用图神经网络分析大脑皮层形态，精准刻画阿尔茨海默病局部老化特征。
Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease
- 构建图神经网络，融合皮层厚度、曲率等5类形态特征预测局部脑龄。
- 在ADNI数据集上误差更低，发现阿尔茨海默病患者全皮层显著老化。
- 揭示曲率和灰白质比对病理敏感，与认知功能下降显著相关。
从T1加权磁共振成像（MRI）估算脑龄（BA）为量化解剖脑衰老提供了有力框架。全局脑龄（GBA）反映整体脑健康，而局部脑龄（LBA）则在个体层面提供皮层特异的衰老模式。尽管已有研究探讨了影响GBA的解剖因素，但尚无框架用于基于皮层形态估计LBA。为此，我们提出一种图神经网络（GNN），利用皮层厚度、表面积、曲率、灰/白质强度比（GWR）、沟深等形态特征，在高空间分辨率下（平均顶点间距=1.37毫米）估计全皮层表面的LBA。模型基于14,423名认知正常（CN）成年人的皮层网格训练，在ADNI数据集上实现低于现有最优方法的平均绝对误差（MAE），并识别出更符合生物学意义的阿尔茨海默病（AD）衰老模式。在CN中，关联皮层是主要老化区域；轻度认知障碍者表现为广泛老化，以海马旁回最为显著；AD患者则全皮层显著老化，尤其集中在内侧颞叶及关联皮层网络。特征消融分析表明，曲率和GWR对AD病理尤为敏感。区域性的局部脑龄差异与阿尔茨海默病相关的神经心理学测验显著相关，将皮层老化模式与临床结果联系起来。结果表明，基于图神经网络的皮层形态建模能够实现更具生物学可解释性的局部脑衰老映射，优于以往工作。
原文摘要 · Abstract (English)
Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a powerful framework for quantifying anatomical brain aging. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) provides cortically specific patterns of aging at the subject level. Although previous studies have examined anatomical contributors to GBA, to our knowledge, no framework has been established to estimate LBA using cortical morphology. To address this gap, we introduce a graph neural network (GNN) that uses morphometric features$\unicode{x2013}$cortical thickness, surface area, curvature, gray/white matter intensity ratio (GWR), sulcal depth$\unicode{x2013}$to estimate LBA across the cortical surface at high spatial resolution (mean inter-vertex distance = 1.37 mm). Trained on cortical surface meshes extracted from the MRIs of cognitively normal (CN) adults (N = 14,423), our model achieves lower mean absolute error (MAE) than the existing state-of-the-art while identifying more biologically plausible patterns of aging in Alzheimer's disease (AD) on the ADNI dataset. Association cortices emerge as primary sites of morphometric aging in CNs, whereas mild cognitive impairment is characterized by widespread aging that is pronounced in the parahippocampal gyrus. AD subjects demonstrate significant aging across the entire cortex, particularly within medial temporal regions and associated cortical networks. Feature ablation highlights curvature and GWR as preferentially sensitive to AD pathology. Regional LBA gaps are significantly associated with neuropsychological measures of AD-related cognitive impairment, linking cortical aging patterns to clinical outcomes. These results demonstrate that GNN-based modeling of cortical morphometry enables biologically interpretable mapping of local brain aging with greater interpretability than prior work.
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