AMPNet: Attention as Message Passing for Graph Neural Networks

Graph Neural Networks (GNNs) have emerged as a powerful representation learning framework for graph-structured data. A key limitation of conventional GNNs is their representation of each node with a singular feature vector, potentially overlooking intricate details about individual node features. Here, we propose an Attention-based Message-Passing layer for GNNs (AMPNet) that encodes individual features per node and models feature-level interactions through cross-node attention during message-passing steps. We demonstrate the abilities of AMPNet through extensive benchmarking on real-world biological systems such as fMRI brain activity recordings and spatial genomic data, improving over existing baselines by 20% on fMRI signal reconstruction, and further improving another 8% with positional embedding added. Finally, we validate the ability of AMPNet to uncover meaningful feature-level interactions through case studies on biological systems. We anticipate that our architecture will be highly applicable to graph-structured data where node entities encompass rich feature-level information.Comment: 16 pages (12 + 4 pages appendix). 5 figures and 7 table

A survey-wide association study to identify youth-specific correlates of major depressive episodes.

BACKGROUND:Major depressive disorder is a common disease with high mortality and morbidity worldwide. Though peak onset is during late adolescence, the prevalence of major depressive disorder remains high throughout adulthood. Leveraging an association study design, this study screened a large number of variables in the 2017 National Survey on Drug Use and Health to characterize differences between adult and youth depression across a wide array of phenotypic measurements. METHODS:All dichotomous variables were manually identified from the survey for association screening. Association between each dichotomous variable and past-year major depressive episode (MDE) occurrence was calculated as an odds ratio for adults (≥18 years) and youth (12-17 years), and tested for significance with Fischer's exact test. Logarithm of the calculated odds ratios were plotted and fitted to a linear model to assess correlation between adult and youth risk factors. RESULTS:Many of the screened variables showed similar association between past-year depressive episode occurrence in youth and adults; Lin's concordance correlation coefficient between adult and youth associations was 0.91 (95% CI 0.89-0.92). Differentially associated variables were identified, tracking: female sex, alcohol use, cigarette use, marijuana use, Medicaid/CHIP coverage, cognitive changes due to a mental, physical or emotional condition, and respondents' identification of a single depressive event as the worst experienced. CONCLUSIONS:While some youth-specific correlates of major depressive episodes were identified through screening, including some novel associations, most examined variables showed similar association with youth and adult depression. Further study of results is warranted, especially concerning the finding of increased association between marijuana use and depressive episodes in youth