不依赖节点对齐，用随机游走分析脑皮层折叠网络，提升阿尔茨海默病与路易体痴呆诊断精度。
Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis
- 基于皮层褶皱构建个体化网络，用随机游走分布表示，避免节点对齐需求
- 在大规模临床数据集上优于传统图学习方法，分类准确率显著提升
- 适合处理脑结构异质性强的神经退行性疾病诊断，尤其适用于个体化医疗
阿尔茨海默病（AD）与路易体痴呆（LBD）临床表现重叠，需差异化诊断。虽神经影像脑网络分析具潜力，但基于图谱的表征会掩盖个体解剖差异。基于三铰接褶皱的皮层折叠网络提供生物合理性替代方案，但个体间皮层褶皱变异导致地标对应不一致、网络规模极不规则，违反多数图学习方法的固定拓扑与节点对齐假设，尤其在病理改变加剧解剖异质性的临床数据中更为明显。为此，我们提出一种概率不变的随机游走框架，无需显式节点对齐即可分类个体化皮层折叠网络。通过局部形态特征构建皮层相似性网络，并以匿名化随机游走分布表示，结合解剖感知编码以保持置换不变性。在大规模AD与LBD受试者临床队列上的实验显示，该方法持续优于现有基于褶皱与图谱的模型，展现出鲁棒性与在痴呆诊断中的应用潜力。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroimaging-based brain network analysis is promising, atlas-based representations may obscure individualized anatomy. Gyral folding-based networks using three-hinge gyri provide a biologically grounded alternative, but inter-individual variability in cortical folding results in inconsistent landmark correspondence and highly irregular network sizes, violating the fixed-topology and node-alignment assumptions of most existing graph learning methods, particularly in clinical datasets where pathological changes further amplify anatomical heterogeneity. We therefore propose a probability-invariant random-walk-based framework that classifies individualized gyral folding networks without explicit node alignment. Cortical similarity networks are built from local morphometric features and represented by distributions of anonymized random walks, with an anatomy-aware encoding that preserves permutation invariance. Experiments on a large clinical cohort of AD and LBD subjects show consistent improvements over existing gyral folding and atlas-based models, demonstrating robustness and potential for dementia diagnosis.
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