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Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry
[chapter]
2019
Lecture Notes in Computer Science
We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain structures, the FNN uses cortical WM/GM contrast and cortical thickness at 78 cortical regions. These last two measurements were derived from the T1-weighted MR images using cortical surfaces produced by the CIVET pipeline. The age and gender of the subjects and the scanner manufacturer are also used as
doi:10.1007/978-3-030-31901-4_7
fatcat:z3mtbs2rurfwrm3vkhjcjhlr5a