SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease
arXiv:2603.20452v2 Announce Type: replace Abstract: Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose substantial challenges for learning reliable temporal representations. To address this challenge, we propose SDE-HGNN, a stochastic differential equation (SDE)-driven spatio-temporal hypergraph neural network for irregular longitudinal fMRI connectome modeling. The framework first employs an SDE-based reconstruction module to recover continuous latent trajectories from irregular observations. Base
The proliferation of irregular, high-dimensional longitudinal data in medical fields, coupled with advancements in AI and SDEs, provides the necessary foundation for this new approach right now.
This development represents a significant step towards more accurate and robust AI models for medical prognostics, specifically addressing a critical data challenge that has hindered prior efforts.
Current methods for handling irregular longitudinal fMRI data are significantly enhanced, allowing for more reliable disease progression modeling in complex diseases like Alzheimer's.
- · AI healthcare researchers
- · Pharmaceutical companies
- · Medical diagnostic firms
- · Patients with neurodegenerative diseases
- · Traditional statistical modeling approaches
- · Less advanced diagnostic technologies
Improved early detection and personalized treatment strategies for Alzheimer's and other neurodegenerative diseases become more feasible.
The methodology could be generalized to other longitudinal medical data, accelerating AI's impact across diverse clinical domains.
More precise disease progression models could lead to a re-evaluation of drug trial designs and reduce the cost of clinical development.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG