Speaker
Description
The maternal brain undergoes significant anatomical change during pregnancy, yet the geometric principles governing these transformations remain poorly understood. Studying brain shape change is critical for identifying markers of maternal health, but prior work focuses on scalar volumes, masking subtle deformation. We develop a digital twin that integrates precision imaging with large datasets to map pregnancy trajectories of brain structures. For preprocessing, we conduct a quality control analysis to identify which segmentation and meshing tools provide the most accurate surface representations for the brain structures. Utilizing varifold geometry and multidimensional scaling (MDS), we create a shared latent space representing brain deformations. This merges a densely sampled longitudinal dataset including hormone metrics with a larger population study on postpartum health. Traditional linear statistical models (e.g., PCA) often fail in these paradigms because subcortical shapes exist on very high dimensional nonlinear manifolds, negating impact of Euclidean methods. To manage this, we train a multilayer perceptron to map latent MDS coordinates back to vertex positions, achieving a compact, expressive representation of 3D anatomy. Using regression models, our framework is able to predict 3D shape based on time and hormones. Our digital twin provides a foundational dynamic atlas, enabling personalized modeling to inform our current understanding of maternal brain health.
Bibliography
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year={2024},
publisher={Nature Publishing Group US New York}
}
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year={2024},
publisher={Nature Publishing Group US New York}
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