Faghiri Ashkan, Yang Kun, Faria Andreia, Ishizuka Koko, Sawa Akira, Adali Tülay, Calhoun Vince
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Netw Neurosci. 2024 Oct 1;8(3):734-761. doi: 10.1162/netn_a_00372. eCollection 2024.
Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between subcortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.
使用时间分辨网络来表示数据对于分析人类大脑的功能数据很有价值。从数据构建时间分辨网络的一种常用方法是滑动窗口皮尔逊相关性（SWPC）。SWPC的一个主要局限性在于它对活动时间序列应用了高通滤波器。因此，如果我们选择一个短窗口（这对于估计连通性的快速变化是理想的），我们将去除重要的低频信息。在此，我们提出一种基于通信理论中的单边带调制（SSB）的方法。这使我们能够选择更短的窗口来捕捉时间分辨功能网络连通性（trFNC）的快速变化。我们使用模拟数据和真实的静息态功能磁共振成像（fMRI）数据来证明与SWPC相比，SSB + SWPC具有更优越的性能。我们还比较了首次发作精神病（FEP）个体和典型对照组（TC）之间反复出现的trFNC模式，并表明FEP个体更多地处于全脑连通性较弱的状态。SSB + SWPC特有的一个结果是，TC个体更多地处于皮层下和皮层区域之间具有负连通性的状态。基于所有这些结果，我们认为SSB + SWPC在捕捉trFNC的时间变化方面更敏感。
