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 - Oxtoby, Neil P.
AU - Ferreira, Fabio S.
AU - Mihalik, Agoston
AU - Wu, Tong
AU - Brudfors, Mikael
AU - Lin, Hongxiang
AU - Rau, Anita
AU - Blumberg, Stefano B.
AU - Robu, Maria
AU - Zor, Cemre
AU - Tariq, Maira
AU - Garcia, Mar Estarellas
AU - Kanber, Baris
AU - Nikitichev, Daniil I.
AU - Mourão-Miranda, Janaina
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Fluid intelligence
KW - Graph theory features
KW - MRI
KW - Structural covariance networks
KW - Support vector regression
UR - https://www.scopus.com/pages/publications/85075655248
U2 - 10.1007/978-3-030-31901-4_14
DO - 10.1007/978-3-030-31901-4_14
M3 - 会议稿件
AN - SCOPUS:85075655248
SN - 9783030319007
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 114
EP - 123
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 -