English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Resting-state functional magnetic resonance imaging (rs-fMRI)-derivedfunctional connectivity patterns have been extensively utilized to delineateglobal functional organization of the human brain in health, development, andneuropsychiatric disorders. In this paper, we investigate how functionalconnectivity in males and females differs in an age prediction framework. Wefirst estimate functional connectivity between regions-of-interest (ROIs) usingdistance correlation instead of Pearson's correlation. Distance correlation, asa multivariate statistical method, explores spatial relations of voxel-wisetime courses within individual ROIs and measures both linear and nonlineardependence, capturing more complex information of between-ROI interactions.Then, a novel non-convex multi-task learning (NC-MTL) model is proposed tostudy age-related gender differences in functional connectivity, where ageprediction for each gender group is viewed as one task. Specifically, in theproposed NC-MTL model, we introduce a composite regularizer with a combinationof non-convex $\ell_{2,1-2}$ and $\ell_{1-2}$ regularization terms forselecting both common and task-specific features. Finally, we validate theproposed NC-MTL model along with distance correlation based functionalconnectivity on rs-fMRI of the Philadelphia Neurodevelopmental Cohort forpredicting ages of both genders. The experimental results demonstrate that theproposed NC-MTL model outperforms other competing MTL models in age prediction,as well as characterizing developmental gender differences in functionalconnectivity patterns.

Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies