Pinpointing the sources of dementia is crucial to the effective treatment of neurodegenerative diseases. In this paper, we propose a diffusion model with impulsive sources over the brain connectivity network to model the progression of brain atrophy. To reliably estimate the atrophy sources, we impose sparse regularization on the source distribution and solve the inverse problem with an efficient gradient descent method. We localize the possible origins of Alzheimer's disease (AD) based on a large set of repeated magnetic resonance imaging (MRI) scans in Alzheimer's Disease Neuroimaging Initiative database. The distribution of the sources averaged over the sample population is evaluated. We find that the dementia sources have different concentrations in the brain lobes for AD patients and mild cognitive impairment (MCI) subjects, indicating possible switch of the dementia driving mechanism. Moreover, we demonstrate that we can effectively predict changes of brain atrophy patterns with the proposed model. Our work could help understand the dynamics and origin of dementia, as well as monitor the progression of the diseases in an early stage.

中文翻译：

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用网络扩散模型定位脑疾病进展的来源

查明痴呆的来源对于有效治疗神经退行性疾病至关重要。在本文中，我们提出了一种在大脑连接网络上具有脉冲源的扩散模型，以模拟脑萎缩的进展。为了可靠地估计萎缩源，我们对源分布施加稀疏正则化，并使用有效的梯度下降方法解决逆问题。我们根据阿尔茨海默病神经影像倡议数据库中的大量重复磁共振成像 (MRI) 扫描来定位阿尔茨海默病 (AD) 的可能起源。对样本总体上的平均来源分布进行评估。我们发现痴呆源在 AD 患者和轻度认知障碍 (MCI) 受试者的脑叶中具有不同的浓度，指示痴呆驱动机制的可能开关。此外，我们证明我们可以使用所提出的模型有效地预测脑萎缩模式的变化。我们的工作可以帮助了解痴呆症的动态和起源，并在早期监测疾病的进展。