Background Recent advances in equipment miniaturization have led to low-cost, portable electroencephalography (EEG) systems that facilitate data collection in real-world settings and with larger samples. Although wireless EEG systems were originally developed for non-research applications, recent studies have provided valuable information to help researchers make informed choices, particularly about participant comfort, mobility during recordings, and data validity. This study aimed to assess the impact of portability by comparing the performance of portable consumer- and research-grade systems (EPOC Saline Flex, EM; LiveAmp, LA) with fixed research-grade systems (BrainAmp, BA). Method Continuous EEG was recorded with each system in healthy adults performing five benchmark tasks in fundamental and clinical cognitive neuroscience. Mental states (alpha power variations in open/closed eyes) and unconscious perception (steady-state visual evoked potential, SSVEP) were analyzed through time/frequency methods, while active (N200 and P300 components during active listening and N170 component during face recognition) and passive cognitive processes (Mismatch negativity, MMN component during passive listening) were examined using time/amplitude analyses (event-related potential, ERPs). Our analyses compared system efficiency at native and equalized sampling rates and examined 100%, 75%, and 50% of the datasets to determine the required trial number for satisfactory signal quality. Results Despite the smaller amount of signal retained for EM, all systems recorded the expected resting state alpha power decrease and SSVEP responses, with EM showing lower spectral effects ([EM < (LA≈BA)]). ERPs for active (N170, N200, P300) and passive (MMN) processes emerged across all systems, with EM and LA showing lower amplitudes only for N170 compared to BA. Furthermore, the dataset reduction resulted in a decreased N170 at P7 only for EM ([EM < LA < BA]). EM also exhibited shorter latencies for all ERPs except for MMN. Conclusion This study provides concrete guidance for designing EEG experiments in real-world settings, with significant potential for investigating children and vulnerable populations. The efficiency of the three EEG systems is more influenced by task duration than sampling rates. A wireless EEG device, such as the EM, can effectively support both time/frequency and time/amplitude analyses in cognitive science, provided that the number of trials is sufficient and latencies are controlled.

Pitch change detection and pitch memory are behaviorally impaired in congenital amusia. Yet rather preserved mismatch negativity (MMN) to unexpected pitch changes has previously been reported using EEG, suggesting a discrepancy between conscious and preattentive pitch perception in this population. Coupling MEG with EEG, our study re-examined MMN in congenital amusia in light of two factors: stimulus onset asynchrony (SOA) and change size of the deviant. Individuals with and without congenital amusia passively listened to oddball sequences with either frequency (pitch) or intensity (loudness) deviants, using short (500 ms) or long (1500 ms) SOAs and small or large changes (0.25 or 2 semitones; -5 or -15 dB). In a subsequent active change detection task, participants with amusia had impaired detection of small frequency changes, while a smaller group difference was found for small intensity changes. Long SOAs increased amusics' behavioral response times more than those of controls for frequency and intensity deviants. Time courses of source data in MEG revealed decreased amplitude and increased latency of MMNs to frequency deviants in right temporal and right frontal cortices in amusia, across all the tested SOAs and change sizes. Some MMN abnormalities were found in amusic participants also for intensity deviants, across all the tested SOAs and change sizes. Thanks to the sensitivity of MEG, this study pinpoints that the right-sided fronto-temporal anomalies characterizing amusia are linked to modifications in the processing of sounds in sequences, most particularly for the pitch dimension, already at the preattentive level.

Magneto-encephalography (MEG) provides a higher spatial resolution than electro-encephalography (EEG) to measure human auditory responses. However, conventional cryogenic MEG systems (SQUID-MEG) suffer from severe technological restrictions limiting, for instance, routine clinical use. Fortunately, a new generation of MEG sensors, optically pumped magnetometers (OPMs), has been developed to bridge the gap, combining the wearability of EEG with the benefits of MEG signal acquisition. We aim to assess their potential for studying auditory mismatch processing. The auditory mismatch negativity (MMN) is a well-characterized evoked component observable using a passive oddball paradigm with two-tone sound sequences. It has been extensively described using both EEG and MEG and is part of many EEG-based clinical applications, such as the assessment of patients with disorders of consciousness. MMN is therefore a relevant candidate to evaluate OPM performance. We use recently developed Helium-OPMs, which are high dynamic range MEG sensors that operate at room temperature. We compare their performance with cryogenic SQUID-MEG and EEG in a passive frequency oddball paradigm. Results show a significant MMN across subjects in all modalities as well as a high temporal similarity between modalities. Signal-to-noise ratios were also similar, and detection of significant individual MMN (within-subjects) using the OPM system was equal to or better than EEG. Given that the OPM system tested here is a prototype comprised of only five sensors, these results are a promising step towards wearable MEG that combines the advantages of MEG and EEG.

The neural substrates supporting the beneficial effect of sleep on motor memory consolidation are well described. However, less is known about the brain oscillatory dynamics underlying these processes. We characterized the oscillatory dynamics associated with motor sequence learning and their modulation by post-learning sleep using magnetoencephalography (MEG) in young healthy adults. After learning a motor sequence task while their brain activity was recorded with MEG, participants were distributed in two groups according to whether they slept or were totally sleep deprived during the first post-training night. Consolidation was assessed with a retest in the MEG three days after training. Behaviorally, performance improved over the consolidation interval irrespective of whether sleep was afforded during the first night. MEG results showed that initial motor sequence learning was characterized by a progressive decrease in beta Event Related Desynchronization (ERD, 18-25Hz) over bilateral motor areas. Interestingly, while these practice-related modulations of beta ERD were not influenced by the sleep status, post-learned-movement beta Event Related Synchronization (ERS) over bilateral parietal areas increased over the consolidation interval in the sleep, compared to the sleep deprived, group. These results extend current models of motor memory consolidation by identifying ERS as an oscillatory marker of sleep-dependent consolidation. ### Competing Interest Statement The authors have declared no competing interest.

Mismatch negativity (MMN) has been frequently used to assess auditory processing and change detection in autism spectrum disorder (ASD), but findings have been fairly inconsistent. To address this issue, we conducted a systematic review and meta-analysis of MMN amplitude (76 effect sizes) and latency (62 effect sizes) in ASD to identify factors contributing to this heterogeneity and to interpret findings within the predictive coding framework. While residual heterogeneity remained, significant effects of the interaction between age group and design type (unifeature vs. multifeature, i.e., one or several types of deviants) and deviant type were found for MMN amplitude. In multifeature designs, autistic children and adolescents exhibited reduced MMN amplitudes compared to neurotypical peers (g = 0.25, p = 0.01), whereas autistic adults showed increased MMN amplitudes (g = -0.26, p = 0.02). In addition, autistic individuals had significantly smaller MMN amplitudes than neurotypical individuals in paradigms using phoneme deviants (g = 0.41, p < 0.001). Across designs, no significant MMN latency differences were observed between neurotypical and autistic individuals. These results are discussed within the predictive coding framework, as MMN responses are thought to reflect prediction errors, aligning with theories suggesting heightened prediction errors in autistic adults. Future studies with larger samples and improved data reporting are needed to further clarify the developmental trajectory and variability of MMN responses in ASD. Additionally, computational modeling approaches can help characterize learning dynamics and disentangle predictive coding accounts among autistic individuals.

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.

Error signals are the cornerstone of predictive coding and are widely considered essential to sensory perception and beyond. The mismatch negativity (MMN) is arguably the most emblematic and most studied brain error signal. It is affected in many brain disorders. However, its precise algorithmic function and the underlying physiology remain mysterious. Over the past decade, theoretical and computational explanations have been put forward. They highlight a paradox: the MMN is considered a signature of context-dependent perceptual learning, although it is defined as an evoked response averaged across trials, thus neglecting the information carried by error signal fluctuations over time. We propose to deconstruct the MMN, by virtue of hypothesis driven computational approaches whose aim it to account for these fluctuations.

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.

The function of the postsynaptic compartment is based on the presence and activity of postsynaptic receptors, whose dynamics are controlled by numerous scaffolding, signaling and trafficking proteins. Although the receptors and the scaffolding proteins have received substantial attention, the trafficking proteins have not been investigated extensively. Their mobility rates are unknown, and it is unclear how the postsynaptic environment affects their dynamics. To address this, we analyzed several trafficking proteins (α-synuclein, amphiphysin, calmodulin, doc2a, dynamin, and endophilin), estimating their movement rates in the dendritic shaft, as well as in morphologically distinct "mushroom" and "stubby" postsynapse types. The diffusion parameters were surprisingly similar across dendritic compartments, and a few differences between proteins became evident only in the presence of a synapse neck. We conclude that the movement of trafficking proteins is not strongly affected by the postsynaptic compartment, in stark contrast to the presynapse, which regulates strongly the movement of such proteins.

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.

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.

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.

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.

A bstract 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 MEG/EEG were recorded. Event-related potentials and fields (ERPs/ERFs) were processed and source reconstruction was applied to ERFs. 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. H ighlights Migraineurs performed as well as healthy participants in an attention task. However, EEG markers of both bottom-up and top-down attention are increased. Migraine is also associated with a facilitated recruitment of the right temporo-parietal junction.

In BCI, artifact removal remains an acute challenge. Filtering must be efficient in removing artifacts while preserving relevant features, e.g. eventrelated potentials (ERP) like the mismatch negativity (MMN). MMN is a prediction error signal whose modulations reflect human perceptual inference and learning. Characterizing these subtle processes requires fitting non-linear models onto single-trial data. And disentangling between alternative models is challenging because of a low signal-to-noise ratio. We evaluated four methods for online artifact removal. We mimicked online data processing using real electroencephalography (EEG) data from an auditory oddball paradigm. We compared the four approaches with standard offline analysis, in their ability to reveal (i) the MMN, (ii) the MMN modulations by the manipulation of the predictability of a sound sequence and (iii) the most likely learning mechanism at play. Artifact Subspace Reconstruction (ASR) and Empirical Mode Decomposition (EMD) were the most successful. Interestingly, they even proved more sensitive than the offline analysis, likely because they avoid rejecting trials.

Migraine is characterized by a hypersensitivity to environmental stimulation which climaxes during attacks but persists interictally. This multisensory disturbance may arise from a dysfunction of top-down and/or bottom-up attention which would lead to the inability to filter out irrelevant information and a state of sensory overload. We used a recent paradigm to evaluate jointly top-down and bottom-up attention among migraineurs and healthy controls using visually-cued target sounds and unexpected task-irrelevant distracting sounds. Behavioral responses and MEG/EEG were recorded. 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). Based on these results, migraineurs appear to present an increased bottom-up orienting response to all incoming sounds, and an enhanced recruitment of top-down attention. We propose that the interictal state in migraine is characterized by a dysfunction of bottom-up attention and that the hyperfunction of top-down attention acts as a compensatory mechanism enabling them to maintain adequate task-efficiency. These attentional alterations might participate to the disruptions of sensory processing in migraine.

Since their introduction in the late eighties, Bayesian approaches for neuroimaging have opened the way to new powerful and quantitative analysis of brain data. Here, we apply this statistical framework to evaluate empirically the gain of fused EEG-MEG source reconstruction, compared to unimodal (EEG or MEG) one. Combining EEG and MEG information for source reconstruction has been consistently evidenced to enhance localization performances using simulated data. However, given considerable efforts to conduct simultaneous recordings, empirical evaluation becomes necessary to quantify the real information gain. And this is obviously not straightforward due to the ill-posedness of the inverse problem. Here, we consider Bayesian model comparison to quantify the ability of EEG, MEG and fused (EEG/MEG) inversions of data to resolve spatial source models. These models consisted in cortical distributions inferred from real EEG, MEG and EEG/MEG brain responses. We applied this comparative evaluation to the timely issue of the generators of auditory mismatch responses evoked by unexpected sounds. These included the well-known (MMN) but also earlier deviance responses. As expected, fused localization was evidenced to outperform unimodal inversions with larger model separability. The present methodology confirms with real data the theoretical interest of simultaneous EEG/MEG recordings and fused inversion to highly inform (spatially and temporally) source modeling. Precisely, a bilateral fronto-temporal network could be identified for both the MMN and early deviance response. Interestingly, multimodal inversions succeeded in revealing spatio-temporal details of the functional organization within the supratemporal plane that have not been reported so far, nor were visible here with unimodal inversions. The present refined auditory network could serve as priors for auditory modeling studies.