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Brain structural connectivity, capturing the white matter fiber tracts amongbrain regions inferred by diffusion MRI (dMRI), provides a uniquecharacterization of brain anatomical organization. One fundamental question toaddress with structural connectivity is how to properly summarize and performstatistical inference for a group-level connectivity architecture, forinstance, under different sex groups, or disease cohorts. Existing analysescommonly summarize group-level brain connectivity by a simple entry-wise samplemean or median across individual brain connectivity matrices. However, such aheuristic approach fully ignores the associations among structural connectionsand the topological properties of brain networks. In this project, we propose alatent space-based generative network model to estimate group-level brainconnectivity. We name our method the attributes-informed brain connectivity(ABC) model, which compared with existing group-level connectivity estimations,(1) offers an interpretable latent space representation of the group-levelconnectivity, (2) incorporates the anatomical knowledge of nodes and tests itsco-varying relationship with connectivity and (3) quantifies the uncertaintyand evaluates the likelihood of the estimated group-level effects againstchance. We devise a novel Bayesian MCMC algorithm to estimate the model. Byapplying the ABC model to study brain structural connectivity stratified by sexamong Alzheimer's Disease (AD) subjects and healthy controls incorporating theanatomical attributes (volume, thickness and area) on nodes, our method showssuperior predictive power on out-of-sample structural connectivity andidentifies meaningful sex-specific network neuromarkers for AD.

Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling