bioRxivNeurosciencePreprint
What drives intersubject correlation of EEG during auditory narratives?
Emilia FlóiD, Álvaro CabanaiD, Juan Carlos Valle-LisboaiD, Damian CruseiD, Jens MadseniD, Lucas C. ParraiD, Jacobo Diego SittiD
Abstract
When participants are engaged with auditory narratives, physiological and neural signals exhibit temporal correlations between subjects. The intersubject correlation (ISC) increases when attention is directed to the stories, suggesting that shared neural and bodily dynamics arise from a similar processing of the narratives. In this study, we investigated whether the ISC of the EEG elicited by auditory narratives is driven by low-level acoustic (envelope, spectrogram) and/or higher-level linguistic information (word onset, word surprisal) in two groups of healthy participants during passive, attentive and distracted listening. We use temporal response functions (TRFs) for acoustic, and linguistic features to assess the contribution of each feature to the ISC, measured using correlated component analysis (CorrCA). TRFs derived for acoustic features explained a larger fraction of variance in the EEG than linguistic features and were the main contributors to the ISC. The attention-related increase in ISC was driven by all features. Importantly, word surprisal had an effect on ISC only during active story engagement, with timing and scalp distribution consistent with language processing. Notably, the linear responses captured by TRFs only explained a small amount of the overall ISC, suggesting that ISC is largely driven by nonlinear responses to the narratives. Identifying the factors that drive these common neural responses, is clinically relevant for interpreting EEG ISC exhibited in unresponsive patients. We propose that the combined use of ISC and TRFs has the potential to provide meaningful markers of language processing in patients with disorders of consciousness, and we suggest practical recommendations for implementing these approaches in clinical settings.
- organism: human
- assay: electronic health records
- research focus: cell-cell communication
- approach: statistical model
- Citations
- 0
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