ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology
| Autor: | Stefano B. Blumberg, M Tariq, Mikael Brudfors, Anita Rau, Agoston Mihalik, Hongxiang Lin, Neil P. Oxtoby, Janaina Mourao-Miranda, Tong Wu, Maria Robu, Maria Del Mar Estarellas Garcia, Cemre Zor, Baris Kanber, Fabio Ferreira, Daniil I. Nikitichev |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Rich-club coefficient business.industry Pattern recognition Regression analysis Residual Statistics - Applications Set (abstract data type) Support vector machine FOS: Biological sciences Quantitative Biology - Neurons and Cognition Test set Neurons and Cognition (q-bio.NC) Applications (stat.AP) Artificial intelligence Centrality business Mathematics Clustering coefficient |
| Zdroj: | Adolescent Brain Cognitive Development Neurocognitive Prediction ISBN: 9783030319007 ABCD-NP@MICCAI Adolescent brain cognitive development neurocognitive prediction Lecture Notes in Computer Science |
| DOI: | 10.1007/978-3-030-31901-4_14 |
| Popis: | 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. 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 2019 |
| Databáze: | OpenAIRE |
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