Learning-induced modulation of scale-free properties of brain activity measured with MEG

International audiencePrevious studies have suggested that infraslow brain activity could play an important role in cognition. Its scale-free properties (coarsely described by its 1/f power spectrum) are indeed modulated between contrasted conscious states (sleep vs. awake). However, finer modulations remain to be investigated. Here, we make use of a robust multifractal analysis to investigate the group-level impact of perceptual learning (visual (V), or audiovisual (AV), N=12 subjects in each group) on the source reconstructed scale-free activity recorded with magnetoencephalography (MEG) during rest and task. We first observed a significant decrease of self-similarity in evoked activity during the task after both trainings. More interestingly, only the most efficient training (AV) induced a decrease of self-similarity in spontaneous activity at rest whereas only V training induced an increase of multifractality in evoked activity

La convergence de l'activité neurale vers des attracteurs multifractals localisés prédit la capacité d'apprentissage

National audienceDans cet article, nous mettons en évidence les propriétés d'invariance d'échelle (auto-similarité H, multifractalité M) des signaux cérébraux acquis par magnétoencephalographie (MEG) et nous démontrons leur pertinence fonctionnelle dans une tâche complexe de discrimination visuelle en contrastant les situations avant et après apprentissage. L'analyse de ces invariances démontre que le cerveau peut adopter deux stratégies complémentaires pour apprendre efficacement: soit réduire H dans les aires associatives sous-tendant la plasticité neurale, soit faire converger M vers un attracteur asymptotique dans ces mêmes aires. Abstract – In this paper, we provide evidence for scaling properties (self-similarity H, multifractality: M) in the human brain based on brain activity recorded with magnetoencephalography (MEG). We demonstrate the functional relevance of scaling properties during the learning of complex visual discrimination by contrasting before and after training. The analysis of scale-free dynamics show two complementary strategies for efficient learning and plasticity, namely: in those regions showing plasticity, we report a decrease of H and a convergence of M towards asymptotic values

MODULATION OF SCALE-FREE PROPERTIES OF BRAIN ACTIVITY IN MEG

International audienceThe analysis of scale-free (i.e., 1/f power spectrum) brain activity has emerged in the last decade since it has been shown that low frequency fluctuations interact with oscillatory activity in electrophysiology, noticeably when exogenous factors (stimuli, task) are delivered to the human brain. However, there are some major difficulties in measuring scale-free activity in neuroimaging data: they are noisy, possibly nonstationary ... Here, we make use of multifractal analysis to better understand the biological meaning of scale-free activity recorded with Magnetoencephalography (MEG) data. On a cohort of 20 subjects, we demonstrate the presence of self-similarity on all sensors during rest and visually evoked activity. Also, we report significant multifractality on the norm of gradiometers. Finally, on the latter signals we show how self-similarity and multifractality are modulated between ongoing and evoked activity

Decoding perceptual thresholds from MEG/EEG

International audienceMagnetoencephalography (MEG) can map brain activity by recording the electromagnetic fields generated by the electrical currents in the brain during a perceptual or cognitive task. This technique offers a very high temporal resolution that allows noninvasive brain exploration at a millisecond (ms) time scale. Decoding, a.k.a. brain reading, consists in predicting from neuroimaging data the subject's behavior and/or the parameters of the perceived stimuli. This is facilitated by the use of supervised learning techniques. In this work we consider the problem of decoding a target variable with ordered values. This target reflects the use of a parametric experimental design in which a parameter of the stimulus is continuously modulated during the experiment. The decoding step is performed by a Ridge regression. The evaluation metric, given the ordinal nature of the target is performed by a ranking metric. On a visual paradigm consisting of random dot kinematograms with 7 coherence levels recorded on 36 subjects we show that one can predict the perceptual thresholds of the subjects from the MEG data. Results are obtained in sensor space and for source estimates in relevant regions of interests (MT, pSTS, mSTS, VLPFC)

Analyses des champs évoqués et de l’invariance d’échelle des signaux cérébraux acquis en magnétoencéphalographie durant un paradigme d’apprentissage multisensoriel et reconstruits sur la surface corticale

The analysis of Human brain activity in magnetoencephalography (MEG) can be generally conducted in two ways: either by focusing on the average response evoked by a stimulus repeated over time, more commonly known as an ``event-related field'' (ERF), or by decomposing the signal into functionally relevant oscillatory or frequency bands (such as alpha, beta or gamma). However, the major part of brain activity is arrhythmic and these approaches fail in describing its complexity, particularly in resting-state. As an alternative, the analysis of the 1/f-type power spectrum observed in the very low frequencies, a hallmark of scale-free dynamics, can overcome these issues. Yet it remains unclear whether this scale-free property is functionally relevant and whether its fluctuations matter for behavior. To address this question, our first concern was to establish a visual learning paradigm that would entail functional plasticity during an MEG session. In order to optimize the training effects, we developed new audiovisual (AV) stimuli (an acoustic texture paired with a colored visual motion) that induced multisensory integration and indeed improved learning compared to visual training solely (V) or accompanied with acoustic noise (AVn). This led us to investigate the neural correlates of these three types of training using first a classical method such as the ERF analysis. After source reconstruction on each individual cortical surface using MNE-dSPM, the network involved in the task was identified at the group-level. The selective plasticity observed in the human motion area (hMT+) correlated across all individuals with the behavioral improvement and was supported by a larger network in AV comprising multisensory areas. On the basis of these findings, we further explored the links between the behavior and scale-free properties of these same source-reconstructed MEG signals. Although most studies restricted their analysis to the global measure of self-similarity (i.e. long-range fluctuations), we also considered local fluctuations (i.e. multifractality) by using the Wavelet Leader Based Multifractal Formalism (WLBMF). We found intertwined modulations of self-similarity and multifractality in the same cortical regions as those revealed by the ERF analysis. Most astonishing, the degree of multifractality observed in each individual converged during the training towards a single attractor that reflected the asymptotic behavioral performance in hMT+. Finally, these findings and their associated methodological issues are compared with the ones that came out from the ERF analysis.Il existe deux façons d'analyser l'activité cérébrale acquise en magnétoencéphalographie (MEG) : soit en moyennant les réponses suscitées par la répétition d'un stimulus afin d'observer le « champ évoqué »; soit en décomposant le signal en bandes oscillatoires (tel que l'alpha, le bêta ou le gamma), chacune étant associée à différents rôles fonctionnels. Ces méthodes ne prennent cependant pas compte de la complexité de l'activité cérébrale dont l'essentiel est arythmique, notamment au repos. Pour pallier à cela, une autre approche consiste à analyser le spectre de puissance en 1/f observable dans les très basses fréquences, une caractéristique des systèmes dont la dynamique est invariante d'échelle. Pour savoir si cette propriété joue un quelconque rôle dans le fonctionnement cérébral et si elle a des conséquences sur le comportement, nous avons établit un paradigme d'apprentissage visuel permettant d'observer de la plasticité fonctionnelle au cours d'une session MEG. Pour avoir un entraînement optimal, nous avons développé de nouveaux stimuli audiovisuels (AV) (une texture acoustique associée à un nuage de points colorés en mouvement) permettant une intégration multisensorielle et de ce fait un meilleur apprentissage que celui apporté par un entraînement visuel seul (V) ou accompagné d'un bruit acoustique (AVn). Nous avons ensuite étudié les corrélats neuronaux de ces trois types d'apprentissage par l'analyse classique des champs évoqués. Une fois l'activité reconstruite sur la surface corticale de chaque individu à l'aide de MNE-dSPM, nous avons identifié le réseau impliqué dans la tâche au sein de chaque groupe. En particulier, la plasticité sélective observée dans l'aire hMT+ associée au traitement du mouvement visuel corrélait avec les progressions comportementales des individus et était soutenue en AV par un plus vaste réseau comprenant notamment des aires multisensorielles. Parallèlement, nous avons exploré les liens reliant le comportement et les propriétés d'invariance d'échelle de ces mêmes signaux MEG reconstruits sur le cortex. Tandis que la plupart des études se limitent à analyser l'auto-similarité (une caractéristique globale synonyme de longue mémoire), nous avons aussi considéré les fluctuations locales (c-à-d la multifractalité) au moyen de l'analyse WLBMF. Nous avons trouvé des modulations couplées de l'auto-similarité et de la multifractalité dans des régions similaires à celles révélées par l'analyse des champs évoqués. Plus surprenant, Le degré de multifractalité relevé dans chaque individu convergeait durant l'entraînement vers un même attracteur reflétant la performance comportementale asymptotique

Supramodal processing optimizes visual perceptual learning and plasticity

International audienceMultisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortices may be supramodal i.e. capable of functional selectivity irrespective of the sensory modality of inputs (Pascual-Leone and Hamilton, 2001; Ricciardi and Pietrini, 2011; Voss and Zatorre, 2012; Renier et al., 2013). Here, we asked whether learning to discriminate visual coherence could benefit from supramodal processing. To this end, three groups of participants were briefly trained to discriminate which of a red or green intermixed population of random-dot-kinematograms (RDKs) was most coherent in a visual display while being recorded with magnetoencephalography (MEG). During training, participants heard no sound (V), congruent acoustic textures (AV) or auditory noise (AVn); importantly, congruent acoustic textures shared the temporal statistics – i.e. coherence – of visual RDKs. After training, the AV group significantly outperformed participants trained in V and AVn although they were not aware of their progress. In pre- and post-training blocks, all participants were tested without sound and with the same set of RDKs. When contrasting MEG data collected in these experimental blocks, selective differences were observed in the dynamic pattern and the cortical loci responsive to visual RDKs. First and common to all three groups, vlPFC showed selectivity to the learned coherence levels whereas selectivity in visual motion area hMT+ was only seen for the AV group. Second and solely for the AV group, activity in multisensory cortices (mSTS, pSTS) correlated with post-training performances; additionally, the latencies of these effects suggested feedback from vlPFC to hMT+ possibly mediated by temporal cortices in AV and AVn groups. Altogether, we interpret our results in the context of the Reverse Hierarchy Theory of learning (Ahissar and Hochstein, 2004) in which supramodal processing optimizes visual perceptual learning by capitalizing on sensory-invariant representations - here, global coherence levels across sensory modalities

The Umbilical Cord Blood αβ T-Cell Repertoire: Characteristics of a Polyclonal and Naive but Completely Formed Repertoire

International audienceUmbilical cord blood (CB) constitutes a promising alternative to bone marrow for allogeneic transplantation and is increasingly used because of the reduced severity of graft-versus-host disease after CB transplantation. We have compared the T-cell receptor β chain (TCRB) diversity of CB lymphocytes with that of adult lymphocytes by analyzing the complementarity determining region 3 (CDR3) size heterogeneity. In marked contrast to adult samples, we observed bell-shaped profiles in all of the 22 functional β-chain variable (BV) subfamilies that reflect the lack of prior antigenic stimulation in CB samples. However, the mean CDR3 size and BV usage were comparable between CB and adult samples. BJ2 (65%) segments were used preferentially to BJ1 (35%), especially BJ2S7, BJ2S5, BJ2S3, and BJ2S1, in both CB and in adult lymphocytes. We therefore conclude that although naive as reflected by the heterogeneity of the CDR3 size, the TCRBV repertoire appears fully constituted at birth. The ability to expand TCRB subfamilies was confirmed by stimulation with staphylococcal superantigens toxic shock syndrome toxin-1 and staphylococcal enterotoxin A. This study provides the basis for future analysis of the T-cell repertoire reconstitution following umbilical CB transplantation