ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression

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

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

ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression

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

^*Corresponding author for this work

University College London

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

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.

Original language	English
Title of host publication	Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings
Editors	Kilian M. Pohl, Ehsan Adeli, Wesley K. Thompson, Marius George Linguraru
Publisher	Springer
Pages	133-142
Number of pages	10
ISBN (Print)	9783030319007
DOIs	https://doi.org/10.1007/978-3-030-31901-4_16
State	Published - 2019
Externally published	Yes
Event	1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 13 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11791 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	13/10/19 → 13/10/19

Keywords

Convolutional neural networks
Fluid intelligence
Hackathon
Kernel ridge regression
MRI
PRoNTo

Access to Document

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

Cite this

Mihalik, A., Brudfors, M., Robu, M., Ferreira, F. S., Lin, H., Rau, A., Wu, T., Blumberg, S. B., Kanber, B., Tariq, M., Garcia, M. E., Zor, C., Nikitichev, D. I., Mourão-Miranda, J., & Oxtoby, N. P. (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, E. Adeli, W. K. Thompson, & M. G. Linguraru (Eds.), Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 133-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11791 LNCS). Springer. https://doi.org/10.1007/978-3-030-31901-4_16

Mihalik, Agoston ; Brudfors, Mikael ; Robu, Maria et al. / ABCD Neurocognitive Prediction Challenge 2019 : Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression. Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings. editor / Kilian M. Pohl ; Ehsan Adeli ; Wesley K. Thompson ; Marius George Linguraru. Springer, 2019. pp. 133-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4789be83df794290bf36c44ffbfd92fb,

title = "ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression",

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 (\$\$\textbackslash{}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.",

keywords = "Convolutional neural networks, Fluid intelligence, Hackathon, Kernel ridge regression, MRI, PRoNTo",

author = "Agoston Mihalik and Mikael Brudfors and Maria Robu and Ferreira, \{Fabio S.\} and Hongxiang Lin and Anita Rau and Tong Wu and Blumberg, \{Stefano B.\} and Baris Kanber and Maira Tariq and Garcia, \{Mar Estarellas\} and Cemre Zor and Nikitichev, \{Daniil I.\} and Janaina Mour{\~a}o-Miranda and Oxtoby, \{Neil P.\}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 13-10-2019",

year = "2019",

doi = "10.1007/978-3-030-31901-4\_16",

language = "英语",

isbn = "9783030319007",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "133--142",

editor = "Pohl, \{Kilian M.\} and Ehsan Adeli and Thompson, \{Wesley K.\} and Linguraru, \{Marius George\}",

booktitle = "Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings",

address = "德国",

}

Mihalik, A, Brudfors, M, Robu, M, Ferreira, FS, Lin, H, Rau, A, Wu, T, Blumberg, SB, Kanber, B, Tariq, M, Garcia, ME, Zor, C, Nikitichev, DI, Mourão-Miranda, J & Oxtoby, NP 2019, ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression. in KM Pohl, E Adeli, WK Thompson & MG Linguraru (eds), Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11791 LNCS, Springer, pp. 133-142, 1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-31901-4_16

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 et al.
Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings. ed. / Kilian M. Pohl; Ehsan Adeli; Wesley K. Thompson; Marius George Linguraru. Springer, 2019. p. 133-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11791 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ABCD Neurocognitive Prediction Challenge 2019

T2 - 1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019

AU - Mihalik, Agoston

AU - Brudfors, Mikael

AU - Robu, Maria

AU - Ferreira, Fabio S.

AU - Lin, Hongxiang

AU - Rau, Anita

AU - Wu, Tong

AU - Blumberg, Stefano B.

AU - Kanber, Baris

AU - Tariq, Maira

AU - Garcia, Mar Estarellas

AU - Zor, Cemre

AU - Nikitichev, Daniil I.

AU - Mourão-Miranda, Janaina

AU - Oxtoby, Neil P.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Convolutional neural networks

KW - Fluid intelligence

KW - Hackathon

KW - Kernel ridge regression

KW - MRI

KW - PRoNTo

UR - https://www.scopus.com/pages/publications/85075680768

U2 - 10.1007/978-3-030-31901-4_16

DO - 10.1007/978-3-030-31901-4_16

M3 - 会议稿件

AN - SCOPUS:85075680768

SN - 9783030319007

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 142

BT - Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings

A2 - Pohl, Kilian M.

A2 - Adeli, Ehsan

A2 - Thompson, Wesley K.

A2 - Linguraru, Marius George

PB - Springer

Y2 - 13 October 2019 through 13 October 2019

ER -

Mihalik A, Brudfors M, Robu M, Ferreira FS, Lin H, Rau A et al. ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression. In Pohl KM, Adeli E, Thompson WK, Linguraru MG, editors, Adolescent Brain Cognitive Development Neurocognitive Prediction - 1st Challenge, ABCD-NP 2019, held in Conjunction with MICCAI 2019, Proceedings. Springer. 2019. p. 133-142. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-31901-4_16

ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression

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Keywords

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