Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019

Rebsamen, Michael; Rummel, Christian; Mürner-Lavanchy, Ines; Reyes, M; Wiest, Roland; McKinley, Richard (2019). Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019. In: Pohl, Kilian M.; Thompson, Wesley K.; Adeli, Ehsan; Linguraru, Marius George (eds.) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture notes in computer science: Vol. 11791 (pp. 26-34). Cham, Switzerland: Springer

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Brain morphometry derived from structural magnetic resonance imaging is a widely used quantitative biomarker in neuroimaging studies. In this paper, we investigate its usefulness for the Neurocognitive Prediction Challenge 2019. An in-depth analysis of the features provided by the challenge (anatomical segmentation and volumes for regions of interest according to the SRI24 atlas) motivated us to process the native T1-weighted images with FreeSurfer 6.0, to derive reliable brain morphometry including surface based metrics. A combination of subcortical volumes and cortical thicknesses, curvatures, and surface areas was used as features for a support-vector regressor (SVR) to predict pre-residualized fluid intelligence scores. Results performing only slightly better than the baseline (uniformly predicting the mean) were observed on two internally held-out validation sets, while performance on the official validation set was approximately the same as the baseline. Despite a large dataset of a specific cohort available for training, this suggests that structural brain morphometry alone has limited power for this challenge, at least with today’s imaging and post-processing methods.

Item Type:

Book Section (Book Chapter)

Division/Institute:

04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy > Research Division

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Rebsamen, Michael Andreas; Rummel, Christian; Mürner-Lavanchy, Ines; Wiest, Roland and McKinley, Richard

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0302-9743

ISBN:

978-3-030-31901-4

Series:

Lecture notes in computer science

Publisher:

Springer

Language:

English

Submitter:

Chantal Michel

Date Deposited:

14 Nov 2019 14:23

Last Modified:

17 Dec 2019 13:43

URI:

https://boris.unibe.ch/id/eprint/134875

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