1957–1968 (2014)
Bernal, J., et al.: Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif. Intell. Med. 95, 64–81 (2018)
Acknowledgement
The authors would like to thank Professor Mark Jenkinson for his helpful comments regarding the manuscript.
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Wlaszczyk, A., Kaminska, A., Pietraszek, A., Dabrowski, J., Pawlak, M.A., Nowicka, H. (2019). Predicting Fluid Intelligence from Structural MRI Using Random Forest regression. 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_10
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