Arbabyazd Lucas M, Lombardo Diego, Blin Olivier, Didic Mira, Battaglia Demian, Jirsa Viktor
Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France.
AP-HM, Timone, Service de Pharmacologie Clinique et Pharmacovigilance, F-13005 Marseille, France.
MethodsX. 2020 Dec 1;7:101168. doi: 10.1016/j.mex.2020.101168. eCollection 2020.
•We have developed a framework to describe the dynamics of Functional Connectivity (dFC) estimated from brain activity time-series as a complex random walk in the space of possible functional networks. This conceptual and methodological framework considers dFC as a smooth reconfiguration process, combining "liquid" and "coordinated" aspects. Unlike other previous approaches, our method does not require the explicit extraction of discrete connectivity states.•In our previous work, we introduced several metrics for the quantitative characterization of the dFC random walk. First, dFC speed analyses extract the distribution of the time-resolved rate of reconfiguration of FC along time. These distributions have a clear peak (typical dFC speed, that can already serve as a biomarker) and fat tails (denoting deviations from Gaussianity that can be detected by suitable scaling analyses of FC network streams). Second, meta-connectivity (MC) analyses identify groups of functional links whose fluctuations co-vary in time and that define veritable dFC modules organized along specific dFC meta-hub controllers (differing from conventional FC modules and hubs). The decomposition of whole-brain dFC by MC allows performing dFC speed analyses separately for each of the detected dFC modules.•We present here blocks and pipelines for dFC random walk analyses that are made easily available through a dedicated MATLAB toolbox (), openly downloadable. Although we applied such analyses mostly to fMRI resting state data, in principle our methods can be extended to any type of neural activity (from Local Field Potentials to EEG, MEG, fNIRS, etc.) or even non-neural time-series.
•我们已经开发了一个框架，用于将从大脑活动时间序列估计的功能连接动力学（dFC）描述为可能的功能网络空间中的复杂随机游走。这个概念和方法框架将dFC视为一个平滑的重新配置过程，结合了“流动”和“协调”方面。与之前的其他方法不同，我们的方法不需要明确提取离散的连接状态。
•在我们之前的工作中，我们引入了几个指标来定量表征dFC随机游走。首先，dFC速度分析提取FC随时间重新配置的时间分辨速率的分布。这些分布有一个明显的峰值（典型的dFC速度，已经可以作为生物标志物）和肥尾（表示偏离高斯分布，可以通过对FC网络流进行适当的标度分析来检测）。其次，元连接性（MC）分析识别功能链接组，其波动在时间上协变，并定义沿着特定dFC元枢纽控制器组织的真正dFC模块（不同于传统的FC模块和枢纽）。通过MC对全脑dFC进行分解，可以对每个检测到的dFC模块分别进行dFC速度分析。
•我们在此展示了用于dFC随机游走分析的模块和流程，可通过一个专用的MATLAB工具箱轻松获取（可公开下载）。尽管我们大多将此类分析应用于fMRI静息态数据，但原则上我们的方法可以扩展到任何类型的神经活动（从局部场电位到脑电图、脑磁图、功能近红外光谱等），甚至非神经时间序列。
