Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes

Leo Brueggeman; Tanner Koomar; Yongchao Huang; Brady Hoskins; Tien Tong; James Kent; Ethan Bahl; Charles E. Johnson; Alexander Powers; Douglas Langbehn; Jatin Vaidya; Hans Johnson; Jacob J. Michaelson

doi:10.1007/978-3-030-31901-4_15

Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes

Leo Brueggeman
, Tanner Koomar
, Yongchao Huang
, Brady Hoskins
, Tien Tong
, James Kent
, Ethan Bahl
, Charles E. Johnson
, Alexander Powers
, Douglas Langbehn
, Jatin Vaidya
, Hans Johnson
, Jacob J. Michaelson

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

4 Scopus citations

Abstract

Prediction of cognitive performance from brain structural imaging data is a challenging machine learning topic. Participating in the ABCD Neurocognitive prediction challenge (2019), we implemented several machine learning models to solve this problem. Our results show superior performance from models relying on boosted decision trees and we find benefit from using two different sets of derived brain volumetric features. Lastly, across all models, we report an increase in performance by ensembling several different model types together in a final layer.

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	124-132
Number of pages	9
ISBN (Print)	9783030319007
DOIs	https://doi.org/10.1007/978-3-030-31901-4_15
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

ABCD-NP challenge machine learning intelligence

Access to Document

10.1007/978-3-030-31901-4_15

Cite this

Brueggeman, L., Koomar, T., Huang, Y., Hoskins, B., Tong, T., Kent, J., Bahl, E., Johnson, C. E., Powers, A., Langbehn, D., Vaidya, J., Johnson, H., & Michaelson, J. J. (2019). Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. 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. 124-132). (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_15

Brueggeman, Leo ; Koomar, Tanner ; Huang, Yongchao et al. / Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. 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. 124-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes",

abstract = "Prediction of cognitive performance from brain structural imaging data is a challenging machine learning topic. Participating in the ABCD Neurocognitive prediction challenge (2019), we implemented several machine learning models to solve this problem. Our results show superior performance from models relying on boosted decision trees and we find benefit from using two different sets of derived brain volumetric features. Lastly, across all models, we report an increase in performance by ensembling several different model types together in a final layer.",

keywords = "ABCD-NP challenge machine learning intelligence",

author = "Leo Brueggeman and Tanner Koomar and Yongchao Huang and Brady Hoskins and Tien Tong and James Kent and Ethan Bahl and Johnson, \{Charles E.\} and Alexander Powers and Douglas Langbehn and Jatin Vaidya and Hans Johnson and Michaelson, \{Jacob J.\}",

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\_15",

language = "English",

isbn = "9783030319007",

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

publisher = "Springer",

pages = "124--132",

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 = "Germany",

}

Brueggeman, L, Koomar, T, Huang, Y, Hoskins, B, Tong, T, Kent, J, Bahl, E, Johnson, CE, Powers, A, Langbehn, D, Vaidya, J, Johnson, H & Michaelson, JJ 2019, Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. 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. 124-132, 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_15

Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. / Brueggeman, Leo; Koomar, Tanner; Huang, Yongchao 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. 124-132 (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 - Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes

AU - Brueggeman, Leo

AU - Koomar, Tanner

AU - Huang, Yongchao

AU - Hoskins, Brady

AU - Tong, Tien

AU - Kent, James

AU - Bahl, Ethan

AU - Johnson, Charles E.

AU - Powers, Alexander

AU - Langbehn, Douglas

AU - Vaidya, Jatin

AU - Johnson, Hans

AU - Michaelson, Jacob J.

PY - 2019

Y1 - 2019

N2 - Prediction of cognitive performance from brain structural imaging data is a challenging machine learning topic. Participating in the ABCD Neurocognitive prediction challenge (2019), we implemented several machine learning models to solve this problem. Our results show superior performance from models relying on boosted decision trees and we find benefit from using two different sets of derived brain volumetric features. Lastly, across all models, we report an increase in performance by ensembling several different model types together in a final layer.

AB - Prediction of cognitive performance from brain structural imaging data is a challenging machine learning topic. Participating in the ABCD Neurocognitive prediction challenge (2019), we implemented several machine learning models to solve this problem. Our results show superior performance from models relying on boosted decision trees and we find benefit from using two different sets of derived brain volumetric features. Lastly, across all models, we report an increase in performance by ensembling several different model types together in a final layer.

KW - ABCD-NP challenge machine learning intelligence

UR - https://www.scopus.com/pages/publications/85075645806

U2 - 10.1007/978-3-030-31901-4_15

DO - 10.1007/978-3-030-31901-4_15

M3 - Conference contribution

AN - SCOPUS:85075645806

SN - 9783030319007

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

SP - 124

EP - 132

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

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

Y2 - 13 October 2019 through 13 October 2019

ER -

Brueggeman L, Koomar T, Huang Y, Hoskins B, Tong T, Kent J et al. Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. 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. 124-132. (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_15

Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes

Abstract

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