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  ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

Oxtoby, N. P., Ferreira, F. S., Mihalik, A., Wu, T., Brudfors, M., Lin, H., et al. (2019). ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology. In K. M. Pohl, W. K. Thompson, E. Adeli, & M. G. Linguraru (Eds.), Adolescent brain cognitive development neurocognitive prediction (pp. 114-123). Cham: Springer.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0005-5F44-5 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0005-C363-F
Genre: Konferenzbeitrag

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https://doi.org/10.1007/978-3-030-31901-4_14 (Verlagsversion)
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 Urheber:
Oxtoby, Neil P., Autor
Ferreira, Fabio S.1, Autor           
Mihalik, Agoston1, Autor           
Wu, Tong1, Autor           
Brudfors, Mikael, Autor
Lin, Hongxiang, Autor
Rau, Anita, Autor
Blumberg, Stefano B., Autor
Robu, Maria, Autor
Zor, Cemre1, Autor           
Tariq, Maira, Autor
Garcia, Mar Estarellas, Autor
Kanber, Baris, Autor
Nikitichev, Daniil I., Autor
Mourão-Miranda, Janaina1, 2, Autor           
Affiliations:
1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society, ou_2205641              
2External Organizations, ou_persistent22              

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 Zusammenfassung: 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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Sprache(n): eng - English
 Datum: 2019
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1007/978-3-030-31901-4_14
 Art des Abschluß: -

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Quelle 1

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Titel: Adolescent brain cognitive development neurocognitive prediction
Genre der Quelle: Konferenzband
 Urheber:
Pohl, Kilian M., Herausgeber
Thompson, Wesley K., Herausgeber
Adeli, Ehsan, Herausgeber
Linguraru, Marius George, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Cham : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 114 - 123 Identifikator: -

Quelle 2

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Titel: Lecture Notes in Computer Science
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 11791 Artikelnummer: - Start- / Endseite: - Identifikator: -