Objective. Syntax involves complex neurobiological mechanisms, which are difficult to disentangle for multiple reasons. Approach. Using a protocol able to separate syntactic information from sound information we investigated the neural causal connections evoked by the processing of homophonous phrases, i.e. with the same acoustic information but with different syntactic content. These could be either verb phrases (VP) or noun phrases (NP). We used event-related causality (ERC) from stereo-electroencephalographic (SEEG) recordings in 10 epileptic patients in multiple cortical and subcortical areas, including language areas and their homologous in the non-dominant hemisphere. The recordings were made while the subjects were listening to the homophonous phrases. Main Results. We identified the different networks involved in the processing of these syntactic operations (faster in the dominant hemisphere) showing that VPs engage a wider cortical and subcortical network. We also present a proof-of-concept for the decoding of the syntactic category of a perceived phrase based on causality measures. Significance. Our findings help unravel the neural correlates of syntactic elaboration and show how a decoding based on multiple cortical and subcortical areas could contribute to the development of speech prostheses for speech impairment mitigation.

A sharp tension exists about the nature of human language between two opposite parties: those who believe that statistical surface distributions, in particular using measures like surprisal, provide a better understanding of language processing, vs. those who believe that discrete hierarchical structures implementing linguistic information such as syntactic ones are a better tool. In this paper, we show that this dichotomy is a false one. Relying on the fact that statistical measures can be defined on the basis of either structural or non-structural models, we provide empirical evidence that only models of surprisal that reflect syntactic structure are able to account for language regularities.One-sentence summaryLanguage processing does not only rely on some statistical surface distributions, but it needs to be integrated with syntactic information.

Objective. Stereo-electroencephalography (SEEG) has recently gained importance in analyzing brain functions. Its high temporal resolution and spatial specificity make it a powerful tool to investigate the strength, direction, and spectral content of brain networks interactions, especially when these connections are stimulus-evoked. However, choosing the best approach to evaluate the flow of information is not trivial, due to the lack of validated methods explicitly designed for SEEG. Approach. We propose a novel non-parametric statistical test for event-related causality (ERC) assessment on SEEG recordings. Here, we refer to the ERC as the causality evoked by a particular part of the stimulus (a response window (RW)). We also present a data surrogation method to evaluate the performance of a causality estimation algorithm. We finally validated our pipeline using surrogate SEEG data derived from an experimentally collected dataset, and compared the most used and successful measures to estimate effective connectivity, belonging to the Geweke–Granger causality framework. Main results. Here we show that our workflow correctly identified all the directed connections in the RW of the surrogate data and prove the robustness of the procedure against synthetic noise with amplitude exceeding physiological-plausible values. Among the causality measures tested, partial directed coherence performed best. Significance. This is the first non-parametric statistical test for ERC estimation explicitly designed for SEEG datasets. The pipeline, in principle, can also be applied to the analysis of any type of time-varying estimator, if there exists a clearly defined RW.