Localizing Sources of Brain Disease Progression with Network Diffusion Model.

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 (ADNI) 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.

确定痴呆症的病因对于神经退行性疾病的有效治疗至关重要。在本文中，我们提出了一种基于大脑连接网络的具有脉冲源的扩散模型，用于模拟大脑萎缩的进展。为了可靠地估计萎缩源，我们对源分布施加稀疏正则化，并使用一种高效的梯度下降方法解决反问题。我们基于阿尔茨海默病神经影像学倡议（ADNI）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。对样本群体的源分布平均值进行了评估。我们发现，对于AD患者和轻度认知障碍（MCI）受试者，痴呆源在脑叶中的浓度不同，这表明痴呆驱动机制可能发生了转变。此外，我们证明了我们可以使用所提出的模型有效地预测大脑萎缩模式的变化。我们的工作有助于理解痴呆症的动态和起源，并在早期监测疾病的进展。