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ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression

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Adolescent Brain Cognitive Development Neurocognitive Prediction (ABCD-NP 2019)

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

Abstract

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from kernel ridge regression (\(\lambda =10\)), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.

A. Mihalik, M. Brudfors, J. Mourão-Miranda and N. P. Oxtoby–These authors contributed equally to this work.

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Notes

  1. 1.

    Available from https://github.com/WCHN/segmentation-model.

References

  1. Goriounova, N.A., Mansvelder, H.D.: Genes, cells and brain areas of intelligence. Front. Hum. Neurosci. 13, 44 (2019). https://doi.org/10.3389/fnhum.2019.00044

    Article  Google Scholar 

  2. Deary, I.J., Penke, L., Johnson, W.: The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 11(3), 201–211 (2010). https://doi.org/10.1038/nrn2793

    Article  Google Scholar 

  3. McCall, R.B.: Childhood IQ’s as predictors of adult educational and occupational status. Science 197(4302), 482–483 (1977). https://doi.org/10.1126/science.197.4302.482

    Article  Google Scholar 

  4. Gottfredson, L.S.: Why g matters: the complexity of everyday life. Intelligence 24(1), 79–132 (1997). https://doi.org/10.1016/S0160-2896(97)90014-3

    Article  Google Scholar 

  5. Deary, I.J., Strand, S., Smith, P., Fernandes, C.: Intelligence and educational achievement. Intelligence 35(1), 13–21 (2007). https://doi.org/10.1016/j.intell.2006.02.001

    Article  Google Scholar 

  6. Johnson, W., McGue, M., Iacono, W.G.: Genetic and environmental influences on academic achievement trajectories during adolescence. Dev. Psychol. 42(3), 514–32 (2006). https://doi.org/10.1037/0012-1649.42.3.514

    Article  Google Scholar 

  7. Batty, G.D., Deary, I.J., Gottfredson, L.S.: Premorbid (early life) IQ and later mortality risk: systematic review. Ann. Epidemiol. 17(4), 278–288 (2007). https://doi.org/10.1016/j.annepidem.2006.07.010

    Article  Google Scholar 

  8. Batty, G.D., et al.: IQ in early adulthood and mortality by middle age. Epidemiology 20(1), 100–109 (2008). https://doi.org/10.1097/ede.0b013e31818ba076

    Article  Google Scholar 

  9. Lam, N.H., et al.: Effects of Altered Excitation-Inhibition Balance on Decision Making in a Cortical Circuit Model. bioRxiv, p. 100347 (2017). https://doi.org/10.1101/100347

  10. Deary, I.J., Pattie, A., Starr, J.M.: The stability of intelligence from age 11 to age 90 years: the lothian birth cohort of 1921. Psychol. Sci. 24(12), 2361–2368 (2013). https://doi.org/10.1177/0956797613486487

    Article  Google Scholar 

  11. Fors, S., Torssander, J., Almquist, Y.B.: Is childhood intelligence associated with coexisting disadvantages in adulthood? Evidence from a Swedish cohort study. Adv. Life Course Res. 38, 12–21 (2018). https://doi.org/10.1016/J.ALCR.2018.10.005

    Article  Google Scholar 

  12. MacLullich, A.M.J., Ferguson, K.J., Deary, I.J., Seckl, J.R., Starr, J.M., Wardlaw, J.M.: Intracranial capacity and brain volumes are associated with cognition in healthy elderly men. Neurology 59(2), 169–174 (2002). https://doi.org/10.1212/WNL.59.2.169

    Article  Google Scholar 

  13. McDaniel, M.A.: Big-brained people are smarter: a meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence 33(4), 337–346 (2005). https://doi.org/10.1016/j.intell.2004.11.005

    Article  Google Scholar 

  14. Rushton, J.P., Ankney, C.D.: Whole brain size and general mental ability: a review. Int. J. Neurosci. 119(5), 692–732 (2009). https://doi.org/10.1080/00207450802325843

    Article  Google Scholar 

  15. Andreasen, N.C., et al.: Intelligence and brain structure in normal individuals. Am. J. Psychiatry 150(1), 130–4 (1993). https://doi.org/10.1176/ajp.150.1.130

    Article  Google Scholar 

  16. Narr, K.L., et al.: Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cereb. Cortex 17(9), 2163–2171 (2007). https://doi.org/10.1093/cercor/bhl125

    Article  Google Scholar 

  17. Karama, S., et al.: Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18. NeuroImage 55(4), 1443–1453 (2011). https://doi.org/10.1016/j.neuroimage.2011.01.016

    Article  Google Scholar 

  18. Jung, R.E., Haier, R.J.: The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav. Brain Sci. 30(2), 135–154 discussion 154–187 (2007). https://doi.org/10.1017/S0140525X07001185

    Article  Google Scholar 

  19. Gläscher, J., et al.: Lesion mapping of cognitive abilities linked to intelligence. Neuron 61(5), 681–91 (2009). https://doi.org/10.1016/j.neuron.2009.01.026

    Article  Google Scholar 

  20. Woolgar, A., et al.: Fluid intelligence loss linked to restricted regions of damage within frontal and parietal cortex. Proc. Nat. Acad. Sci. 107(33), 14899–14902 (2010). https://doi.org/10.1073/pnas.1007928107

    Article  Google Scholar 

  21. Oxtoby, N.P., Alexander, D.C., for the EuroPOND consortium: Imaging plus X: multimodal models of neurodegenerative disease. Curr. Opin. Neurol. 30(4), 371–379 (2017). https://doi.org/10.1097/WCO.0000000000000460

    Article  Google Scholar 

  22. Schrouff, J., et al.: PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics 11(3), 319–37 (2013). https://doi.org/10.1007/s12021-013-9178-1

    Article  Google Scholar 

  23. Schrouff, J., Monteiro, J.M., Portugal, L., Rosa, M.J., Phillips, C., Mourão-Miranda, J.: Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models. Neuroinformatics 16(1), 117–143 (2018). https://doi.org/10.1007/s12021-017-9347-8

    Article  Google Scholar 

  24. Blumberg, S.B., Tanno, R., Kokkinos, I., Alexander, D.C.: Deeper image quality transfer: training low-memory neural networks for 3D images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 118–125. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_14

    Chapter  Google Scholar 

  25. Casey, B.J., et al.: The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018). https://doi.org/10.1016/j.dcn.2018.03.001

    Article  Google Scholar 

  26. Hagler, D.J., et al.: Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. bioRxiv, p. 457739 (2018). https://doi.org/10.1101/457739

  27. Pfefferbaum, A., et al.: Altered brain developmental trajectories in adolescents after initiating drinking. Am. J. Psychiatry 175(4), 370–380 (2018). https://doi.org/10.1176/appi.ajp.2017.17040469

    Article  Google Scholar 

  28. 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). https://doi.org/10.1002/hbm.20906

    Article  Google Scholar 

  29. Akshoomoff, N., et al.: VIII. NIH toolbox cognition battery (CB): composite scores of crystallized, fluid, and overall cognition. Monogr. Soc. Res. Child Dev. 78(4), 119–132 (2013). https://doi.org/10.1111/mono.12038

    Article  Google Scholar 

  30. Monté-Rubio, G.C., Falcón, C., Pomarol-Clotet, E., Ashburner, J.: A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods. NeuroImage 178, 753–768 (2018). https://doi.org/10.1016/j.neuroimage.2018.05.065

    Article  Google Scholar 

  31. Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38(1), 95–113 (2007). https://doi.org/10.1016/J.NEUROIMAGE.2007.07.007

    Article  Google Scholar 

  32. Blaiotta, C., Freund, P., Cardoso, M.J., Ashburner, J.: Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. NeuroImage 166, 117–134 (2018). https://doi.org/10.1016/j.neuroimage.2017.10.060

    Article  Google Scholar 

  33. Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: SimpleMKL. J. Mach. Learn. Res. 9, 2491–2521 (2008). http://www.jmlr.org/papers/v9/rakotomamonjy08a.html

    MathSciNet  MATH  Google Scholar 

  34. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  35. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006). http://www.GaussianProcess.org/gpml

    MATH  Google Scholar 

  36. Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001). http://www.jmlr.org/papers/v1/tipping01a.html

    MathSciNet  MATH  Google Scholar 

  37. Sturmfels, P., Rutherford, S., Angstadt, M., Peterson, M., Sripada, C., Wiens, J.: A domain guided CNN architecture for predicting age from structural brain images. In: Doshi-Velez, F., et al. (eds.) Proceedings of the 3rd Machine Learning for Healthcare Conference. Proceedings of Machine Learning Research, Palo Alto, California, vol. 85, pp. 295–311. PMLR (2018). http://proceedings.mlr.press/v85/sturmfels18a.html

  38. Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv e-prints arXiv:1502.02506, February 2015. Preprint

  39. Milletari, F., et al.: Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound. arXiv e-prints arXiv:1601.07014 (2016)

  40. Rao, A., Monteiro, J.M., Mourao-Miranda, J.: Alzheimer’s disease initiative: predictive modelling using neuroimaging data in the presence of confounds. NeuroImage 150, 23–49 (2017). https://doi.org/10.1016/j.neuroimage.2017.01.066

    Article  Google Scholar 

  41. Varoquaux, G.: Cross-validation failure: small sample sizes lead to large error bars. NeuroImage 180, 68–77 (2018). https://doi.org/10.1016/j.neuroimage.2017.06.061

    Article  Google Scholar 

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Acknowledgements

This study was funded by the UCL Centre for Medical Image Computing and UK EPSRC platform grant “Medical image computing for next-generation healthcare technology” (EP/M020533/1) and supported by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre. FSF is funded by a PhD scholarship awarded by Fundacao para a Ciencia e a Tecnologia (SFRH/BD/120640/2016). NPO acknowledges support from the NIHR UCLH Biomedical Research Centre and the EuroPOND project—This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 666992. AM, CZ, TW, and JM-M acknowledge funding from the Wellcome Trust under grant number WT102845/Z/13/Z.

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Authors and Affiliations

  1. Centre for Medical Image Computing (CMIC), Department of Computer Science and Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK

    Agoston Mihalik, Mikael Brudfors, Maria Robu, Fabio S. Ferreira, Hongxiang Lin, Anita Rau, Tong Wu, Stefano B. Blumberg, Maira Tariq, Mar Estarellas Garcia, Cemre Zor, Daniil I. Nikitichev, Janaina Mourão-Miranda & Neil P. Oxtoby

  2. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, Gower Street, London, WC1E 6BT, UK

    Agoston Mihalik, Fabio S. Ferreira, Tong Wu, Cemre Zor & Janaina Mourão-Miranda

  3. The Wellcome Centre for Human Neuroimaging, University College London, Gower Street, London, WC1E 6BT, UK

    Mikael Brudfors

  4. Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Gower Street, London, WC1E 6BT, UK

    Maria Robu, Anita Rau & Daniil I. Nikitichev

  5. Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, Gower Street, London, WC1E 6BT, UK

    Baris Kanber

Authors
  1. Agoston Mihalik
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  2. Mikael Brudfors
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Corresponding author

Correspondence to Neil P. Oxtoby .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Kilian M. Pohl

  2. University of California, San Diego, La Jolla, CA, USA

    Wesley K. Thompson

  3. Stanford University, Stanford, CA, USA

    Ehsan Adeli

  4. Children’s National Health System, Washington, DC, USA

    Marius George Linguraru

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Mihalik, A. et al. (2019). ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge 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_16

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  • DOI: https://doi.org/10.1007/978-3-030-31901-4_16

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