Agnès Trébuchon,  F.-Xavier Alario,  & Catherine Liégeois-Chauvel,

Front. Hum. Neurosci. 15:702773

The posterior part of the superior temporal gyrus (STG) has long been known to be a crucial hub for auditory and language processing, at the crossroad of the functionally defined ventral and dorsal pathways. Anatomical studies have shown that this “auditory cortex” is composed of several cytoarchitectonic areas whose limits do not consistently match macro-anatomic landmarks like gyral and sulcal borders. The only method to record and accurately distinguish neuronal activity from the different auditory sub-fields of primary auditory cortex, located in the tip of Heschl and deeply buried in the Sylvian fissure, is to use stereotaxically implanted depth electrodes (Stereo-EEG) for pre-surgical evaluation of patients with epilepsy. In this prospective, we focused on how anatomo-functional delineation in Heschl’s gyrus (HG), Planum Temporale (PT), the posterior part of the STG anterior to HG, the posterior superior temporal sulcus (STS), and the region at the parietal-temporal boundary commonly labeled “SPT” can be achieved using data from electrical cortical stimulation combined with electrophysiological recordings during listening to pure tones and syllables. We show the differences in functional roles between the primary and non-primary auditory areas, in the left and the right hemispheres. We discuss how these findings help understanding the auditory semiology of certain epileptic seizures and, more generally, the neural substrate of hemispheric specialization for language.

Jacques Pesnot Lerousseau & Daniele Schön

Cerebral Cortex, 31 (11), 4877–4890

It is poorly known whether musical training is associated with improvements in general cognitive abilities, such as statistical learning (SL). In standard SL paradigms, musicians have shown better performances than nonmusicians. However, this advantage could be due to differences in auditory discrimination, in memory or truly in the ability to learn sequence statistics. Unfortunately, these different hypotheses make similar predictions in terms of expected results. To dissociate them, we developed a Bayesian model and recorded electroencephalography (EEG). Our results confirm that musicians perform approximately 15% better than nonmusicians at predicting items in auditory sequences that embed either low or high-order statistics. These higher performances are explained in the model by parameters governing the learning of high-order statistics and the selection stage noise. EEG recordings reveal a neural underpinning of the musician’s advantage: the P300 amplitude correlates with the surprise elicited by each item, and so, more strongly for musicians. Finally, early EEG components correlate with the surprise elicited by low-order statistics, as opposed to late EEG components that correlate with the surprise elicited by high-order statistics and this effect is stronger for musicians. Overall, our results demonstrate that musical expertise is associated with improved neural SL in the auditory domain. Significance statement It is poorly known whether musical training leads to improvements in general cognitive skills. One fundamental cognitive ability, SL, is thought to be enhanced in musicians, but previous studies have reported mixed results. This is because such musician’s advantage can embrace very different explanations, such as improvement in auditory discrimination or in memory. To solve this problem, we developed a Bayesian model and recorded EEG to dissociate these explanations. Our results reveal that musical expertise is truly associated with an improved ability to learn sequence statistics, especially high-order statistics. This advantage is reflected in the electroencephalographic recordings, where the P300 amplitude is more sensitive to surprising items in musicians than in nonmusicians.

Neus Ramos-Escobar,  Emma Segura, Guillem Olivé, Antoni Rodriguez-Fornells, & Clément François

Developmental Cognitive Neuroscience, 51: 101010

When learning a new language, one must segment words from continuous speech and associate them with meanings. These complex processes can be boosted by attentional mechanisms triggered by multi-sensory information. Previous electrophysiological studies suggest that brain oscillations are sensitive to different hierarchical complexity levels of the input, making them a plausible neural substrate for speech parsing. Here, we investigated the functional role of brain oscillations during concurrent speech segmentation and meaning acquisition in sixty 9-year-old children. We collected EEG data during an audio-visual statistical learning task during which children were exposed to a learning condition with consistent word-picture associations and a random condition with inconsistent word-picture associations before being tested on their ability to recall words and word-picture associations. We capitalized on the brain dynamics to align neural activity to the same rate as an external rhythmic stimulus to explore modulations of neural synchronization and phase synchronization between electrodes during multi-sensory word learning. Results showed enhanced power at both word- and syllabic-rate and increased EEG phase synchronization between frontal and occipital regions in the learning compared to the random condition. These findings suggest that multi-sensory cueing and attentional mechanisms play an essential role in children’s successful word learning.

Dietrich Stout, Thierry Chaminade, Jan Apel, Ali Shafti & A. Aldo Faisal

Scientific Reports, 11: 13720

Xiaowei Song,  Pamela García-Saldivar,  Nathan Kindred,  Yujiang Wang,  Hugo Merchant,  Adrien Meguerditchian,  Yihong Yang,  Elliot Stein,  Charles Bradberry, Suliann Hamed, Hank Jedema, & Colline Poirier

NeuroImage, 236, 118009.

Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.