发表机构
Technical University of Munich (TUM); TUM Klinikum; Munich Center for Machine Learning (MCML); Munich Data Science Institute (MDSI); Beijing Tiantan Hospital; Capital Medical University（慕尼黑工业大学; 慕尼黑工业大学医院; 慕尼黑机器学习中心; 慕尼黑数据科学研究所; 北京天坛医院; 首都医科大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探究SCSR模型从UKB数据到中国人群数据集的迁移能力，对比四种训练策略及两种主干模型，发现微调后的SUNet模型判别性能最优，且SCSR对中国人群皮质萎缩检测稳健、跨人群迁移性强。
AI 中文摘要
随机皮质自重建（Stochastic Cortical Self-Reconstruction, SCSR）可将神经退行性疾病如阿尔茨海默病（Alzheimer's disease, AD）的特征性灰质萎缩进行个性化映射，实现高分辨率皮质表面的绘制。与通常在粗糙区域层面操作、固有地受训练期间包含的协变量约束的常规规范建模方法不同，SCSR从顶点水平观测到的皮质厚度直接估计个性化健康参考，这使得能够检测到健康皮质形状的细微、受试者特异性偏差。本研究探究最初基于英国生物样本库（UK Biobank, UKB）数据训练的SCSR向独立中国人群数据集的泛化性与可迁移性。具体而言，我们评估SCSR衍生的Z分数区分健康扫描、轻度认知障碍（mild cognitive impairment, MCI）个体和AD患者的能力，同时评估模型在整个生命周期内的鲁棒性。我们比较四种训练策略：直接应用UKB训练的模型、在中国数据上微调、从头开始训练，以及在UKB和中国队列上联合训练。作为重建主干，我们考虑多层感知机（multilayer perceptron, MLP）和球形UNet（Spherical UNet, SUNet）。我们的结果表明，SCSR在所有评估模型中都能对中国人群的皮质萎缩进行稳健检测。最高的判别性能由微调后的SUNet模型实现（平均成对AUC=0.848），紧随其后的是UKB训练的SUNet。此外，即使训练人群的年龄分布明显更窄，整个生命周期内的重建误差仍然较低，这表明具有很强的跨人群可迁移性。
英文摘要
Stochastic cortical self-reconstruction (SCSR) enables personalized mapping of gray matter atrophy, a hallmark of neurodegenerative disorders such as Alzheimer's disease (AD), onto high-resolution cortical surfaces. Unlike conventional normative modeling approaches, which typically operate at a coarse regional level and remain inherently constrained by the covariates included during training, SCSR estimates an individualized healthy reference directly from the observed cortical thickness at the vertex level. This allows the detection of subtle, subject-specific deviations from healthy cortical shape. In this work, we investigate the generalization and transferability of SCSR, originally trained on UK Biobank (UKB) data, to an independent Chinese population dataset. Specifically, we evaluate the ability of SCSR-derived Z-scores to discriminate between healthy scans, individuals with mild cognitive impairment (MCI), and patients with AD, while also assessing model robustness across the lifespan. We compare four training strategies: direct application of the UKB-trained model, fine-tuning on Chinese data, training from scratch, and joint training on UKB and Chinese cohorts. As reconstruction backbones, we consider both a multilayer perceptron (MLP) and a Spherical UNet (SUNet). Our results demonstrate that SCSR provides robust detection of cortical atrophy in the Chinese population across all evaluated models. The highest discriminative performance was achieved by the fine-tuned SUNet model (average pairwise AUC = 0.848), followed closely by the UKB-trained SUNet. Moreover, reconstruction errors remained low across the lifespan, even when the training population exhibited a substantially narrower age distribution, indicating strong cross-population transferability.
