A Complementary Method of PCC for the Construction of Scalp Resting-State EEG Connectome: Maximum Information Coefficient

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Abstract

Disclosing the complex relationships effectively between paired brain regions played a significant role in measuring the brain functional connectivity and exploring brain topological structures. Even though Pearson correlation coefficient (PCC) has been widely used to construct functional brain networks in the previous studies, it was mainly sensitive to linear associations. Therefore, maximal information coefficient (MIC) was first utilized to make up this weakness of PCC to construct electroencephalography (EEG) connectivity in the current study. The simulation results showed that MIC could capture certain relationships which PCC failed to detect. Furthermore, brain network properties changed with various thresholds under the resting-state EEG, and the comparison analysis of network properties illustrated that MIC and PCC could capture different aspects of connections between paired brain regions. These findings indicated that MIC could be a complementary method of PCC for the construction of scalp resting-state EEG connectome and provided a novel tool to reveal the potential mechanisms of brain networks.

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Tian, Y., Zhang, H., Li, P., & Li, Y. (2019). A Complementary Method of PCC for the Construction of Scalp Resting-State EEG Connectome: Maximum Information Coefficient. IEEE Access, 7, 27146–27154. https://doi.org/10.1109/ACCESS.2019.2897908

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