Multi-Site rs-fMRI Domain Alignment for Autism Spectrum Disorder Auxiliary Diagnosis Based on Hyperbolic Space
Journal:
arXiv
Published Date:
Feb 8, 2025
Abstract
Increasing the volume of training data can enable the auxiliary diagnostic
algorithms for Autism Spectrum Disorder (ASD) to learn more accurate and stable
models. However, due to the significant heterogeneity and domain shift in
rs-fMRI data across different sites, the accuracy of auxiliary diagnosis
remains unsatisfactory. Moreover, there has been limited exploration of
multi-source domain adaptation models on ASD recognition, and the
interpretability of models is often poor. To address these challenges, we
proposed a domain-adaptive algorithm based on hyperbolic space embedding.
Hyperbolic space is naturally suited for representing the topology of complex
networks such as brain functional networks. Therefore, we embedded the brain
functional network into hyperbolic space and constructed the corresponding
hyperbolic space community network to effectively extract latent
representations. To address the heterogeneity of data across different sites
and the issue of domain shift, we introduce a constraint loss function,
Hyperbolic Maximum Mean Discrepancy (HMMD), to align the marginal distributions
in the hyperbolic space. Additionally, we employ class prototype alignment to
to mitigate discrepancies in conditional distributions across domains.
Experimental results demonstrated that the proposed algorithm shows superior
classification performance for ASD compared with baseline models, and is more
robust to multi-site heterogeneity.
