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Found 4 papers, 0 papers with code
Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering
Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of interest (ROIs) with strong interconnections.
Learning High-Order Relationships of Brain Regions
In response to this gap, we propose a novel method named HYBRID which aims to extract MIMR high-order relationships from fMRI data.
Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling
By applying the ABC model to study brain structural connectivity stratified by sex among Alzheimer's Disease (AD) subjects and healthy controls incorporating the anatomical attributes (volume, thickness and area) on nodes, our method shows superior predictive power on out-of-sample structural connectivity and identifies meaningful sex-specific network neuromarkers for AD.
Pursuing Sources of Heterogeneity in Modeling Clustered Population
We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously.
