Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity.

In contrast to most existing studies that typically characterize the developmental sex differences using analysis of variance or equivalently multiple linear regression, we present a parameter-free centralized multi-task learning method to identify sex specific and common resting state functional connectivity (RSFC) patterns underlying the brain development based on resting state functional MRI (rs-fMRI) data. Specifically, we design a novel multi-task learning model to characterize sex specific and common RSFC patterns in an age prediction framework by regarding the age prediction for males and females as separate tasks. Moreover, the importance of each task and the balance of these two patterns, respectively, are automatically learned in order to make the multi-task learning robust as well as free of tunable parameters, i.e., parameter-free for short. Our experimental results on synthetic datasets verified the effectiveness of our method with respect to prediction performance, and experimental results on rs-fMRI scans of 1041 subjects (651 males) of the Philadelphia Neurodevelopmental Cohort (PNC) showed that our method could improve the age prediction on average by 5.82% with statistical significance than the best alternative methods under comparison, in addition to characterizing the developmental sex differences in RSFC patterns.