Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA.
Neuroimage. 2023 Nov 1;281:120358. doi: 10.1016/j.neuroimage.2023.120358. Epub 2023 Sep 11.
Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.
动态静息态功能连接（RSFC）描绘了功能脑网络活动的时变波动。尽管许多研究都探讨了静态功能连接，但尚不清楚动态功能连接的特征是否与神经退行性疾病有关。提取动态 RSFC 的常用滑动窗口和聚类方法存在各种限制，无法提取可靠的特征来解决这个问题。在这里，我们使用一种新颖而稳健的时变动态网络（TVDN）方法，从阿尔茨海默病（AD）患者和匹配对照者的高分辨率脑磁图（MEG）数据中提取动态 RSFC 的特征。TVDN 算法自动且自适应地学习动态 RSFC 的低维时空流形，并检测数据中的动态状态转换。我们表明，在所研究的所有功能特征中，动态流形特征是最能预测 AD 的。这些特征包括：大脑网络的时间复杂度，由状态转换的数量及其停留时间决定，以及大脑网络的空间复杂度，由本征模式的数量决定。与现有基准相比，这些动态特征在区分 AD 与健康受试者方面具有更高的灵敏度和特异性。有趣的是，我们发现与健康对照组相比，AD 患者通常具有更高的空间复杂度但更低的时间复杂度。我们还表明，TVDN 动态分量的图论指标在 AD 与对照组之间存在显著差异，而静态图指标在统计学上没有差异。这些结果表明，动态 RSFC 特征在神经退行性疾病（如阿尔茨海默病）中受到影响，这可能对理解这些疾病的病理生理轨迹至关重要。
