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Evaluating Auditory Neural Activities and Information Transfer Using Phase and Spike Train Correlation Algorithms

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The coherence of neural activities among different areas in the brain has received great attention because it is valuable in understanding the functional mechanism of brain structures. While many methodologies, such as time-frequency and entropy analysis, have been applied to evaluate relations between neural signals, these techniques haven’t been effective in assessing neural communication in order to reach conclusions. Considering various measurements, the results analyzed by the above-mentioned algorithms may be influenced by the types of neural signals and their amplitudes, which affect their reliability and consistency. In this study, we introduced two new methods, phase-phase and spike train correlations, to analyze the neural signals communications among various areas of the brain, aiming to decipher neural information communications between different brain structures of normal rats and those with noise-induced tinnitus, a ringing condition in the ear or head. To test the proposed methodologies, a set of electrophysiological recordings of tinnitus-related spontaneous activities were conducted in the auditory cortex (AC), inferior colliculus (IC), and dorsal cochlear nucleus (DCN). The results using the two proposed algorithms were demonstrated and compared to those obtained by the transfer entropy (TE) method using the same experimental data set. Both algorithms yielded a result in a consistent scale of zero to one indicating the strength of correlation and showed a similar trend to results by TE. The experimental results on rats have shown information flow within and between most structures with a stronger correlation at lower frequencies.

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