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Multi-modal brain functional connectivity (FC) data have shown greatpotential for providing insights into individual variations in behavioral andcognitive traits. The joint learning of multi-modal imaging data can utilizethe intrinsic association, and thus can boost the learning performance.Although several multi-task based learning models have already been proposed byviewing the feature learning on each modality as one task, most of them ignorethe geometric structure information inherent in the modalities, which may playan important role in extracting discriminative features. In this paper, wepropose a new manifold regularized multi-task learning model by simultaneouslyconsidering between-subject and between-modality relationships. Besidesemploying a group-sparsity regularizer to jointly select a few common featuresacross multiple tasks (modalities), we design a novel manifold regularizer topreserve the structure information both within and between modalities in ourmodel. This will make our model more adaptive for realistic data analysis. Ourmodel is then validated on the Philadelphia Neurodevelopmental Cohort dataset,where we regard our modalities as functional MRI (fMRI) data collected undertwo paradigms. Specifically, we conduct experimental studies on fMRI based FCnetwork data in two task conditions for intelligence quotient (IQ) prediction.The results demonstrate that our proposed model can not only achieve improvedprediction performance, but also yield a set of IQ-relevant biomarkers.

A Manifold Regularized Multi-Task Learning Model for IQ Prediction from Multiple fMRI Paradigms