ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid 
intelligence scores from structural MRI using probabilistic segmentation and 
Kernel ridge regression

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

doi:10.1007/978-3-030-31901-4_16

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ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and Kernel ridge regression

Mihalik, A., Brudfors, M., Robu, M., Ferreira, F. S., Lin, H., Rau, 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 K. M. Pohl, W. K. Thompson, E. Adeli, & M. G. Linguraru (Eds.), Adolescent brain cognitive development neurocognitive prediction (pp. 133-142). Cham: Springer.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0005-5F3C-F Version Permalink: https://hdl.handle.net/21.11116/0000-0005-C362-0

Genre: Conference Paper

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https://doi.org/10.1007/978-3-030-31901-4_16 (Publisher version) Open Access status unknown

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Creators:
Mihalik, Agoston¹, Author
Brudfors, Mikael, Author
Robu, Maria, Author
Ferreira, Fabio S.¹, Author
Lin, Hongxiang, Author
Rau, Anita, Author
Wu, Tong¹, Author
Blumberg, Stefano B., Author
Kanber, Baris, Author
Tariq, Maira, Author
Garcia, Mar Estarellas, Author
Zor, Cemre¹, Author
Nikitichev, Daniil I., Author
Mourão-Miranda, Janaina^{1, 2}, Author
Oxtoby, Neil P., 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 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.

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Language(s): eng - English

Dates: Date issued: 2019

Publication Status: Issued

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Identifiers: DOI: 10.1007/978-3-030-31901-4_16

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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

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Publ. Info: Cham : Springer

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 133 - 142 Identifier: -

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Title: Lecture Notes in Computer Science

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Pages: - Volume / Issue: 11791 Sequence Number: - Start / End Page: - Identifier: -