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 different task connectomes into a single predictive model in a principled way, we propose a novel prediction framework, termed multidimensional connectome-based predictive modeling. Two specific algorithms are developed and implemented under this framework. Using two large open-source datasets with multiple tasks-the Human Connectome Project and the Ph

SUBMITTER: Gao S 

PROVIDER: S-EPMC6765422 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature