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  ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

Oxtoby, N. P., Ferreira, F. S., Mihalik, A., Wu, T., Brudfors, M., Lin, H., et al. (2019). ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology. In K. M. Pohl, W. K. Thompson, E. Adeli, & M. G. Linguraru (Eds.), Adolescent brain cognitive development neurocognitive prediction (pp. 114-123). Cham: Springer.

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https://doi.org/10.1007/978-3-030-31901-4_14 (Publisher version)
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 Creators:
Oxtoby, Neil P., Author
Ferreira, Fabio S.1, Author           
Mihalik, Agoston1, Author           
Wu, Tong1, Author           
Brudfors, Mikael, Author
Lin, Hongxiang, Author
Rau, Anita, Author
Blumberg, Stefano B., Author
Robu, Maria, Author
Zor, Cemre1, Author           
Tariq, Maira, Author
Garcia, Mar Estarellas, Author
Kanber, Baris, Author
Nikitichev, Daniil I., Author
Mourão-Miranda, Janaina1, 2, Author           
Affiliations:
1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society, ou_2205641              
2External Organizations, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2019
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-030-31901-4_14
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Title: Adolescent brain cognitive development neurocognitive prediction
Source Genre: Proceedings
 Creator(s):
Pohl, Kilian M., Editor
Thompson, Wesley K., Editor
Adeli, Ehsan, Editor
Linguraru, Marius George, Editor
Affiliations:
-
Publ. Info: Cham : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 114 - 123 Identifier: -

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 11791 Sequence Number: - Start / End Page: - Identifier: -