UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology

Oxtoby, NP; Ferreira, FS; Mihalik, A; Wu, T; Brudfors, M; Lin, H; Rau, A; ... Mourão-Miranda, J; + view all (2019) ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology. In: Pohl, K and Thompson, W and Adeli, E and Linguraru, M, (eds.) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science, vol 11791. (pp. pp. 114-123). Springer: Cham. Green open access

[thumbnail of 1905.10834v1.pdf]
Preview
Text
1905.10834v1.pdf - Accepted Version

Download (2MB) | Preview

Abstract

We predicted fluid intelligence from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.

Type: Proceedings paper
Title: ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology
Event: Adolescent Brain Cognitive Development Neurocognitive Prediction - 2019
ISBN-13: 9783030319007
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-31901-4_14
Publisher version: https://doi.org/10.1007/978-3-030-31901-4_14
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Support Vector Regression, Fluid Intelligence, MRI, Structural Covariance Networks · Graph theory features
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10085010
Downloads since deposit
77Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item