Data-driven modeling of spatiotemporal dynamics using multimodal imaging data
- 1Department of Mathematics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- 2Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- 3Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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-β, 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. Author summary: Alzheimer’s disease is a complex brain disorder that develops slowly over many years. Changes in the brain can begin long before memory and thinking problems become noticeable, but it remains difficult to predict how quickly the disease will progress in a particular person. In this study, we developed a computer-based approach that creates a personalized “digital twin” of Alzheimer’s disease progression. The approach combines information from brain scans collected repeatedly over time with mathematical models of how disease-related changes develop and spread through the brain. We tested the framework using data from nearly 1,900 people participating in the Alzheimer’s Disease Neuroimaging Initiative. Our model predicted future changes in key disease indicators and cognitive function, outperforming several commonly used prediction approaches. It also identified differences in how individuals progress and highlighted brain regions that may play important roles in this process. This work provides a foundation for more personalized prediction of Alzheimer’s disease progression and could ultimately help researchers improve clinical trials and develop more targeted treatments.
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BibTeX
@article{li2026datadriven,
title = {{Data-driven modeling of spatiotemporal dynamics using multimodal imaging data}},
author = {Chunyan Li and Yutong Mao and Xiao Liu and Wenrui Hao},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014751},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014751},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Data-driven modeling of spatiotemporal dynamics using multimodal imaging data
AU - Chunyan Li
AU - Yutong Mao
AU - Xiao Liu
AU - Wenrui Hao
PY - 2026
DA - 2026/09/18
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014751
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014751
AB - 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-β, 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. Author summary: Alzheimer’s disease is a complex brain disorder that develops slowly over many years. Changes in the brain can begin long before memory and thinking problems become noticeable, but it remains difficult to predict how quickly the disease will progress in a particular person. In this study, we developed a computer-based approach that creates a personalized “digital twin” of Alzheimer’s disease progression. The approach combines information from brain scans collected repeatedly over time with mathematical models of how disease-related changes develop and spread through the brain. We tested the framework using data from nearly 1,900 people participating in the Alzheimer’s Disease Neuroimaging Initiative. Our model predicted future changes in key disease indicators and cognitive function, outperforming several commonly used prediction approaches. It also identified differences in how individuals progress and highlighted brain regions that may play important roles in this process. This work provides a foundation for more personalized prediction of Alzheimer’s disease progression and could ultimately help researchers improve clinical trials and develop more targeted treatments.
SN - 1553-7358
ER -