The Experts below are selected from a list of 6391542 Experts worldwide ranked by ideXlab platform
Aris Kanbe - One of the best experts on this subject based on the ideXlab platform.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract: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.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
arXiv: Neurons and Cognition, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract:We predicted residual fluid intelligence scores 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.
Neil P Oxtoby - One of the best experts on this subject based on the ideXlab platform.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract: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.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
arXiv: Neurons and Cognition, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract:We predicted residual fluid intelligence scores 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.
Maria Del Mar Estarellas Garcia - One of the best experts on this subject based on the ideXlab platform.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract: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.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
arXiv: Neurons and Cognition, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract:We predicted residual fluid intelligence scores 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.
M Tariq - One of the best experts on this subject based on the ideXlab platform.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract: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.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
arXiv: Neurons and Cognition, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract:We predicted residual fluid intelligence scores 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.
Maria Robu - One of the best experts on this subject based on the ideXlab platform.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract: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.
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abcd neurocognitive prediction challenge 2019 predicting individual residual fluid intelligence scores from cortical grey matter morphology
arXiv: Neurons and Cognition, 2019Co-Authors: Neil P Oxtoby, Fabio S Ferreira, Agosto Mihalik, Mikael Udfors, Anita Rau, Stefano Lumberg, Maria Robu, M Tariq, Maria Del Mar Estarellas Garcia, Aris KanbeAbstract:We predicted residual fluid intelligence scores 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.
