Normative modeling estimates reference distributions of biological measures conditional on covariates, enabling centiles and clinically interpretable deviation scores to be derived. Most neuroimaging pipelines fit one model per imaging-derived phenotype (IDP), which scales well but discards multivariate dependence that may encode coordinated patterns. We propose denoising diffusion probabilistic models (DDPMs) as a unified conditional density estimator for tabular IDPs, from which univariate centiles and deviation scores are derived by sampling. We utilise two denoiser backbones: (i) a feature-wise linear modulation (FiLM) conditioned multilayer perceptron (MLP) and (ii) a tabular transformer with feature self-attention and intersample attention (SAINT), conditioning covariates through learned embeddings. We evaluate on a synthetic benchmark with heteroscedastic and multimodal age effects and on UK Biobank FreeSurfer phenotypes, scaling from dimension of 2 to 200. Our evaluation suite includes centile calibration (absolute centile error, empirical coverage, and the probability integral transform), distributional fidelity (Kolmogorov-Smirnov tests), multivariate dependence diagnostics, and nearest-neighbour memorisation analysis. For low dimensions, diffusion models deliver well-calibrated per-IDP outputs comparable to traditional baselines while jointly modeling realistic dependence structure. At higher dimensions, the transformer backbone remains substantially better calibrated than the MLP and better preserves higher-order dependence, enabling scalable joint normative models that remain compatible with standard per-IDP pipelines. These results support diffusion-based normative modeling as a practical route to calibrated multivariate deviation profiles in neuroimaging.

翻译：规范建模通过估计生物指标在协变量条件下的参考分布，从而推导出百分位数和具有临床可解释性的偏差分数。现有神经影像处理流程通常为每个影像衍生表型独立拟合模型，这种方法虽具有良好的可扩展性，但忽略了可能编码协调模式的多变量依赖关系。我们提出将去噪扩散概率模型作为表格型影像衍生表型的统一条件密度估计器，通过采样从中推导单变量百分位数和偏差分数。我们采用两种去噪主干网络：(i) 基于特征线性调制的条件多层感知机，(ii) 具备特征自注意力与样本间注意力的表格Transformer（SAINT），通过学习嵌入对协变量进行条件化处理。我们在具有异方差和多模态年龄效应的合成基准数据集及英国生物银行FreeSurfer表型数据上进行评估，数据维度从2维扩展至200维。评估体系包含百分位数校准（绝对百分位误差、经验覆盖率和概率积分变换）、分布保真度（Kolmogorov-Smirnov检验）、多变量依赖性诊断以及最近邻记忆分析。在低维度场景中，扩散模型能提供与传统基线方法相当的、校准良好的单表型输出，同时联合建模真实的多变量依赖结构。在高维度场景中，Transformer主干网络相较于多层感知机仍保持显著更优的校准性能，并能更好地保留高阶依赖关系，从而构建可扩展的联合规范模型，同时保持与标准单表型处理流程的兼容性。这些研究结果证明基于扩散的规范建模是实现神经影像中校准多变量偏差谱图的有效途径。