GABAergic inhibition shapes behavior and neural dynamics in human visual working memory.

Neuronal inhibition, primarily mediated by GABAergic neurotransmission, is crucial for brain development and healthy cognition. Gamma-aminobutyric acid concentration levels in sensory areas have been shown to correlate with hemodynamic and oscillatory neuronal responses. How these measures relate to one another during working memory, a higher-order cognitive process, is still poorly understood. We address this gap by collecting magnetoencephalography, functional magnetic resonance imaging, and Flumazenil positron emission tomography data within the same subject cohort using an n-back working-memory paradigm. By probing the relationship between GABAA receptor distribution, neural oscillations, and Blood Oxygen Level Dependent (BOLD) modulations, we found that GABAA receptor density in higher-order cortical areas predicted the reaction times on the working-memory task and correlated positively with the peak frequency of gamma power modulations and negatively with BOLD amplitude. These findings support and extend theories linking gamma oscillations and hemodynamic responses to gamma-aminobutyric acid neurotransmission and to the excitation-inhibition balance and cognitive performance in humans. Considering the small sample size of the study, future studies should test whether these findings also hold for other, larger cohorts as well as to examine in detail how the GABAergic system and neural fluctuations jointly support working-memory task performance.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response.

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

Neurocomputational Underpinnings of Expected Surprise.

Predictive coding accounts of brain functions profoundly influence current approaches to perceptual synthesis. However, a fundamental paradox has emerged, that may be very relevant for understanding hallucinations, psychosis, or cognitive inflexibility: in some situations, surprise or prediction error-related responses can decrease when predicted, and yet, they can increase when we know they are predictable. This paradox is resolved by recognizing that brain responses reflect precision-weighted prediction error. This presses us to disambiguate the contributions of precision and prediction error in electrophysiology. To meet this challenge for the first time, we appeal to a methodology that couples an original experimental paradigm with fine dynamic modeling. We examined brain responses in healthy human participants (N = 20; 10 female) to unexpected and expected surprising sounds, assuming that the latter yield a smaller prediction error but much more amplified by a larger precision weight. Importantly, addressing this modulation requires the modeling of trial-by-trial variations of brain responses, that we reconstructed within a fronto-temporal network by combining EEG and MEG. Our results reveal an adaptive learning of surprise with larger integration of past (relevant) information in the context of expected surprises. Within the auditory hierarchy, this adaptation was found tied down to specific connections and reveals in particular precision encoding through neuronal excitability. Strikingly, these fine processes are automated as sound sequences were unattended. These findings directly speak to applications in psychiatry, where specifically impaired precision weighting has been suggested to be at the heart of several conditions such as schizophrenia and autism.SIGNIFICANCE STATEMENT In perception as Bayesian inference and learning, context sensitivity expresses as the precision weighting of prediction errors. A subtle mechanism that is thought to lie at the heart of several psychiatric conditions. It is thus critical to identify its neurophysiological and computational underpinnings. We revisit the passive auditory oddball paradigm by manipulating sound predictability and use a twofold modeling approach to simultaneous EEG-MEG recordings: (1) trial-by-trial modeling of cortical responses reveals a context-sensitive perceptual learning process; (2) the dynamic causal modeling (DCM) of evoked responses uncovers the associated changes in synaptic efficacy. Predictability discloses a link between precision weighting and self-inhibition of superficial pyramidal (SP) cells, a result that paves the way to a fine description of healthy and pathologic perception.

Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals.

Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.

Empirical Bayes evaluation of fused EEG-MEG source reconstruction: Application to auditory mismatch evoked responses.

We here turn the general and theoretical question of the complementarity of EEG and MEG for source reconstruction, into a practical empirical one. Precisely, we address the challenge of evaluating multimodal data fusion on real data. For this purpose, we build on the flexibility of Parametric Empirical Bayes, namely for EEG-MEG data fusion, group level inference and formal hypothesis testing. The proposed approach follows a two-step procedure by first using unimodal or multimodal inference to derive a cortical solution at the group level; and second by using this solution as a prior model for single subject level inference based on either unimodal or multimodal data. Interestingly, for inference based on the same data (EEG, MEG or both), one can then formally compare, as alternative hypotheses, the relative plausibility of the two unimodal and the multimodal group priors. Using auditory data, we show that this approach enables to draw important conclusions, namely on (i) the superiority of multimodal inference, (ii) the greater spatial sensitivity of MEG compared to EEG, (iii) the ability of EEG data alone to source reconstruct temporal lobe activity, (iv) the usefulness of EEG to improve MEG based source reconstruction. Importantly, we largely reproduce those findings over two different experimental conditions. We here focused on Mismatch Negativity (MMN) responses for which generators have been extensively investigated with little homogeneity in the reported results. Our multimodal inference at the group level revealed spatio-temporal activity within the supratemporal plane with a precision which, to our knowledge, has never been achieved before with non-invasive recordings.

Auditory attention alterations in migraine: A behavioral and MEG/EEG study.

OBJECTIVES: To evaluate alterations of top-down and/or bottom-up attention in migraine and their cortical underpinnings. METHODS: 19 migraineurs between attacks and 19 matched control participants performed a task evaluating jointly top-down and bottom-up attention, using visually-cued target sounds and unexpected task-irrelevant distracting sounds. Behavioral responses and magneto- and electro-encephalography signals were recorded. Event-related potentials and fields were processed and source reconstruction was applied to event-related fields. RESULTS: At the behavioral level, neither top-down nor bottom-up attentional processes appeared to be altered in migraine. However, migraineurs presented heightened evoked responses following distracting sounds (orienting component of the N1 and Re-Orienting Negativity, RON) and following target sounds (orienting component of the N1), concomitant to an increased recruitment of the right temporo-parietal junction. They also displayed an increased effect of the cue informational value on target processing resulting in the elicitation of a negative difference (Nd). CONCLUSIONS: Migraineurs appear to display increased bottom-up orienting response to all incoming sounds, and an enhanced recruitment of top-down attention. SIGNIFICANCE: The interictal state in migraine is characterized by an exacerbation of the orienting response to attended and unattended sounds. These attentional alterations might participate to the peculiar vulnerability of the migraine brain to all incoming stimuli.

EEG artifact correction strategies for online trial-by-trial analysis.

Brain-machine interfaces (BMIs) use brain signals to control closed-loop systems in real-time. This comes with substantial challenges, such as having to remove artifacts in order to extract reliable features, especially when using electroencephalography (EEG). Some approaches have been described in the literature to address online artifact correction. However, none are being used as a 'gold-standard' method, and no research has been conducted to analyze and compare their respective effects on statistical data analysis (inference-based decision). OBJECTIVE: In this paper, we evaluate methods for artifact correction and describe the necessary adjustments to implement them for online EEG data analysis. APPROACH: We investigate the following methods: artifact subspace reconstruction (ASR), fully online and automated artifact removal for brain-computer interfacing (FORCe), online empirical model decomposition (EMD), and online independent component analysis. For assessment, we simulated online data processing using real data from an auditory oddball task. We compared the above methods with classical offline data processing, in their ability (i) to reveal a significant mismatch negativity (MMN) response to auditory stimuli; (ii) to reveal the more subtle modulation of the MMN by contextual changes (namely, the predictability of the sound sequence), and (iii) to identify the most likely learning process that explains the MMN response. MAIN RESULTS: Our results show that ASR and EMD are both able to reveal a significant MMN and its modulation by predictability, and even appear more sensitive than the offline analysis when comparing alternative models of perception underlying auditory evoked responses. SIGNIFICANCE: ASR and EMD show many advantages when compared to other online artifact correction methods. Besides, subtle modulation analysis of the MMN, embedded in perception computational models is a novel method for assessing the quality of artifact correction methods.

Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design.

The relatively young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we argue that, brought together, they may trigger an important shift in the way experimental paradigms are being designed, which should prove fruitful to both endeavors. This change simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new approach as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g., stimuli) during the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new, however, is our ability to perform online processing of the huge amount of complex data that brain imaging techniques provide. This is all the more relevant at a time when physiological and psychological processes are beginning to be approached using more realistic, generative models which may be difficult to tease apart empirically. Based upon Bayesian inference, ASAP proposes a generic and principled way to optimize experimental design adaptively. In this perspective paper, we summarize the main steps in ASAP. Using synthetic data we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its future potential for basic and clinical neuroscience as well as some remaining challenges.

Gamma oscillations in V1 are correlated with GABA(A) receptor density: A multi-modal MEG and Flumazenil-PET study.

High-frequency oscillations in the gamma-band reflect rhythmic synchronization of spike timing in active neural networks. The modulation of gamma oscillations is a widely established mechanism in a variety of neurobiological processes, yet its neurochemical basis is not fully understood. Modeling, in-vitro and in-vivo animal studies suggest that gamma oscillation properties depend on GABAergic inhibition. In humans, search for evidence linking total GABA concentration to gamma oscillations has led to promising -but also to partly diverging- observations. Here, we provide the first evidence of a direct relationship between the density of GABA(A) receptors and gamma oscillatory gamma responses in human primary visual cortex (V1). By combining Flumazenil-PET (to measure resting-levels of GABA(A) receptor density) and MEG (to measure visually-induced gamma oscillations), we found that GABA(A) receptor densities correlated positively with the frequency and negatively with amplitude of visually-induced gamma oscillations in V1. Our findings demonstrate that gamma-band response profiles of primary visual cortex across healthy individuals are shaped by GABA(A)-receptor-mediated inhibitory neurotransmission. These results bridge the gap with in-vitro and animal studies and may have future clinical implications given that altered GABAergic function, including dysregulation of GABA(A) receptors, has been related to psychiatric disorders including schizophrenia and depression.

Implicit learning of predictable sound sequences modulates human brain responses at different levels of the auditory hierarchy.

Deviant stimuli, violating regularities in a sensory environment, elicit the mismatch negativity (MMN), largely described in the Event-Related Potential literature. While it is widely accepted that the MMN reflects more than basic change detection, a comprehensive description of mental processes modulating this response is still lacking. Within the framework of predictive coding, deviance processing is part of an inference process where prediction errors (the mismatch between incoming sensations and predictions established through experience) are minimized. In this view, the MMN is a measure of prediction error, which yields specific expectations regarding its modulations by various experimental factors. In particular, it predicts that the MMN should decrease as the occurrence of a deviance becomes more predictable. We conducted a passive oddball EEG study and manipulated the predictability of sound sequences by means of different temporal structures. Importantly, our design allows comparing mismatch responses elicited by predictable and unpredictable violations of a simple repetition rule and therefore departs from previous studies that investigate violations of different time-scale regularities. We observed a decrease of the MMN with predictability and interestingly, a similar effect at earlier latencies, within 70 ms after deviance onset. Following these pre-attentive responses, a reduced P3a was measured in the case of predictable deviants. We conclude that early and late deviance responses reflect prediction errors, triggering belief updating within the auditory hierarchy. Beside, in this passive study, such perceptual inference appears to be modulated by higher-level implicit learning of sequence statistical structures. Our findings argue for a hierarchical model of auditory processing where predictive coding enables implicit extraction of environmental regularities.

The modulatory influence of a predictive cue on the auditory steady-state response.

Whether attention exerts its impact already on primary sensory levels is still a matter of debate. Particularly in the auditory domain the amount of empirical evidence is scarce. Recently noninvasive and invasive studies have shown attentional modulations of the auditory Steady-State Response (aSSR). This evoked oscillatory brain response is of importance to the issue, because the main generators have been shown to be located in primary auditory cortex. So far, the issue whether the aSSR is sensitive to the predictive value of a cue preceding a target has not been investigated. Participants in the present study had to indicate on which ear the faster amplitude modulated (AM) sound of a compound sound (42 and 19 Hz AM frequencies) was presented. A preceding auditory cue was either informative (75%) or uninformative (50%) with regards to the location of the target. Behaviorally we could confirm that typical attentional modulations of performance were present in case of a preceding informative cue. With regards to the aSSR we found differences between the informative and uninformative condition only when the cue/target combination was presented to the right ear. Source analysis indicated this difference to be generated by a reduced 42 Hz aSSR in right primary auditory cortex. Our and previous data by others show a default tendency of "40 Hz" AM sounds to be processed by the right auditory cortex. We interpret our results as active suppression of this automatic response pattern, when attention needs to be allocated to right ear input.

The neural bases underlying pitch processing difficulties.

Normal listeners are often surprisingly poor at processing pitch changes. The neural bases of this difficulty were explored using magnetoencephalography (MEG) by comparing participants who obtained poor thresholds on a pitch-direction task with those who obtained good thresholds. Source-space projected data revealed that during an active listening task, the poor threshold group displayed greater activity in the left auditory cortical region when determining the direction of small pitch glides, whereas there was no difference in the good threshold group. In a passive listening task, a mismatch response (MMNm) was identified for pitch-glide direction deviants, with a tendency to be smaller in the poor listeners. The results imply that the difficulties in pitch processing are already apparent during automatic sound processing, and furthermore suggest that left hemisphere auditory regions are used by these listeners to consciously determine the direction of a pitch change. This is in line with evidence that the left hemisphere has a poor frequency resolution, and implies that normal listeners may use the sub-optimal hemisphere to process pitch changes.

Visual activation and audiovisual interactions in the auditory cortex during speech perception: intracranial recordings in humans.

Hemodynamic studies have shown that the auditory cortex can be activated by visual lip movements and is a site of interactions between auditory and visual speech processing. However, they provide no information about the chronology and mechanisms of these cross-modal processes. We recorded intracranial event-related potentials to auditory, visual, and bimodal speech syllables from depth electrodes implanted in the temporal lobe of 10 epileptic patients (altogether 932 contacts). We found that lip movements activate secondary auditory areas, very shortly (approximately equal to 10 ms) after the activation of the visual motion area MT/V5. After this putatively feedforward visual activation of the auditory cortex, audiovisual interactions took place in the secondary auditory cortex, from 30 ms after sound onset and before any activity in the polymodal areas. Audiovisual interactions in the auditory cortex, as estimated in a linear model, consisted both of a total suppression of the visual response to lipreading and a decrease of the auditory responses to the speech sound in the bimodal condition compared with unimodal conditions. These findings demonstrate that audiovisual speech integration does not respect the classical hierarchy from sensory-specific to associative cortical areas, but rather engages multiple cross-modal mechanisms at the first stages of nonprimary auditory cortex activation.