Adaptive Multi-Band Instantaneous Dynamic Functional Connectivity Method Based on Hilbert-Huang Transform
Li Muchen1, Song Zirui1, Xu Yuyang1, Zhou Yang2, Wang Yanxi2, Qing Zhao1*
1(School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China) 2(School of Automation, Southeast University, Nanjing 211189, China)
Abstract：Functional connectivity (FC) is an important indicator commonly used in the functional magnetic resonance imaging (fMRI) research to study neural synchrony between brain regions. Recent studies have indicated that FC exhibits frequency and time-varying characteristics, the latter being referred to as “dynamic functional connectivity” (dFC). This study addressed the lack of fixed standards for important parameters such as frequency bands and sliding window widths in the studies of FC frequency and temporal characteristics. We proposed to apply the Hilbert-Huang transform (HHT) to establish an adaptive cross-frequency FC time-frequency characteristic analysis method. The study utilized an fMRI dataset from the “Consortium for Reliability and Reproducibility” (CORR) at the Institute of Psychology, Chinese Academy of Sciences, which includes 50 participants with 2 scans each. After conventional preprocessing, the fMRI signals from 246 brain regions were extracted using the human brain connectome atlas. The HHT method was then applied to the fMRI signals to adaptively decompose them into intrinsic mode functions (IMFs) with different frequency characteristics. Using the instantaneous phases defined by HHT, dFC matrices were constructed for each time point based on the instantaneous phase differences between pairs of the 246 brain regions, and conventional dFC analysis was performed. The results showed that within the frequency band representing resting-state neural activity, namely 0.01~0.1 Hz, there were three IMFs (IMF1~IMF3) in the functional network connectivity pattern of the human brain, with Hilbert-weighted frequency distributions of (0.050±0.007), (0.022±0.005), and (0.011±0.004)Hz. K-means clustering classified the dFC matrices at different time points for each IMF into five brain connection states. The occurrence times of these five states in any two frequency bands exhibited significant clustering effects (chi-square test, P<0.001). The connection patterns of states that tend to occur simultaneously in different frequency bands also exhibited high spatial similarity (for example, the spatial similarity of the average connection matrices for the first connection state of IMF1 and IMF2 reaches R=0.90, P<0.001). In conclusion, this study revealed the existence of multi-frequency dynamic network patterns in the human brain and provided a multi-frequency instantaneous brain network analysis with higher temporal resolution, offering new insights into the analysis of fMRI data.
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