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.
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