Information transfer among brain regions is the fundamental intelligence computing in the primate brain. The local and global topology connectivity among functional brain regions directly driven human cognitive behaviors. The real neural signal includes the real neural firing and neural self-noise. Neural self-noise non-regular firing or synchronization will cause severe neuron disease and it also plays a very important role in the functional connectivity. Therefore, how the neural self-noise affect the information flow among brain regions? Firstly, Phase Locking Value(PLV) can investigate task-induced neural synchronization from the EEG signal [1]. Secondly, Shannon’s information theory and its extensions framework can be used to quantitative the maximum information capacity in the noise neural channel. The basic idea is 1) simulation: the fake neural signal (EEG) produced by setting certain time points, number of subjects, and number trials with neural self-noise. The amplitude of neural signals can be changed according to given parameters. 2) Phase Locking Value (PLV) measured among brain regions with setting different parameters of time series. 3) redundancy measured among brain regions with Total Correlation (TC)/Multivariate Mutual Information. [2] 4) Compared different measure results and check how the neural self-noise affects the information flow in the brain. The result shown 1) neural-self noise directly affect functional connectivity among brain regions (increase neuron self-noise will reduce PLV and TC) 2) TC has stronger statistical power than PLV in the quantitative functional connectivity
Appendix:
The Matlab demo used in this short research can get from here: https://github.com/sinodanish/NeuroMatch3.0
References:
[1] Lachaux, Jean‐Philippe, et al. “Measuring phase synchrony in brain signals.” Human brain mapping 8.4 (1999): 194-208.
[2] Watanabe, S. Information theoretical analysis of multivariate correlation.IBM Journal of research anddevelopment1960,4, 66–82.