Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Hum Brain Mapp. 2020 Oct 15;41(15):4264-4287. doi: 10.1002/hbm.25124. Epub 2020 Jul 9.
To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel-reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs-fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered-SWC (T-SWC) with different window lengths) based on both simulated and real rs-fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T-SWC (p-value = .001) and SWC (p-value <.001), respectively. Results of these different methods applied on real rs-fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T-SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC-based method was unable to detect these dynamic connections.
为了估计动态功能连接（dFC），传统的滑动窗口相关（SWC）方法在动态连接检测方面的性能较差。这源于观察结果的等权重、次优的时间尺度、非稀疏输出以及它是双变量的事实。为了克服这些限制，我们利用了核加权逻辑回归（KELLER）算法，这是遗传研究中常用的一种方法，用于估计静息状态功能磁共振成像（rs-fMRI）数据中的 dFC。KELLER 可以通过估计大脑区域之间的功能连接的空间和时间模式来估计 dFC。本文基于模拟和真实 rs-fMRI 数据，将所提出的 KELLER 方法与当前方法（SWC 和具有不同窗口长度的锥形 SWC（T-SWC））的性能进行了比较。通过假设检验评估了估计的 dFC 网络以检测动态连接的脑区对。模拟结果表明，KELLER 可以检测到具有 87.35%的统计功效的动态连接，而 T-SWC（p 值=0.001）和 SWC（p 值<0.001）分别为 70.17%和 58.54%。将这些不同方法应用于真实 rs-fMRI 数据的结果从两个方面进行了研究：计算识别的平均动态模式和识别默认模式网络（DMN）中的动态模式之间的相似性。在 68%的受试者中，100 秒窗口长度的 T-SWC 在不同窗口长度中表现出与 KELLER 最高的相似性。关于 DMN，KELLER 估计了背侧和腹侧 DMN 之间先前报道的动态连接对，而基于 SWC 的方法无法检测到这些动态连接。
