Study on neural entrainment to continuous speech using dynamic source connectivity analysis

Abstract

AbstractObjective.Many recent studies investigating the processing of continuous natural speech have employed electroencephalography (EEG) due to its high temporal resolution. However, most of these studies explored the response mechanism limited to the electrode space. In this study, we intend to explore the underlying neural processing in the source space, particularly the dynamic functional interactions among different regions during neural entrainment to speech.Approach.We collected 128-channel EEG data while 22 participants listened to story speech and time-reversed speech using a naturalistic paradigm. We compared three different strategies to determine the best method to estimate the neural tracking responses from the sensor space to the brain source space. After that, we used dynamic graph theory to investigate the source connectivity dynamics among regions that were involved in speech tracking.Main result.By comparing the correlations between the predicted neural response and the original common neural response under the two experimental conditions, we found that estimating the common neural response of participants in the electrode space followed by source localization of neural responses achieved the best performance. Analysis of the distribution of brain sources entrained to story speech envelopes showed that not only auditory regions but also frontoparietal cognitive regions were recruited, indicating a hierarchical processing mechanism of speech. Further analysis of inter-region interactions based on dynamic graph theory found that neural entrainment to speech operates across multiple brain regions along the hierarchical structure, among which the bilateral insula, temporal lobe, and inferior frontal gyrus are key brain regions that control information transmission. All of these information flows result in dynamic fluctuations in functional connection strength and network topology over time, reflecting both bottom-up and top-down processing while orchestrating computations toward understanding.Significance.Our findings have important implications for understanding the neural mechanisms of the brain during processing natural speech stimuli.