Sex-specific topological structure associated with dementia via latent space estimation

We investigate sex-specific topological structure associated with typical Alzheimer's disease (AD) dementia using a novel state-of-the-art latent space estimation technique. This study applies a probabilistic approach for latent space estimation that extends current multiplex network modeling approaches and captures the higher-order dependence in functional connectomes by preserving transitivity and modularity structures. We find sex differences in network topology with females showing more default mode network (DMN)-centered hyperactivity whereas males showing more limbic system (LS)-centered hyperactivity while both show DMN-centered hypoactivity. We find that centrality plays an important role in dementia-related dysfunction with stronger association between connectivity changes and regional centrality in females than in males. The study contributes to the current literature by providing a more comprehensive picture of dementia-related neurodegeneration linking centrality, network segregation and DMN-centered changes in functional connectomes, and how these components of neurodegeneration differ between the sexes.