Department of Pharmaceutical, Veterinary and Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium.
Department of Biomedical Engineering, Emory University, 1760 Haygood Dr. NE, Atlanta, GA, 30322, USA.
Sci Rep. 2018 Jul 3;8(1):10024. doi: 10.1038/s41598-018-28237-9.
Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regression to determine if QPPs improved disease classification. We present three prominent findings: (1) Compared to controls, TG2576 mice were marked by opposing neural dynamics in which DMN areas were anti-correlated and displayed diminished anti-correlation with the TPN. (2) QPPs reflected lowered DMN functional connectivity in TG2576 mice and revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures significantly improved classification compared to conventional functional connectivity measures. Altogether, our findings provide insight into the neural dynamics of aberrant network connectivity in AD and indicate that QPPs might serve as a translational diagnostic tool.
静息态 fMRI 可用于测量大脑功能连接，已将默认模式 (DMN) 和任务正激活 (TPN) 网络的中断确定为有希望的阿尔茨海默病 (AD) 生物标志物。神经活动的准周期模式 (QPP) 描述了显示 DMN 与 TPN 反相关的重复时空模式。我们推断 QPP 可以为 AD 网络功能障碍提供新的见解，并改善疾病诊断。因此，我们使用 rsfMRI 研究了淀粉样变性模型 TG2576 小鼠和年龄匹配对照的 QPP。确定了多个 QPP 并在组间进行了比较。我们使用线性回归从功能扫描中去除它们的贡献，并评估它们如何反映功能连接。最后，我们使用弹性网络回归来确定 QPP 是否可以改善疾病分类。我们提出了三个突出的发现：(1)与对照组相比，TG2576 小鼠的神经动力学明显相反，DMN 区域呈反相关，与 TPN 的反相关性降低。(2)QPP 反映了 TG2576 小鼠 DMN 功能连接的降低，并显示出明显降低的 DMN-TPN 反相关性。(3)与传统的功能连接测量相比，QPP 衍生的测量显著改善了分类。总之，我们的研究结果为 AD 中异常网络连接的神经动力学提供了深入了解，并表明 QPP 可能成为一种转化诊断工具。
