基于解剖结构的表面扩散模型用于阿尔茨海默病规范建模
Anatomy-Guided Surface Diffusion Model for Alzheimer's Disease Normative Modeling
AI总结：
本文提出基于解剖结构的表面扩散模型，用于提高阿尔茨海默病规范建模的解剖对齐和个体差异检测能力。
摘要：
规范建模已成为表征神经退行性疾病异质性和个体变异的关键方法，特别是在阿尔茨海默病(AD)中。皮层规范建模的一个挑战是由于折叠模式变化导致的解剖结构不匹配。传统上，注册被用来解决这个问题，而最近许多研究则利用深度生成模型来生成解剖学对齐的样本以分析疾病进展；然而，这些模型大多应用于体积数据，这在捕捉大脑皮层复杂的形态变化时往往力不从心。作为一种替代方法，基于表面的分析已被证明在疾病建模如AD中更加敏感，但与体积数据一样，它也面临不匹配问题。为了解决这些限制，我们提出了一种新的生成规范建模框架，通过将条件扩散生成模型转移到球面非欧几里得域。此外，这种方法通过显式地对个体解剖分割进行条件化来生成正常特征图分布，以确保更好的几何对齐，这有助于减少分析中的受试者解剖变异。我们发现，我们的模型能够生成比注册参考数据更解剖学对齐的样本，并通过消融研究和规范评估实验，这些样本能够更好地测量个体差异，并在区分正常认知（CN）、轻度认知障碍（MCI）和阿尔茨海默病（AD）患者时提高敏感性。
英文摘要：
Normative modeling has emerged as a pivotal approach for characterizing heterogeneity and individual variance in neurodegenerative diseases, notably Alzheimer's disease(AD). One of the challenges of cortical normative modeling is the anatomical structure mismatch due to folding pattern variability. Traditionally, registration is applied to address this issue and recently many studies have utilized deep generative models to generate anatomically align samples for analyzing disease progression; however, these models are predominantly applied to volume-based data, which often falls short in capturing intricate morphological changes on the brain cortex. As an alternative, surface-based analysis has been proven to be more sensitive in disease modeling such as AD, yet, like volume-based data, it also suffers from the mismatch problem. To address these limitations, we proposed a novel generative normative modeling framework by transferring the conditional diffusion generative model to the spherical non-Euclidean domain. Additionally, this approach generates normal feature map distributions by explicitly conditioning on individual anatomical segmentation to ensure better geometrical alignment which helps to reduce anatomical variance between subjects in analysis. We find that our model can generate samples that are better anatomically aligned than registered reference data and through ablation study and normative assessment experiments, the samples are able to better measure individual differences from the normal distribution and increase sensitivity in differentiating cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) patients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
