Data-Driven Modeling of Spatiotemporal Dynamics Using Multimodal Imaging Data

[2511.08847] Data-Driven Modeling of Spatiotemporal Dynamics Using Multimodal Imaging Data Skip to main content Search Submit Donate Log in Quantitative Biology > Neurons and Cognition arXiv:2511.08847 (q-bio) [Submitted on 12 Nov 2025 ( v1 ), last revised 24 Aug 2026 (this version, v2)] Title: Data-Driven Modeling of Spatiotemporal Dynamics Using Multimodal Imaging Data Authors: Chunyan Li , Yutong Mao , Xiao Liu , Wenrui Hao View PDF HTML (experimental) Abstract: Understanding how biological systems evolve across space and time remains a fundamental challenge, particularly when dynamic processes vary substantially across individuals. We present a personalized graph-based dynamical modeling framework for characterizing spatiotemporal biological dynamics from longitudinal multimodal imaging data. The framework constructs individualized brain graphs from MRI and PET measurements and learns patient-specific dynamical parameters governing regional structural and molecular changes. Applied to 1,891 participants from the Alzheimer's Disease Neuroimaging Initiative, the model captures the coordinated evolution of amyloid-$\beta$, tau, neurodegeneration, and cognition and accurately predicts their future trajectories, outperforming established clinical and neuroimaging benchmarks. Patient-specific dynamical parameters reveal distinct patterns of biological progression and provide improved prediction of future cognitive decline compared with standard biomarkers. Sensitivity analysis further identifies regional network features associated with the propagation of pathological and structural changes, recovering known temporolimbic and frontal vulnerability patterns. These results demonstrate how data-driven dynamical modeling can integrate multimodal longitudinal measurements to uncover individualized spatiotemporal patterns and latent mechanisms of biological change. The framework provides a quantitative approach for studying complex biological dynamics across heterogeneous individuals and establishes a foundation for personalized modeling of progressive biological processes. Comments: Provisionally accepted for publication in PLOS Computational Biology Subjects: Neurons and Cognition (q-bio.NC) ; Dynamical Systems (math.DS) MSC classes: 92C55, 35Q92, 37N25, 65M32 ACM classes: I.6.3; J.3 Cite as: arXiv:2511.08847 [q-bio.NC] (or arXiv:2511.08847v2 [q-bio.NC] for this version) https://doi.org/10.48550/arXiv.2511.08847 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chunyan Li [ view email ] [v1] Wed, 12 Nov 2025 00:04:07 UTC (2,916 KB) [v2] Mon, 24 Aug 2026 19:38:12 UTC (1,690 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.NC < prev | next > new | recent | 2025-11 Change to browse by: math math.DS q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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