Combining multiple connectomes improves predictive modeling of phenotypic measures
Abstract
Resting-state and task-based functional connectivity matrices, or connectomes, are powerful predictors of individual differences in phenotypic measures. However, most of the current state-of-the-art algorithms only build predictive models based on a single connectome for each individual. This approach neglects the complementary information contained in connectomes from different sources and reduces prediction performance. In order to combine...
Paper Details
Title
Combining multiple connectomes improves predictive modeling of phenotypic measures
Published Date
Nov 1, 2019
Journal
Volume
201
Pages
116038 - 116038
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