Functional connectivity in the dorsal stream and between bilateral auditory-related cortical areas differentially contribute to speech decoding depending on spectro-temporal signal integrity and performance

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Abstract

Speech processing relies on the interdependence between auditory perception, sensorimotor integration, and verbal memory functions. Functional and structural connectivity between bilateral auditory-related cortical areas (ARCAs) facilitates spectro-temporal analyses, whereas the dynamic interplay between ARCAs and Broca's area (i.e., dorsal pathway) contributes to verbal memory functions, articulation, and sound-to-motor mapping. However, it remains unclear whether these two neural circuits are preferentially driven by spectral or temporal acoustic information, and whether their recruitment is predictive of speech perception performance and learning. Therefore, we evaluated EEG-based intracranial (eLORETA) functional connectivity (lagged coherence) in both pathways (i.e., between bilateral ARCAs and in the dorsal stream) while good- (GPs, N = 12) and poor performers (PPs, N = 13) learned to decode natural pseudowords (CLEAN) or comparable items (speech-noise chimeras) manipulated in the envelope (ENV) or in the fine-structure (FS). Learning to decode degraded speech was generally associated with increased functional connectivity in the theta, alpha, and beta frequency range in both circuits. Furthermore, GPs exhibited increased connectivity in the left dorsal stream compared to PPs, but only during the FS condition and in the theta frequency band. These results suggest that both pathways contribute to the decoding of spectro-temporal degraded speech by increasing the communication between brain regions involved in perceptual analyses and verbal memory functions. Otherwise, the left-hemispheric recruitment of the dorsal stream in GPs during the FS condition points to a contribution of this pathway to articulatory-based memory processes that are dependent on the temporal integrity of the speech signal. These results enable to better comprehend the neural circuits underlying word-learning as a function of temporal and spectral signal integrity and performance.

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