Alzheimer’s disease, the most common form of dementia, is characterized by the emergence and spread of senile plaques and neurofibrillary tangles, causing widespread neurodegeneration. Though the progression of Alzheimer’s disease is considered to be stereotyped, the significant variability within clinical populations obscures this interpretation on the individual level. Of particular clinical importance is understanding where exactly pathology, e.g. tau, emerges in each patient and how the incipient atrophy pattern relates to future spread of disease. Here we demonstrate a newly developed graph theoretical method of inferring prior disease states in patients with Alzheimer’s disease and mild cognitive impairment using an established network diffusion model and an L₁-penalized optimization algorithm. Although the ‘seeds’ of origin using our inference method successfully reproduce known trends in Alzheimer’s disease staging on a population level, we observed that the high degree of heterogeneity between patients at baseline is also reflected in their seeds. Additionally, the individualized seeds are significantly more predictive of future atrophy than a single seed placed at the hippocampus. Our findings illustrate that understanding where disease originates in individuals is critical to determining how it progresses and that our method allows us to infer early stages of disease from atrophy patterns observed at diagnosis.

中文翻译：

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一种推断神经变性区域起源的方法

阿尔茨海默病是最常见的痴呆症，其特征是老年斑和神经原纤维缠结的出现和扩散，导致广泛的神经变性。尽管阿尔茨海默病的进展被认为是刻板印象，但临床人群中的显着差异掩盖了个体水平上的这种解释。特别重要的临床意义是了解每个患者的病理学（例如 tau）的确切位置以及初期萎缩模式与未来疾病传播的关系。在这里，我们展示了一种新开发的图理论方法，该方法使用已建立的网络扩散模型和L ₁来推断阿尔茨海默病和轻度认知障碍患者的先前疾病状态-惩罚优化算法。尽管使用我们的推理方法的起源“种子”成功地在人群水平上重现了阿尔茨海默病分期的已知趋势，但我们观察到基线患者之间的高度异质性也反映在他们的种子上。此外，个体化的种子比放置在海马体的单个种子更能预测未来的萎缩。我们的研究结果表明，了解疾病起源于个体的位置对于确定其进展方式至关重要，并且我们的方法使我们能够从诊断时观察到的萎缩模式推断疾病的早期阶段。