Temporal Response Function-Driven Representational Similarity Analysis for Speech Perception Decoding with MEG and...

Speech perception relies on distributed neuronal populations, yet traditional decoding often utilizes static strategies that overlook inherent temporal dependencies and dynamic regulation. Therefore, we introduce the concept of system identification into multivariate decoding. By modeling brain response characteristics through time-lagged regression between speech stimuli and neural responses, we propose a temporal response function-based representational similarity analysis method (TRF-RSA). This method models the dynamic time-lag mapping from continuous stimulus features to neural responses, effectively separating stimulus-driven coherent activity from high-dimensional noise. More importantly, it elevates the analytical perspective from static comparisons of raw signals to dynamic trajectories in weight space. We conducted an auditory experiment and incorporated high spatiotemporal resolution optically pumped magnetometer magnetoencephalography magnetoencephalography (OPM-MEG) with electroencephalography (EEG). The results showed that TRF-RSA significantly enhanced the pattern similarity between speech sounds and the ability to discriminate between pattern differences. Furthermore, it revealed stronger similarities elicited by biological vocalizations, indicating a preference in the brain for these species-specific sounds. Source localization results not only confirmed the classical speech perception network but also revealed activation in limbic and deep brain regions. By modeling the relationship between stimulus features and neural responses, TRF-RSA dynamically quantified the spatiotemporal patterns of stimulus-driven neural activity, improving the sensitivity of representational pattern decoding during the encoding process. These findings suggest that this method is a sensitive neuroimaging tool that not only advances our understanding of the spatiotemporal dynamics of speech processing but also provides a new reference for population dynamics research.