Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences - Cai, Biao; Zhou, Zhongxing; Zhang, Aiying; Zhang, Gemeng; Xiao, Li; Stephen, Julia M.; Wilson, Tony W.; Calhoun, Vince D.; Wang, Yu-Ping (Journal of neuroscience methods, 2022)
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. Comparison with existing method: 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. •Examine the identification and prediction performance using phase-based profiles.•Propose a pipeline to combine the amplitude and phase information to reveal FC’s the fingerprinting ability.•Validate GICA-TVGL using two independent neuroimaging datasets (e.g., HCP and PNC).