Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Nat Rev Neurosci. 2023 Oct;24(10):620-639. doi: 10.1038/s41583-023-00731-8. Epub 2023 Aug 24.
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
神经退行性疾病是痴呆症最常见的病因。尽管这些疾病的潜在分子病理学已经确定，但在这些疾病中，大脑进行性改变的模式在不同患者之间和患者内部存在很大的异质性。神经影像学方法的最新进展表明，病理性蛋白沿着特定的大脑宏观网络积累，这表明大脑的网络结构参与了神经退行性疾病的系统水平病理生理学。然而，“基于网络的神经退行性变”在广泛的神经退行性疾病中的适用程度尚不清楚。在这里，我们讨论了基于神经影像学的连接组学在神经退行性过程的映射和预测中的最新进展。我们综述了支持大脑网络作为病理性蛋白传播的被动通道的发现。作为另一种观点，我们还讨论了补充性工作，这些工作表明网络改变主动调节连接脑区之间病理性蛋白的传播。在这篇观点文章中，我们提出了一个综合框架，在这个框架中，可以沿着三个创新维度推进基于连接组的模型：根据可测量的生物学特征来调整传播行为的参数；构建基于个体水平信息的个体化模型，并允许模型参数随时间动态交互。我们讨论了这些策略在改善疾病认识和迈向精准医疗方面的前景和挑战。
