Mutual-Information-Based Approach for Neural Connectivity during Self-Paced Finger Lifting Task

Frequency-dependent modulation between neuronal assemblies may provide insightful mechanisms of functional organization in the context of neural connectivity. We present a conjoined time-frequency cross mutual information (TFCMI) method to explore the subtle brain neural connectivity by magnetoencephalography (MEG) during a self-paced finger lifting task. Surface electromyogram (sEMG) was obtained from the extensor digitorum communis. Both within-modality (MEG-MEG) and between-modality studies (sEMG-MEG) were conducted. The TFCMI method measures both the linear and nonlinear dependencies of the temporal dynamics of signal power within a pre-specified frequency band. Each single trial of MEG across channels and sEMG signals was transformed into time-frequency domain with use of the Morlet wavelet to obtain better temporal spectral (power) information. As compared to coherence approach (linear dependency only) in broadband analysis, the TFCMI method demonstrated advantages in encompassing detection for the mesial frontocentral cortex and bilateral primary sensorimotor areas, clear demarcation of event- and non-event-related regions, and robustness for sEMG - MEG between-modality study, i.e., corticomuscular communication. We conclude that this novel TFCMI method promises a possibility to better unravel the intricate functional organizations of brain in the context of oscillation-coded communication.