A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms.

ABSTRACT:

Objective

Multi-modal brain functional connectivity (FC) data have shown great potential for providing insights into individual variations in behavioral and cognitive traits. The joint learning of multi-modal data can utilize intrinsic association, and thus can boost learning performance. Although several multi-task based learning models have already been proposed by viewing feature learning on each modality as one task, most of them ignore the structural information inherent across the modalities, which may play an important role in extracting discriminative features.

Methods

In this paper, we propose a new manifold regularized multi-task learning model by simultaneously considering between-subject and between-modality relationships. Specifically, the l_2,1-norm (

SUBMITTER: Xiao L 

PROVIDER: S-EPMC7883481 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature