Beta bursts question the ruling power for brain-computer interfaces.

Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

The challenge of controlling an auditory BCI in the case of severe motor disability.

BACKGROUND: The locked-in syndrome (LIS), due to a lesion in the pons, impedes communication. This situation can also be met after some severe brain injury or in advanced Amyotrophic Lateral Sclerosis (ALS). In the most severe condition, the persons cannot communicate at all because of a complete oculomotor paralysis (Complete LIS or CLIS). This even prevents the detection of consciousness. Some studies suggest that auditory brain-computer interface (BCI) could restore a communication through a « yes-no¼ code. METHODS: We developed an auditory EEG-based interface which makes use of voluntary modulations of attention, to restore a yes-no communication code in non-responding persons. This binary BCI uses repeated speech sounds (alternating "yes" on the right ear and "no" on the left ear) corresponding to either frequent (short) or rare (long) stimuli. Users are instructed to pay attention to the relevant stimuli only. We tested this BCI with 18 healthy subjects, and 7 people with severe motor disability (3 "classical" persons with locked-in syndrome and 4 persons with ALS). RESULTS: We report online BCI performance and offline event-related potential analysis. On average in healthy subjects, online BCI accuracy reached 86% based on 50 questions. Only one out of 18 subjects could not perform above chance level. Ten subjects had an accuracy above 90%. However, most patients could not produce online performance above chance level, except for two people with ALS who obtained 100% accuracy. We report individual event-related potentials and their modulation by attention. In addition to the classical P3b, we observed a signature of sustained attention on responses to frequent sounds, but in healthy subjects and patients with good BCI control only. CONCLUSIONS: Auditory BCI can be very well controlled by healthy subjects, but it is not a guarantee that it can be readily used by the target population of persons in LIS or CLIS. A conclusion that is supported by a few previous findings in BCI and should now trigger research to assess the reasons of such a gap in order to propose new and efficient solutions. CLINICAL TRIAL REGISTRATIONS: No. NCT02567201 (2015) and NCT03233282 (2013).

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.

Disentangling sensory precision and prior expectation of change in autism during tactile discrimination.

Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precision or weight ascribed to sensory inputs relative to prior expectations. Using three tactile behavioral tasks and computational modeling, we specifically targeted the implicit dynamics of sensory adaptation and perceptual learning in ASD. Participants were neurotypical and autistic adults without intellectual disability. In Experiment I, tactile detection thresholds and adaptation effects were measured to assess sensory precision. Experiments II and III relied on two-alternative forced choice tasks designed to elicit a time-order effect, where prior knowledge biases perceptual decisions. Our results suggest a subtler explanation than a simple imbalance in the prior/sensory weights, having to do with the dynamic nature of perception, that is the adjustment of precision weights to context. Compared to neurotypicals, autistic adults showed no difference in average performance and sensory sensitivity. Both groups managed to implicitly learn and adjust a prior that biased their perception. However, depending on the context, autistic participants showed no, normal or slower adaptation, a phenomenon that computational modeling of trial-to-trial responses helped us to associate with a higher expectation for sameness in ASD, and to dissociate from another observed robust difference in terms of response bias. These results point to atypical perceptual learning rather than altered perceptual inference per se, calling for further empirical and computational studies to refine the current predictive coding theories of ASD.

Diverse beta burst waveform motifs characterize movement-related cortical dynamics.

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

Infraclinical detection of voluntary attention in coma and post-coma patients using electrophysiology.

OBJECTIVE: Early functional evaluation and prognosis of patients with disorders of consciousness is a major challenge that clinical assessments alone cannot solve. Objective measures of brain activity could help resolve this uncertainty. We used electroencephalogram at bedside to detect voluntary attention with a paradigm previously validated in healthy subjects. METHODS: Using auditory-oddball sequences, our approach rests on detecting known attentional modulations of Event Related Potentials that reflect compliance with verbal instructions. Sixty-eight unresponsive patients were tested in their first year after coma onset (37 coma and 31 first year post-coma patients). Their evolution 6 months after the test was considered. RESULTS: Fourteen of the 68 patients, showed a positive response. Nine were in a coma and 5 in a minimally conscious state (MCS). Except for one who died early, all responders evolved to exit-MCS within 6 months (93%), while 35 (65%) among non-responders only. CONCLUSIONS: Among those patients for whom the outcome is highly uncertain, 21% responded positively to this simple but cognitively demanding test. Strikingly, some coma patients were among responders. SIGNIFICANCE: The proposed paradigm revealed cognitive-motor dissociation in some coma patients. This ability to sustain attention on demand predicted awakening within 6 months and represents an immediately useful information for relatives and caregivers.

Tangent space alignment: Transfer learning for Brain-Computer Interface.

Statistical variability of electroencephalography (EEG) between subjects and between sessions is a common problem faced in the field of Brain-Computer Interface (BCI). Such variability prevents the usage of pre-trained machine learning models and requires the use of a calibration for every new session. This paper presents a new transfer learning (TL) method that deals with this variability. This method aims to reduce calibration time and even improve accuracy of BCI systems by aligning EEG data from one subject to the other in the tangent space of the positive definite matrices Riemannian manifold. We tested the method on 18 BCI databases comprising a total of 349 subjects pertaining to three BCI paradigms, namely, event related potentials (ERP), motor imagery (MI), and steady state visually evoked potentials (SSVEP). We employ a support vector classifier for feature classification. The results demonstrate a significant improvement of classification accuracy, as compared to a classical training-test pipeline, in the case of the ERP paradigm, whereas for both the MI and SSVEP paradigm no deterioration of performance is observed. A global 2.7% accuracy improvement is obtained compared to a previously published Riemannian method, Riemannian Procrustes Analysis (RPA). Interestingly, tangent space alignment has an intrinsic ability to deal with transfer learning for sets of data that have different number of channels, naturally applying to inter-dataset transfer learning.

Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI.

OBJECTIVE: Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process especially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance. Notably the use of biased feedback, i.e. non-realistic representation of performance. Benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations, we investigate what personality type, initial state and calibration performance (CP) could benefit from a biased feedback. METHODS: We conduct an experiment (n = 30 for 2 sessions). The feedback provided to each group (n = 10) is either positively, negatively or not biased. RESULTS: Statistical analyses suggest that interactions between bias and: 1) workload, 2) anxiety, and 3) self-control significantly affect online performance. For instance, low initial workload paired with negative bias is associated to higher peak performances (86%) than without any bias (69%). High anxiety relates negatively to performance no matter the bias (60%), while low anxiety matches best with negative bias (76%). For low CP, learning rate (LR) increases with negative bias only short term (LR = 2%) as during the second session it severely drops (LR = -1%). CONCLUSION: We unveil many interactions between said human factors and bias. Additionally, we use prediction models to confirm and reveal even more interactions. SIGNIFICANCE: This paper is a first step towards identifying optimal biased feedback for a personality type, state, and CP in order to maximize BCI performance and learning.

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.

Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference.

Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data.

An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces.

The development of reliable assistive devices for patients that suffer from motor impairments following central nervous system lesions remains a major challenge in the field of non-invasive Brain-Computer Interfaces (BCIs). These approaches are predominated by electroencephalography and rely on advanced signal processing and machine learning methods to extract neural correlates of motor activity. However, despite tremendous and still ongoing efforts, their value as effective clinical tools remains limited. We advocate that a rather overlooked research avenue lies in efforts to question neurophysiological markers traditionally targeted in non-invasive motor BCIs. We propose an alternative approach grounded by recent fundamental advances in non-invasive neurophysiology, specifically subject-specific feature extraction of sensorimotor bursts of activity recorded via (possibly magnetoencephalography-optimized) electroencephalography. This path holds promise in overcoming a significant proportion of existing limitations, and could foster the wider adoption of online BCIs in rehabilitation protocols.

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.

Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders.

STUDY OBJECTIVES: To assess the performance of the single-channel automatic sleep staging (AS) software ASEEGA in adult patients diagnosed with various sleep disorders. METHODS: Sleep recordings were included of 95 patients (38 women, 40.5 ± 13.7 years) diagnosed with insomnia (n = 23), idiopathic hypersomnia (n = 24), narcolepsy (n = 24), and obstructive sleep apnea (n = 24). Visual staging (VS) was performed by two experts (VS1 and VS2) according to the American Academy of Sleep Medicine rules. AS was based on the analysis of a single electroencephalogram channel (Cz-Pz), without any information from electro-oculography nor electromyography. The epoch-by-epoch agreement (concordance and Conger's coefficient [κ]) was compared pairwise (VS1-VS2, AS-VS1, AS-VS2) and between AS and consensual VS. Sleep parameters were also compared. RESULTS: The pairwise agreements were: between AS and VS1, 78.6% (κ = 0.70); AS and VS2, 75.0% (0.65); and VS1 and VS2, 79.5% (0.72). Agreement between AS and consensual VS was 85.6% (0.80), with the following distribution: insomnia 85.5% (0.80), narcolepsy 83.8% (0.78), idiopathic hypersomnia 86.1% (0.68), and obstructive sleep disorder 87.2% (0.82). A significant low-amplitude scorer effect was observed for most sleep parameters, not always driven by the same scorer. Hypnograms obtained with AS and VS exhibited very close sleep organization, except for 80% of rapid eye movement sleep onset in the group diagnosed with narcolepsy missed by AS. CONCLUSIONS: Agreement between AS and VS in sleep disorders is comparable to that reported in healthy individuals and to interexpert agreement in patients. ASEEGA could therefore be considered as a complementary sleep stage scoring tool in clinical practice, after improvement of rapid eye movement sleep onset detection.

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.

Estimates of cortical column orientation improve MEG source inversion.

Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.

Exploring scoring methods for research studies: Accuracy and variability of visual and automated sleep scoring.

Sleep studies face new challenges in terms of data, objectives and metrics. This requires reappraising the adequacy of existing analysis methods, including scoring methods. Visual and automatic sleep scoring of healthy individuals were compared in terms of reliability (i.e., accuracy and stability) to find a scoring method capable of giving access to the actual data variability without adding exogenous variability. A first dataset (DS1, four recordings) scored by six experts plus an autoscoring algorithm was used to characterize inter-scoring variability. A second dataset (DS2, 88 recordings) scored a few weeks later was used to explore intra-expert variability. Percentage agreements and Conger's kappa were derived from epoch-by-epoch comparisons on pairwise and consensus scorings. On DS1 the number of epochs of agreement decreased when the number of experts increased, ranging from 86% (pairwise) to 69% (all experts). Adding autoscoring to visual scorings changed the kappa value from 0.81 to 0.79. Agreement between expert consensus and autoscoring was 93%. On DS2 the hypothesis of intra-expert variability was supported by a systematic decrease in kappa scores between autoscoring used as reference and each single expert between datasets (.75-.70). Although visual scoring induces inter- and intra-expert variability, autoscoring methods can cope with intra-scorer variability, making them a sensible option to reduce exogenous variability and give access to the endogenous variability in the data.

Active inference as a unifying, generic and adaptive framework for a P300-based BCI.

OBJECTIVE: Going adaptive is a major challenge for the field of brain-computer interface (BCI). This entails a machine that optimally articulates inference about the user's intentions and its own actions. Adaptation can operate over several dimensions which calls for a generic and flexible framework. APPROACH: We appeal to one of the most comprehensive computational approach to brain (adaptive) functions: the active inference (AI) framework. It entails an explicit (probabilistic) model of the user that the machine interacts with, here involved in a P300-spelling task. This takes the form of a discrete input-output state-space model establishing the link between the machine's (i) observations-a P300 or error potential for instance, (ii) representations-of the user intentions to spell or pause, and (iii) actions-to flash, spell or switch-off the application. MAIN RESULTS: Using simulations with real EEG data from 18 subjects, results demonstrate the ability of AI to yield a significant increase in bit rate (17%) over state-of-the-art approaches, such as dynamic stopping. SIGNIFICANCE: Thanks to its flexibility, this one model enables to implement optimal (dynamic) stopping but also optimal flashing (i.e. active sampling), automated error correction, and switching off when the user does not look at the screen anymore. Importantly, this approach enables the machine to flexibly arbitrate between all these possible actions. We demonstrate AI as a unifying and generic framework to implement a flexible interaction behaviour in a given BCI context.

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.

The epistemic and pragmatic value of non-action: a predictive coding perspective on meditation.

The surge of interest about mindfulness meditation is associated with a growing empirical evidence about its impact on the mind and body. Yet, despite promising phenomenological or psychological models of mindfulness, a general mechanistic understanding of meditation steeped in neuroscience is still lacking. In parallel, predictive processing approaches to the mind are rapidly developing in the cognitive sciences with an impressive explanatory power: processes apparently as diverse as perception, action, attention, and learning, can be seen as unfolding and being coherently orchestrated according to the single general mandate of free-energy minimization. Here, we briefly explore the possibility to supplement previous phenomenological models of focused attention meditation by formulating them in terms of active inference. We first argue that this perspective can account for how paying voluntary attention to the body in meditation helps settling the mind by downweighting habitual and automatic trajectories of (pre)motor and autonomic reactions, as well as the pull of distracting spontaneous thought at the same time. Secondly, we discuss a possible relationship between phenomenological notions such as opacity and de-reification, and the deployment of precision-weighting via the voluntary allocation of attention. We propose the adoption of this theoretical framework as a promising strategy for contemplative research. Explicit computational simulations and comparisons with experimental and phenomenological data will be critical to fully develop this approach.

Tactile hypersensitivity and GABA concentration in the sensorimotor cortex of adults with autism.

Sensory hypersensitivity is frequently encountered in autism spectrum disorder (ASD). Gamma-aminobutyric acid (GABA) has been hypothesized to play a role in tactile hypersensitivity. The aim of the present study was twofold. First, as a study showed that children with ASD have decreased GABA concentrations in the sensorimotor cortex, we aimed at determining whether the GABA reduction remained in adults with ASD. For this purpose, we used magnetic resonance spectroscopy to measure GABA concentration in the sensorimotor cortex of neurotypical adults (n = 19) and ASD adults (n = 18). Second, we aimed at characterizing correlations between GABA concentration and tactile hypersensitivity in ASD. GABA concentration in the sensorimotor cortex of adults with ASD was lower than in neurotypical adults (decrease by 17%). Interestingly, GABA concentrations were positively correlated with self-reported tactile hypersensitivity in adults with ASD (r = 0.50, P = 0.01), but not in neurotypical adults. In addition, GABA concentrations were negatively correlated with the intra-individual variation during threshold measurement, both in neurotypical adults (r = -0.47, P = 0.04) and in adults with ASD (r = -0.59, P = 0.01). In other words, in both groups, the higher the GABA level, the more precise the tactile sensation. These results highlight the key role of GABA in tactile sensitivity, and suggest that atypical GABA modulation contributes to tactile hypersensitivity in ASD. We discuss the hypothesis that hypersensitivity in ASD could be due to suboptimal predictions about sensations. Autism Research 2019, 12: 562-575. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: People with autism spectrum disorder (ASD) often experience tactile hypersensitivity. Here, our goal was to highlight a link between tactile hypersensitivity and the concentration of gamma-aminobutyric acid (GABA) (an inhibitory neurotransmitter) in the brain of adults with ASD. Indeed, self-reported hypersensitivity correlated with reduced GABA levels in brain areas processing touch. Our study suggests that this neurotransmitter may play a key role in tactile hypersensitivity in autism.