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Predicting fluid intelligence in adolescence from structural MRI with deep learning methods

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  • Saha, Susmita
  • Pagnozzi, Alex
  • Bradford, Dana
  • Fripp, Jurgen

Abstract

The objective of this study was to investigate the potential of unsegmented structural T1w MR images of adolescent brain for predicting uncorrected/actual fluid intelligence scores without any predefined feature extraction. We also examined whether prediction of uncorrected scores is simply a harder problem from both biological and technical point of view, than prediction of residualised scores.

Suggested Citation

  • Saha, Susmita & Pagnozzi, Alex & Bradford, Dana & Fripp, Jurgen, 2021. "Predicting fluid intelligence in adolescence from structural MRI with deep learning methods," Intelligence, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:intell:v:88:y:2021:i:c:s0160289621000520
    DOI: 10.1016/j.intell.2021.101568
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    References listed on IDEAS

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    1. Sue Ramsden & Fiona M. Richardson & Goulven Josse & Michael S. C. Thomas & Caroline Ellis & Clare Shakeshaft & Mohamed L. Seghier & Cathy J. Price, 2011. "Verbal and non-verbal intelligence changes in the teenage brain," Nature, Nature, vol. 479(7371), pages 113-116, November.
    2. Liye Wang & Chong-Yaw Wee & Heung-Il Suk & Xiaoying Tang & Dinggang Shen, 2015. "MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
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