Biology subjects

Trebuchon, A.

Publications and source records attributed to Trebuchon, A..

17 recordsLinked to original sources

bioRxiv2026

Cortical encoding of probabilistic temporal predictions during speech perception

The temporal structure of speech has traditionally been characterized by the rhythmicity of its canonical linguistic units (phonemes, syllables, words), each summarized by a mean occurrence rate. While valid, this view overlooks whether speech carries a finer, context-dependent and probabilistic temporal structure that could support temporal predictive coding during listening. Using large French and English speech corpora, we trained models of increasing complexity to predict the onsets of linguistic units. Recurrent neural networks (RNNs) outperform mean-rate and hazard-rate models, showing that the variability around these rates is not noise but a temporal structure shaped by local context, statistically predictable across phonemes, syllables and words. Recording from 7,698 intracerebral electrodes in 53 neurosurgical patients listening to natural speech, we next show that the models' output), the continuous probability of an upcoming onset (when), explains neural activity beyond acoustic and linguistic content (what) features, with markedly stronger effects for RNNs than for mean- or hazard-rate models. This dynamic neural prediction of when an onset will occur is dissociable from the encoding of linguistic content, relying on largely distinct channel populations. Temporal predictions engage a distributed cortical network extending from bilateral temporal cortex into left frontal and sensorimotor regions. Together, these results establish temporal prediction in speech as a dynamic, context-dependent and probabilistic process in its own right.

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bioRxiv2026

Surprise gates two distinct mechanisms to support memorability in music

Music is a uniquely memorable human creation that, when skillfully composed, can persist in individual memory (as in e.g., earworms) and cultural transmission (e.g. global hits or anthems). While both acoustic and statistical properties are known to influence a songs memorability, the neural mechanisms that facilitate the engramming of certain musical sequences remain unclear. Current theories suggest that memory systems function as predictive internal models, enhancing learning when expectations are violated. Yet expected stimuli, by aligning with and reinforcing prior knowledge, also enhance memorability. How these two opposing processes arise from the brains sensitivity to statistical regularities, especially in naturalistic sequences, is not well understood. Here, we leveraged musics intrinsic balance between expectancy and surprise, and examined the neural correlates of minutes-scale memorability using intracranial EEG recordings from nine patients with epilepsy performing a musical memory task. Quantifying the statistical surprise of each musical excerpt with PolyRNN, a polyphonic model of musical expectations, we uncovered a U-shaped relationship between musical surprise and memory performance: both highly expected and highly surprising melodies led to greater memorability. While neural pattern similarity between song repetitions was enhanced for low-surprise stimuli, high-surprise stimuli enhanced neural pattern separability in medial temporal regions, each mediating memorability in distinguishable ways. These findings reveal complementary neural mechanisms through which statistical structure shapes musical sequence memory, clarifying how the brain encodes complex, ecologically valid stimuli.

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bioRxiv2025

Coupled rhythms in early auditory cortex mirror speech acoustics

Theta and gamma neural dynamics dominate the human auditory cortex during speech perception and have been proposed to track syllable boundaries and encode phonemic information, respectively. To what extent these rhythms are intrinsically generated or imposed by speech acoustics remains unsolved. Applying analytic methods from neuroscience to speech audio corpora from 17 languages, we found that canonical brain features --theta, gamma, and their phase-amplitude coupling-- are a robust and specific acoustic signature of speech envelope across languages. They represent syllabic rate (2-6 Hz), vocalic features (30-50 Hz), and fundamental frequency (100-150 Hz). Intracerebral (sEEG) recordings from the auditory cortex of 18 epilepsy patients revealed that theta-gamma dynamics and their coupling are absent at rest. They emerge during speech perception and are linearly driven by the acoustic envelope, consistent with an evoked origin. Nevertheless, these responses originate from distinct yet functionally interconnected neural populations, indicating that the early auditory cortex demultiplexes speech timescales. Thus, early auditory cortex mirrors theta-gamma speech rhythms across segregated neural populations. IN BRIEFEarly auditory cortex demultiplexes speech: stimulus-locked gamma activity aligns theta phase with the nested acoustic structure, functionally organising speech timescales

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bioRxiv2024

Multi-stream predictions in human auditory cortex during natural music listening

Musical expectations shape how we perceive and process music, yet current computational models are limited to monophonic or simplified stimuli. The study of the neural processes underlying musical expectations in real-world music therefore requires significant advances in our statistical modeling of these stimuli. We present PolyRNN, a recurrent neural network designed to model expectations in naturalistic, polyphonic music. We recorded neurophysiological activity non invasively (MEG) and within the human brain (intracranial EEG) while participants listened to naturally expressive piano recordings. The musical expectations estimated by the model are encoded in evoked P2- and P3-like components in auditory regions. Comparing PolyRNN to a state-of-the-art generative music model, we show that piano roll representations are best suited to represent expectations in polyphonic contexts. Overall, our approach provides a new way to capture the musical expectations emerging from natural music listening, and enables the study of predictive processes in more ecologically valid settings.

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bioRxiv2024

Revealing the co-existence of written and spoken language coding neural populations in the left-ventral occipitotemporal cortex

Reading relies on the ability to map written symbols with speech sounds. The left ventral occipitotemporal cortex (left-vOT) plays a crucial role in this process. Through the automatization of the mapping ability, this specific part of the ventral visual pathway (a.k.a., the Visual Word Form Area) progressively becomes specialized in written word recognition. Yet, despite its key role in reading, the area also responds to speech. This observation raises questions about the actual nature of neural representations encoded in the left-vOT and, therefore, the underlying mechanism of the cross-modal responses. Here, we addressed this issue by applying fine-grained analyses of within- and cross-modal repetition suppression effects (RSEs) and Multi-Voxel Pattern Analyses in fMRI and sEEG experiments. Convergent evidence across analysis methods and protocols showed significant RSEs and successful decoding in both within-modal visual and auditory conditions suggesting that subpopulations of neurons within the left-vOT distinctly encode written and spoken language inputs. This functional organization of neural populations enables the area to respond to speech input directly and indirectly, i.e., after speech sounds are converted to orthographic representations. The finding opens further discussions on how the human brain may be prepared and adapted for an acquisition of a complex ability such as reading. Significance StatementLearning to read generates new functional responses in neurons in the left ventral visual pathway. Soon after reading acquisition, these neurons become specialized in processing known scripts, thus leading to the functional designation of the "Visual Word Form Area" (VWFA). However, controversies remain regarding the nature of neural representations encoded in this "reading" region, as its activation to speech is also reported. We investigate the neural mechanism(s) underlying these bimodal responses using within and cross-modal repetition suppression and decoding protocols. fMRI and sEEG experiments provided converging evidence indicating that, despite its specialization in reading, VWFA also contained subpopulations of neurons that encode speech. This functional organization could reveal why neurons at this anatomical location are ideal for reading acquisition.

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bioRxiv2024

States of functional connectivity flow and their multiplex dynamics in human epilepsy and postictal aphasia

We present a methodological framework for analysing multi-frequency dynamic functional connectivity (dFC) in electrophysiological recordings. The approach characterises not only the magnitude of network reconfiguration over time, but also whether these changes are spatially random or, instead, spatially organised in ways that drive a slower reconfiguration of modular structure. We define a generative null model of multi-scale connectivity fluctuations that differ in their degree of spatiotemporal organisation, and we describe dFC flows through the joint assessment of (i) instantaneous reconfiguration speed and (ii) the extent and quality of ongoing modular reorganisation. Different combinations of these features delineate distinct "flow styles", ranging from more liquid to more frozen dynamics. As a case study, we apply this framework to SEEG recordings from epileptic patients. We identify transitions between dynamic "allegiance states", whose flow styles closely mirror those of the null model. Seizure onset is associated with a pronounced slowing of dFC-speed, while a specific post-ictal regime combines low speed with highly frozen allegiance, and aligns most strongly with clinician-annotated aphasia. These pilot results suggest that temporal multiplex network analyses can reveal transient, frequency-specific network regimes linked to symptom expression and offers a generalisable tool for dissecting fast network dynamics in intracranial recordings. Author SummaryCognitive functions rely on the brains capacity to continually reorganize interactions among neuronal populations. Capturing this flexibility requires describing not only average functional network structure but also how it evolves over time. Using time- and frequency-resolved coherence from intracranial recordings in epilepsy patients, we model dynamic functional connectivity as a temporal multiplex network spanning multiple frequency bands. We introduce a frame-work that distinguishes different "styles" of network reconfiguration--faster or slower, and more structured or more random.

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bioRxiv2024

Bridging verbal coordination and neural dynamics

AbstractOur use of language, which is profoundly social in nature, essentially takes place in interactive contexts and is shaped by precise coordination dynamics that interlocutors must observe. Thus, language interaction is highly demanding on fast adjustment of speech production. Here, we developed a real-time coupled-oscillators virtual partner that allows - by changing the coupling strength parameters - to modulate the ability to synchronise speech with a virtual speaker. Then, we recorded the intracranial brain activity of 16 patients with drug-resistant epilepsy while they performed a verbal coordination task with the virtual partner (VP). More precisely, patients had to repeat short sentences synchronously with the VP. This synchronous speech task is efficient to highlight both the dorsal and ventral language pathways. Importantly, combining time-resolved verbal coordination and neural activity shows more spatially differentiated patterns and different types of neural sensitivity along the dorsal pathway. More precisely, high-frequency activity in left secondary auditory regions is highly sensitive to verbal coordinative dynamics, while primary regions are not. Finally, while bilateral engagement was observed in the high-frequency activity of the IFG BA44--which seems to index online coordinative adjustments that are continuously required to compensate deviation from synchronisation-- interpretation of right hemisphere involvement should be approached cautiously due to relatively sparse electrode coverage. These findings illustrate the possibility and value of using a fully dynamic, adaptive and interactive language task to gather deeper understanding of the subtending neural dynamics involved in speech perception, production as well as their interaction.

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bioRxiv2023

GluK2 is a target for gene therapy in drug-resistant Temporal Lobe Epilepsy

ObjectiveTemporal lobe epilepsy (TLE) is characterized by recurrent seizures generated in the limbic system, particularly in the hippocampus. In TLE, recurrent mossy fiber sprouting from dentate gyrus granule cells (DGCs) creates an aberrant epileptogenic network between DGCs which operates via ectopically expressed GluK2/GluK5-containing kainate receptors (KARs). TLE patients are often resistant to anti-seizure medications and suffer significant comorbidities; hence there is an urgent need for novel therapies. Previously we have shown that GluK2 knockout mice are protected from seizures. This study aims at providing evidence that downregulating KARs in the hippocampus using gene therapy reduces chronic epileptic discharges in TLE. MethodsWe combined molecular biology and electrophysiology in rodent models of TLE and in hippocampal slices surgically resected from patients with drug-resistant TLE. ResultsHere we confirmed the translational potential of KAR suppression using a non-selective KAR antagonist that markedly attenuated Interictal-like Epileptiform Discharges (IEDs) in TLE patient-derived hippocampal slices. An adeno-associated virus (AAV) serotype-9 vector expressing anti-grik2 miRNA was designed to specifically downregulate GluK2 expression. Direct delivery of AAV9-anti grik2 miRNA into the hippocampus of TLE mice led to a marked reduction in seizure activity. Transduction of TLE patient hippocampal slices reduced levels of GluK2 protein and, most importantly, significantly reduced IEDs. InterpretationOur gene silencing strategy to knock down aberrant GluK2 expression demonstrates inhibition of chronic seizure in a mouse TLE model and IEDs in cultured slices derived from TLE patients. These results provide proof-of-concept for a gene therapy approach targeting GluK2 KARs for drug-resistant TLE patients.

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bioRxiv2023

Dimensionality and ramping: Signatures of sentence integration in the dynamics of brains and deep language models

A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: First, the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation, and second, this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and Jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous magneto-encephalography and intracranial electro-encephalography. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than Jabberwocky. Furthermore, multivariate decoding of normal versus Jabberwocky confirmed three dynamic patterns: (i) a phasic pattern following each word, peaking in temporal and parietal areas, (ii) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri, and (iii) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition. Significance statementStarting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multi-word sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep Neural Language Models, artificial neural networks trained on text and performing very well on many Natural Language Processing tasks. Then, using a unique combination of magnetoencephalography and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.

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bioRxiv2022

Independent component analysis reveals an early hippocampal recognition system using intracerebral evoked potentials in humans

The role of the hippocampal formation in memory recognition has been well studied in animals, with different pathways and structures linked to specific memory processes. In contrast, the hippocampus is commonly analyzed as a unique responsive area in most electrophysiological studies in humans, and the specific activity of its subfields remains unexplored. We combined intracerebral electroencephalogram recordings from epileptic patients with independent component analysis (ICA) during a memory recognition task involving the recognition of old and new images to disentangle the activities of multiple neuronal sources within the hippocampus. We identified two sources with different responses emerging from the hippocampus: a fast one (maximum at [~]250 ms) that could not be directly identified from raw recordings, and a later one, peaking at [~]400 ms. The earliest component was found in 12 out of 15 electrodes, with different amplitudes for old and new items in half of the electrodes. The latter component, identified in 13 out of 15 electrodes, had different delays for each condition, with a faster activation ([~]290 ms after stimulus onset) for recognized items. We hypothesize that both sources represent two steps of hippocampal memory recognition, the faster reflecting the input from other structures and the latter the hippocampal internal processing. Recognized images evoking early activations would facilitate neural computation in the hippocampus, accelerating memory retrieval of complementary information. Overall, our results suggest that hippocampal activity is composed by several sources, including an early system for memory recognition, that can be disentangled with ICA methods. SIGNIFICANCE STATEMENTIn the human memory circuit, the hippocampus is considered as a relatively late structure, associated to the retrieval of elaborated memories. In most electrophysiological studies, it is analyzed as a unique responsive area, and the specific activity of its subfields remains unexplored. In this work, we combined intracerebral recordings with independent component analysis to separate the electrophysiological activity from two different substructures of the hippocampus. We analyzed the responses of both sources in a memory task involving the recognition of old and new images. Our results revealed new hippocampal dynamics associated to different subfields, with memory recognition occurring much faster than previously reported. Importantly, we confirmed the potential of independent component analysis, which can be extended to other brain areas.

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bioRxiv2022

Speech and music recruit frequency-specific distributed and overlapping cortical networks

To what extent does speech and music processing rely on domain-specific and domain-general neural networks? Using whole-brain intracranial EEG recordings in 18 epilepsy patients listening to natural, continuous speech or music, we investigated the presence of frequency-specific and network-level brain activity. We combined it with a statistical approach in which a clear operational distinction is made between shared, preferred, and domain-selective neural responses. We show that the majority of focal and network-level neural activity is shared between speech and music processing. Our data also reveal an absence of anatomical regional selectivity. Instead, domain-selective neural responses are restricted to distributed and frequency-specific coherent oscillations, typical of spectral fingerprints. Our work highlights the importance of considering natural stimuli and brain dynamics in their full complexity to map cognitive and brain functions.

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bioRxiv2022

Magnetoencephalography can reveal deep brain network activities linked to memory processes

Recording from deep neural structures such as hippocampus non-invasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large-scale neocortical networks. In the present study, we disentangled mesial activity and large-scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with SEEG signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

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bioRxiv2021

Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice

Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of participants (n = 30), while they were performing interleaved quiz and choice tasks. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that 1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, 2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain versus loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the comparison of safe and risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.

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bioRxiv2020

Orientation of Temporal Interference for Non-Invasive Deep Brain Stimulation in Epilepsy

In patients with focal drug-resistant epilepsy, electrical stimulation from intracranial electrodes is frequently used for the localization of seizure onset zones and related pathological networks. The ability of electrically stimulated tissue to generate beta and gamma range oscillations, called rapid-discharges, is a frequent indication of an epileptogenic zone. However, a limit of intracranial stimulation is the fixed physical location and number of implanted electrodes, leaving numerous clinically and functionally relevant brain regions unexplored. Here, we demonstrate an alternative technique relying exclusively on nonpenetrating surface electrodes, namely an orientation-tunable form of temporally-interfering (TI) electric fields to target the CA3 of the mouse hippocampus which focally evokes seizure-like events (SLEs) having the characteristic frequencies of rapid-discharges, but without the necessity of the implanted electrodes. The orientation of the topical electrodes with respect to the orientation of the hippocampus is demonstrated to strongly control the threshold for evoking SLEs. Additionally, we demonstrate the use of square waves as an alternative to sine waves for TI stimulation. An orientation-dependent analysis of classic implanted electrodes to evoke SLEs in the hippocampus is subsequently utilized to support the results of the minimally-invasive temporally-interfering fields. The principles of orientation-tunable TI stimulation seen here can be generally applicable in a wide range of other excitable tissues and brain regions, overcoming several limitations of fixed electrodes which penetrate tissue.

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bioRxiv2019

Persistent neural entrainment in the human cortex is frequency selective

Cortical oscillations have been proposed to play a functional role in speech and music perception, attentional selection and working memory, via the mechanism of neural entrainment. One of the most compelling arguments for neural entrainment is that its modulatory effect on ongoing oscillations outlasts rhythmic stimulation. We tested the existence of this phenomenon by studying cortical neural oscillations during and after presentation of melodic stimuli in a passive perception paradigm. Melodies were composed of [~]60 and [~]80 Hz tones embedded in a 2.5 Hz stream. Using intracranial and surface recordings in humans, we reveal consistent neural response properties throughout the cortex, well beyond the auditory regions. Persistent oscillatory activity in the high-gamma band was observed in response to the tones. By contrast, in response to the 2.5 Hz stream, no persistent activity in any frequency band was observed. We further show that our data are well-captured by a model of damped harmonic oscillator and can be classified into three classes of neural dynamics, with distinct damping properties and eigenfrequencies. This model provides a mechanistic and quantitative explanation of the frequency selectivity of auditory neural entrainment in the human cortex. Significance statementIt has been proposed that the functional role of cortical oscillations is subtended by a mechanism of entrainment, the synchronisation in phase or amplitude of neural oscillations to a periodic stimulation. We tested whether the modulatory effect on ongoing oscillations outlasts the rhythmic stimulation, a phenomenon considered to be one of the most compelling arguments for entrainment. Using intracranial and surface recordings of human listening to rhythmic auditory stimuli, we reveal consistent oscillatory responses throughout the cortex, with persistent activity of high-gamma oscillations. On the contrary, neural oscillations do not outlast low-frequency acoustic dynamics. We interpret our results as reflecting harmonic oscillator properties - a model ubiquitous in physics but rarely used in neuroscience.

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bioRxiv2019

Converging intracortical signatures of two separated processing timescales in human early auditory cortex.

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.\n\nAuthor summaryLike most animal vocalizations, speech results from a pseudo-rhythmic process that reflects the convergence of motor and auditory neural substrates and the natural resonance properties of the vocal apparatus towards efficient communication. Here, we leverage the excellent temporal and spatial resolution of intracranial EEG to demonstrate that neural activity in human early auditory cortical areas during speech perception exhibits a dual-scale spectral profile of power changes, with speech increasing power in low (delta-theta) and high (gamma - high-gamma) frequency ranges, while decreasing power in intermediate (alpha-beta) frequencies. Single-trial multivariate decoding also resulted in a bimodal spectral profile of information content, with better decoding at low and high frequencies than at intermediate ones. From both spectral and informational perspectives, these patterns are consistent with the activity of a relatively simple computational model comprising two reciprocally connected excitatory/inhibitory sub-networks operating at different (low and high) timescales. By combining experimental, decoding and modeling approaches, we provide consistent evidence for the existence, information coding value and underlying neuronal architecture of dual timescale processing in human auditory cortex.

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bioRxiv2019

Asymmetric sampling in human auditory cortex reveals spectral processing hierarchy

Speech perception is mediated by both left and right auditory cortices, but with differential sensitivity to specific acoustic information contained in the speech signal. A detailed description of this functional asymmetry is missing, and the underlying models are widely debated. We analyzed cortical responses from 96 epilepsy patients with electrode implantation in left or right primary, secondary, and/or association auditory cortex. We presented short acoustic transients to reveal the stereotyped spectro-spatial oscillatory response profile of the auditory cortical hierarchy. We show remarkably similar bimodal spectral response profiles in left and right primary and secondary regions, with preferred processing modes in the theta ([~]4-8 Hz) and low gamma ([~]25-50 Hz) ranges. These results highlight that the human auditory system employs a two-timescale processing mode. Beyond these first cortical levels of auditory processing, a hemispheric asymmetry emerged, with delta and beta band ([~]3/15 Hz) responsivity prevailing in the right hemisphere and theta and gamma band ([~]6/40 Hz) activity in the left. These intracranial data provide a more fine-grained and nuanced characterization of cortical auditory processing in the two hemispheres, shedding light on the neural dynamics that potentially shape auditory and speech processing at different levels of the cortical hierarchy.\n\nAuthor summarySpeech processing is now known to be distributed across the two hemispheres, but the origin and function of lateralization continues to be vigorously debated. The asymmetric sampling in time (AST) hypothesis predicts that (1) the auditory system employs a two-timescales processing mode, (2) present in both hemispheres but with a different ratio of fast and slow timescales, (3) that emerges outside of primary cortical regions. Capitalizing on intracranial data from 96 epileptic patients we sensitively validated each of these predictions and provide a precise estimate of the processing timescales. In particular, we reveal that asymmetric sampling in associative areas is subtended by distinct two-timescales processing modes. Overall, our results shed light on the neurofunctional architecture of cortical auditory processing.

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