Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.
Biomedical Engineering Department, Tulane University, New Orleans, LA, USA; School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
J Neurosci Methods. 2022 Apr 15;372:109539. doi: 10.1016/j.jneumeth.2022.109539. Epub 2022 Feb 24.
Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors.
In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes.
We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors.
The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session.
Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness.
功能连接组已被证明能够预测个体的特征，充当指纹。大多数研究使用 fMRI 信号的幅度信息来构建连接，但相位同步是否可以纳入以提高对个体认知行为的预测尚不清楚。
在本文中，我们通过相位锁定方法从 fMRI 时间序列中提取相位信息，然后构建功能连接组来解决这个问题。
我们首先使用基于相位的图谱与基于幅度的连接组进行比较，检查基于相位的图谱在识别和预测性能方面的表现。然后，我们结合基于相位和基于幅度的连通性来提取相位同步所支持的个体特异性信息。结果表明，基于相位的连接组可以实现高达 82.7%至 92.6%的个体识别率。基于相位的连通性提供了与基于幅度的信号互补的独特信息。内网络相位锁定对于个体预测似乎更具信息量。此外，相位同步可用于预测认知行为。
基于幅度的连通性由于神经同步而无法捕捉个体特异性信息。讨论部分涉及与其他基于相位的方法的比较。
我们的发现表明，神经同步携带个体特异性信息，可以通过锁相值来捕获。将相位信息纳入连接组为理解每个个体大脑的独特性提供了一种很有前途的方法。
