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)

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13–30 Hz) and gamma (31–80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.This work was supported by grants from the Nancy Lurie Marks Family Foundation (TK, SK, MGK), Autism Speaks (TK), The Simons Foundation (SFARI 239395, TK), The National Institute of Child Health and Development (R01HD073254, TK), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 5R01EB009048, MSH), and the Cognitive Rhythms Collaborative: A Discovery Network (NFS 1042134, MSH). (Nancy Lurie Marks Family Foundation; Autism Speaks; SFARI 239395 - Simons Foundation; R01HD073254 - National Institute of Child Health and Development; P41EB015896 - National Institute for Biomedical Imaging and Bioengineering; 5R01EB009048 - National Institute for Biomedical Imaging and Bioengineering; NFS 1042134 - Cognitive Rhythms Collaborative: A Discovery Network

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band.

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development

Contributions aux méthodes parcimonieuses pour la localisation de source en MEG/EEG

Understanding the full complexity of the brain has been a challenging research project for decades, yet there are many mysteries that remain unsolved. Being able to model how the brain represents, analyzes, processes, and transforms information of millions of different tasks in a record time is primordial for both cognitive and clinical studies. These tasks can go from language, perception, memory, attention, emotion, to reasoning and creativity. Magnetoencephalography (MEG) and Electroencephalography (EEG) allow us to non-invasively measure the brain activity with high temporal and good spatial resolution using sensors positioned all over the head, in order to be analyzed. For a given magnetic-electric field outside the head, there are an infinite number of electrical current source distributed inside of the brain that could have created it. This means that the M/EEG inverse problem is ill-posed, having many solutions to the single problem. This constrains us to make assumptions about how the brain might work. This thesis investigated the assumption of having sparse source estimate, i.e. only few sources are activated for each specific task. This is modeled as a penalized regression with a spatio-temporal regularization term. The aim of this thesis was to use outstanding methodologies from machine learning field to solve the three steps of the M/EEG inverse problem. The first step is to model the problem in the time frequency domain with a multi-scale dictionary to take into account the mixture of non-stationary brain sources, i.e. brain regions share information resulting in brain activity alternating from a source to another. This is done by formulating the problem as a penalized regression with a data fit term and a spatio-temporal regularization term, which has an extra hyperparameter. This hyperparameter is mostly tuned by hand, which makes the analysis of source brain activity not objective, but also hard to generalize on big studies. The second contribution is to automatically estimate this hyperparameter under some conditions, which increase the objectivity of the solvers. However, these state-of-the-art solvers have a main problem that their source localization solver gives one solution, and does not allow for any uncertainty quantification. We investigated this question by studying new techniques as done by a Bayesian community involving Markov Chain Monte Carlo (MCMC) methods. It allows us to obtain uncertainty maps over source localization estimation, which is primordial for a clinical study, e.g. epileptic activity. The last main contribution is to have a complete comparison of state-of-the-art solvers on phantom dataset. Phantom is an artificial object that mimics the brain activity based on theoretical description and produces realistic data corresponding to complex spatio-temporal current sources. In other words, all solvers have been tested on an almost real dataset with a known ground truth for a real validation.Cette thèse a développé des méthodes parcimonieuses pour la localisation de sources en magnétoencéphalographie (MEG) et l'électroencéphalographie (EEG). Pour un champ électromagnétique donné, il y a un nombre infini de sources réparties à l’intérieur du cerveau qui aurait pu le créer. Cela signifie que le problème inverse est mal-posé, ayant de nombreuses possibles solutions. Cela nous contraint à faire des hypothèses ou des apriori sur le problème. Cette thèse a étudié les méthodes parcimonieuses, i.e., seulement quelques sources focales sont activées lors d’une tâche précise. La première contribution est de modéliser le problème comme une régression pénalisée dans le domaine temps-fréquence avec un dictionnaire multi-échelle pour prendre en compte tous les aspects d’un signal cérébral. En ajoutant le terme de régularisation spatio-temporel, le modèle ajoute un hyperparamètre qui reste à optimiser. Ceci a constitué la seconde contribution de cette thèse où une estimation automatique des hyperparamètres a été mise en oeuvre. La troisième contribution est de réduire l’écart entre les deux communautés qui formulent le problème inverse comme étant une régression pénalisée ou comme un modèle Bayésien. Cette thèse montre sous quelles hypothèses et sous quelle paramétrisation, on obtient une équivalence des deux formulations et comment profiter de cette nouvelle formulation Bayésienne pour quantifier l’incertitude de nos solutions. La dernière contribution a eu pour but de valider les solveurs sur des données fantôme, c’est à dire des vraies données avec une réalité terrain pour pouvoir quantifier l’erreur de localisation en position, orientation, et amplitude

About voting and non-voting at European elections of 1989 some empirical observations drawn from a pre-election survey conducted half a year before election day

SIGLEUuStB Koeln(38)-890107099 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekPreprintDEGerman

A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: Application to M/EEG source imaging

Majorization-minimization (MM) is a standard iterative optimization technique which consists in minimizing a sequence of convex surrogate functionals. MM approaches have been particularly successful to tackle inverse problems and statistical machine learning problems where the regularization term is a sparsity-promoting concave function. However, due to non-convexity, the solution found by MM depends on its initialization. Uniform initialization is the most natural and often employed strategy as it boils down to penalizing all coefficients equally in the first MM iteration. Yet, this arbitrary choice can lead to unsatisfactory results in severely under-determined inverse problems such as source imaging with magneto- and electro-encephalography (M/EEG). The framework of hierarchical Bayesian modeling (HBM) is an alternative approach to encode sparsity. This work shows that for certain hierarchical models, a simple alternating scheme to compute fully Bayesian maximum a posteriori (MAP) estimates leads to the exact same sequence of updates as a standard MM strategy (see the adaptive lasso). With this parallel outlined, we show how to improve upon these MM techniques by probing the multimodal posterior density using Markov Chain Monte-Carlo (MCMC) techniques. Firstly, we show that these samples can provide well-informed initializations that help MM schemes to reach better local minima. Secondly, we demonstrate how it can reveal the different modes of the posterior distribution in order to explore and quantify the inherent uncertainty and ambiguity of such ill-posed inference procedure. In the context of M/EEG, each mode corresponds to a plausible configuration of neural sources, which is crucial for data interpretation, especially in clinical contexts. Results on both simulations and real datasets show how the number or the type of sensors affect the uncertainties on the estimates

A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging

International audienceMajorization-minimization (MM) is a standard iterative optimization technique which consists in minimizing a sequence of convex surrogate functionals. MM approaches have been particularly successful to tackle inverse problems and statistical machine learning problems where the regularization term is a sparsity-promoting concave function. However, due to non-convexity, the solution found by MM depends on its initialization. Uniform initialization is the most natural and often employed strategy as it boils down to penalizing all coefficients equally in the first MM iteration. Yet, this arbitrary choice can lead to unsatisfactory results in severely under-determined inverse problems such as source imaging with magneto- and electro-encephalography (M/EEG). The framework of hierarchical Bayesian modeling (HBM) is an alternative approach to encode sparsity. This work shows that for certain hierarchical models, a simple alternating scheme to compute fully Bayesian maximum a posteriori(MAP) estimates leads to the exact same sequence of updates as a standard MM strategy (see the adaptive lasso). With this parallel outlined, we show how to improve upon these MM techniques by probing the multimodal posterior density using Markov Chai

M/EEG source localization with multi-scale time-frequency dictionaries

International audienceMagnetoencephalography (MEG) and electroen- cephalography (EEG) source localization is an ill-posed problem due to a small number of sensors measuring the brain activity. This results in a non-unique source estimate. To identify an appropriate solution out of an infinite set of possible candidates, the problem requires setting certain constraints depending on the assumptions or a priori knowledge about the source distri- bution. Different constraints have been proposed so far, mainly those that impose sparsity on the source reconstruction in both standard and time-frequency domains. Source localization in the time-frequency domain has already been investigated using Gabor dictionary in both a convex (TF-MxNE) and non-convex way (Iterative Reweighted TF-MxNE). The iterative reweighted (ir)TF-MxNE solver has been shown to outperform TF-MxNE in both source recovery and amplitude bias. However, the choice of an optimal dictionary remains unsolved. Due to a mixture of signals, i.e. short transient signals (right after the stimulus onset) and slower brain waves, the choice of a single dictionary explaining simultaneously both signals types in a sparse way is difficult. In this work, we introduce a method to improve the source estimation relying on a multi-scale dictionary, i.e. multiple dictionaries with different scales concatenated to fit short transients and slow waves at the same time. We compare our results with irTF-MxNE on realistic simulation, then we use somatosensory data to demonstrate the benefits of the approach on in terms of reduced leakage (time courses mixture), temporal smoothness and detection of both signals types

Automated rejection and repair of bad trials in MEG/EEG

peer reviewed© 2016 IEEE. We present an automated solution for detecting bad trials in magneto-/electroencephalography (M/EEG). Bad trials are commonly identified using peak-to-peak rejection thresholds that are set manually. This work proposes a solution to determine them automatically using cross-validation. We show that automatically selected rejection thresholds perform at par with manual thresholds, which can save hours of visual data inspection. We then use this automated approach to learn a sensor-specific rejection threshold. Finally, we use this approach to remove trials with finer precision and/or partially repair them using interpolation.We illustrate the performance on three public datasets. The method clearly performs better than a competitive benchmark on a 19-subject Faces dataset

Autoreject: Automated artifact rejection for MEG and EEG data

peer reviewedWe present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold -- a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparison with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. Comparison include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience