Dynamic Connectivity Patterns in Resting State and Task-Based MEG: An Instantaneous Amplitude Correlation Approach

Understanding dynamic functional connectivity is pivotal in unraveling the intricate dynamics of neural activity. This research leverages Magnetoencephalography (MEG) and Instantaneous Amplitude Correlation (IAC) to explore the evolving patterns of dynamic functional connectivity in the human brain. The study meticulously compares IAC outcomes during resting state and task-based MEG, offering insights into the adaptability of brain connectivity. A comprehensive literature review contextualizes the study within existing research, highlighting the relevance of dynamic functional connectivity analysis. The MEG data preprocessing employs advanced techniques, including artifact reduction and source estimation. The IAC analysis, featuring tensor factorization and k-means clustering, reveals distinctive connectivity patterns in various frequency bands. Results demonstrate pronounced transitions between connectivity states, particularly in the beta frequency bands during resting state MEG. This comparative analysis enriches our understanding of neural dynamics and connectivity fluctuations, paving the way for potential clinical applications. The study underscores the need for broader validation through expanded datasets, emphasizing the implications for cognitive neuroscience and clinical practices.