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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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Genre: Conference Paper

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https://doi.org/10.1007/978-3-030-31901-4_16 (Publisher version)
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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, Janaina1, 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: 2019
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-030-31901-4_16
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Source 1

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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: 133 - 142 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: -