Guillaume Marrelec

• Doctor of Engineering
• Senior research scientist at French Institute of Health and Medical Research

We propose to quantify dependence between two systems $X$ and $Y$ in a dataset $D$ based on the Bayesian comparison of two models: one, $H_0$, of statistical independence and another one, $H_1$, of dependence. In this framework, dependence between $X$ and $Y$ in $D$, denoted $B(X,Y|D)$, is quantified as $P(H_1|D)$, the posterior probability for the...

In functional MRI (fMRI), effective connectivity analysis aims at inferring the causal influences that brain regions exert on one another. A common method for this type of analysis is structural equation modeling (SEM). We here propose a novel method to test the validity of a given model of structural equation. Given a structural model in the form...

In neuroscience, time-frequency analysis is widely used to investigate brain rhythms in brain recordings. In event-related protocols, it is applied to quantify how the brain responds to a stimulation repeated over many trials. We here focus on two common measures: the power of the transform for each single trial averaged across trials, avgPOW; and...

Beside the well‐documented involvement of secondary somatosensory area, the cortical network underlying late somatosensory evoked potentials (P60/N60 and P100/N100) is still unknown. Electroencephalogram and magnetoencephalogram source imaging were performed to further investigate the origin of the brain cortical areas involved in late somatosensor...

We investigated the ADV of perfluorohexane (PFH) nano and micro-droplets at a frequency of 1.1 MHz, at conditions where there is no superharmonic focusing. Our experiments were performed on suspensions of droplets in glycerol to avoid sedimentation. The ADV pressure threshold was defined as the pressure for which there is half a chance to observe a...

We propose to quantify dependence between two systems $\mathcal{X}$ and $\mathcal{Y}$ in a dataset $D$ based on the Bayesian comparison of two models: one, $H_{0}$ , of statistical independence and another one, $H_{1}$ , of dependence. In this framework, dependence between $\mathcal{X}$ and $\mathcal{Y}$ in $D$ , denoted ${\mathfra...

For a random variable X, we are interested in the blind extraction of its finest mutual independence pattern μ(X)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu (X)...

For a random variable $X$, we are interested in the blind extraction of its finest mutual independence pattern $\mu ( X )$. We introduce a specific kind of independence that we call dichotomic. If $\Delta ( X )$ stands for the set of all patterns of dichotomic independence that hold for $X$, we show that $\mu ( X )$ can be obtained as the intersect...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective. In neuroscience, time-frequency analysis has been used to get insight into brain rhythms from brain recordings. In event-related protocols, one applies it to investigate how the brain responds to a stimulation repeated over many trials. In t...

Objective. In neuroscience, time-frequency analysis has been used to get insight into brain rhythms from brain recordings. In event-related protocols, one applies it to investigate how the brain responds to a stimulation repeated over many trials. In this framework, three measures have been considered: the amplitude of the transform for each single...

In directional statistics, the von Mises distribution is a key element in the analysis of circular data. While there is a general agreement regarding the estimation of its location parameter μ, several methods have been proposed to estimate the concentration parameter κ. We here provide a thorough evaluation of the behavior of 12 such estimators fo...

In directional statistics, the von Mises distribution is a key element in the analysis of circular data. While there is a general agreement regarding the estimation of its location parameter $\mu$, several methods have been proposed to estimate the concentration parameter $\kappa$. We here provide a thorough evaluation of the behavior of 12 such es...

We consider Gaussian graphical models associated with an equicorrelational and one-dimensional conditional independence graph. We show that pairwise correlation decays exponentially as a function of distance. We also provide a limit when the number of variables tend to infinity and quantify the difference between the finite and infinite cases.

We consider Gaussian graphical models associated with an equicorrelational and one-dimensional conditional independence graph. We show that pairwise correlation decays exponentially as a function of distance. We also provide a limit when the number of variables tend to infinity and quantify the difference between the finite and infinite cases.

Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models, one being nested into the other and used to generate a null distribution for a statistic of interest, usually under the asymptotic a...

Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models, one being nested into the other and used to generate a null distribution for a statistic of interest, usually under the asymptotic a...

We consider a generalization of information density to a partitioning into $N \geq 2$ subvectors. We calculate its cumulant-generating function and its cumulants, showing that these quantities are only a function of all the regression coefficients associated with the partitioning.

We consider a generalization of information density to a partitioning into N≥2 subvectors. We calculate its cumulant-generating function and its cumulants in the particular case of a multivariate normal distribution, showing that these quantities are only a function of all the regression coefficients associated with the partitioning.

Extracting information from a signal exhibiting damped resonances is a challenging task in many practical cases due to the presence of noise and high-attenuation. The interpretation of the signal relies on a model whose order (\textit{i.e.}, the number of resonances) is in general unknown. In this study, the signal is modelled as a sum of Lorentzia...

Objectives: Infraclinical sensory alterations have been reported at early stages of amyotrophic lateral sclerosis (ALS). While previous studies mainly focused on early somatosensory evoked potentials (SEPs), late SEPs, which reflect on cortical pathways involved in cognitive-motor functions, are relatively underinvestigated. Early and late SEPs we...

Multiple studies have found neurofunctional changes in normal aging in a context of selective attention. Furthermore, many articles report intrahemispheric alteration in functional networks. However, little is known about age-related changes within the Ventral Attention Network (VAN), which underlies selective attention. The aim of this study is to...

Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to...

The nonlinear elasticity of solids at the microstrain level has been recently studied by applying dynamic acousto-elastic testing. It is the analog of conventional quasi-static acousto-elastic experiments but the strain-dependence (or stress-dependence) of ultrasonic wave-speed is measured with an applied strain ranging from 10⁻⁷ to 10⁻⁵ and produc...

Resonant Ultrasound Spectroscopy (RUS) is a method to measure the elasticity tensor of a material. RUS is particularly advantageous to measure small samples of anisotropic materials. In RUS, resonant frequencies of a sample are measured and computed frequencies of a numerical model of the sample are fitted, yielding the stiffness tensor. RUS was de...

Resonant ultrasound spectroscopy is an experimental technique for measuring the stiffness of anisotropic solid materials. The free vibration resonant frequencies of a specimen are measured and the stiffness coefficients of the material adjusted to minimize the difference between experimental and predicted frequencies. An issue of this inverse appro...

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We fou...

Advances in magnetic resonance imaging (MRI) allow to gain critical insight into the structure of neural networks and their functional dynamics. To relate structural connectivity [as quantified by diffusion-weighted imaging (DWI) tractography] and functional connectivity [as obtained from functional MRI (fMRI)], increasing emphasis has been put on...

The consolidation of motor sequence learning is known to depend on sleep. Work in our laboratory and others have shown that the striatum is associated with this off-line consolidation process. In this study, we aimed to quantify the sleep-dependent dynamic changes occurring at the network level using a measure of functional integration. We directly...

Investigating the relationship between brain structure and function is a central endeavor for neuroscience research. Yet, the mechanisms shaping this relationship largely remain to be elucidated and are highly debated. In particular, the existence and relative contributions of anatomical constraints and dynamical physiological mechanisms of differe...

Functional brain networks are sets of cortical, subcortical and cerebellar regions whose neuronal activities are synchronous over multiple time scales. Spatial independent component analysis (sICA) is a widespread approach to identify functional networks in the human brain from functional magnetic resonance imaging (fMRI) resting-state data, and th...

How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called "the dual intertwined rings architecture") that relates the functional specialization of cortical networks to their spatial distribution over the cerebral...

Previous research on participants with aphasia has mainly been based on standard functional neuroimaging analysis. Recent studies have shown that functional connectivity analysis can detect compensatory activity, not revealed by standard analysis. Little is known, however, about the default-mode network in aphasia. In the current study, we studied...

Functional connectivity changes in the language network (Price, 2010), and in a control network involved in second language (L2) processing (Abutalebi & Green, 2007) were examined in a group of Persian (L1) speakers learning French (L2) words. Measures of network integration that characterize the global integrative state of a network (Marrelec, Bel...

Recent advances in magnetic resonance imaging (MRI) are allowing neuroscientists to gain critical insights into the neural networks mediating a variety of cognitive processes. This work investigates structural and functional connectivity in the human brain under different experimental conditions through multimodal MRI acquisitions. To define the no...

Consciousness is reduced during nonrapid eye movement (NREM) sleep due to changes in brain function that are still poorly understood. Here, we tested the hypothesis that impaired consciousness during NREM sleep is associated with an increased modularity of brain activity. Cerebral connectivity was quantified in resting-state functional magnetic res...

Mindfulness meditation has been shown to promote emotional stability. Moreover, during the processing of aversive and self-referential stimuli, mindful awareness is associated with reduced medial prefrontal cortex (MPFC) activity, a central default mode network (DMN) component. However, it remains unclear whether mindfulness practice influences fun...

In the present paper, we propose a large sample asymptotic approximation for the sampling and posterior distributions of differential entropy when the sample is composed of independent and identically distributed realization of a multivariate normal distribution.

Consciousness has been related to the amount of integrated information that the brain is able to generate. In this paper, we tested the hypothesis that the loss of consciousness caused by propofol anesthesia is associated with a significant reduction in the capacity of the brain to integrate information. To assess the functional structure of the wh...

Detailed spatial overlaps between ROIs between TalFr and TalFox (circle), TalFr and gICA (square), and TalFox and gICA (diamond). If S1 and S2 are the spheres extracted for a given ROI by methods 1 and 2, respectively, then the overlap between methods 1 and 2 for that ROI is computed as volume(S1 ∩ S2)/{[volume(S1)+volume (S2)]/2}. (0.01 MB EPS)

Condition-specific effect of method. Condition-by-condition P-values for an effect of method. MDS is performed on the components obtained for a given method after MDS on all the data. MDS* is performed on the components obtained for a given method after MDS on the data corresponding to that method only. (0.01 MB PDF)

Proof of Equation (11). (0.07 MB PDF)

Detailed spatial overlaps between ROIs between indICAs and the three other methods. If S1 and S2 are the spheres extracted for a given ROI by methods 1 and 2, respectively, then the overlap between methods 1 and 2 for that ROI is computed as volume(S1 ∩ S2)/{[volume(S1)+volume (S2)]/2}. The bottom and top of the box are the 25th and 75th percentile...

Detailed distances between ROI centers as extracted with indICAs and the three other methods. The bottom and top of the box are the 25th and 75th percentile (the lower and upper quartiles, respectively), and the band in the box is the 50th percentile (median); whiskers represent minimum and maximum values. (0.10 MB PDF)

Detailed distances between ROI centers between TalFr and TalFox (circle), TalFr and gICA (square), and TalFox and gICA (diamond). (0.01 MB EPS)

Method-specific effect of condition. Method-by-method P-values for an effect of condition. MDS is performed on the components obtained for a given method after MDS on all the data. MDS* is performed on the components obtained for a given method after MDS on the data corresponding to that method only. (0.01 MB PDF)

In blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), assessing functional connectivity between and within brain networks from datasets acquired during steady-state conditions has become increasingly common. However, in contrast to connectivity analyses based on task-evoked signal changes, selecting the optimal spatia...

Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has been extensively used to study how task performance modulates brain activity. Such an approach emphasizes the principle of functional segregation, in that different brain areas are characterized by their involvement in specific cognitive processes. However, this ty...

Brain functional networks are sets of distant cortical, subcortical or cerebellar regions characterized by coherent dynamics. While spatial independent component analysis (sICA) reproducibly detects the cortical components of these networks from resting-state functional magnetic resonance imaging (fMRI) data, little is known about their subcortical...

While the cortical components of functional networks detected by spatial independent component analysis (sICA) in functional magnetic resonance imaging (fMRI) have been reproducibly described in various While the cortical components of functional networks detected by spatial independent component analysis (sICA) in functional magnetic resonance ima...

Brain regions are thought to be organized in large-scale networks, and studying interactions within and between such networks using functional magnetic resonance imaging (fMRI) could prove relevant for understanding brain's functional organization. Such interactions can be quantified by looking at their integration, a generalized measure of correla...

When characterizing regional cerebral gray matter differences in structural magnetic resonance images (sMRI) by voxel-based morphometry (VBM), one faces a known drawback of VBM, namely that histogram unequalization in the intensity images introduces false-positive results. To overcome this limitation, we propose to improve VBM by a new approach (ca...

Motor skill learning is associated with profound changes in brain activation patterns over time. Associative and rostral premotor cortical and subcortical regions are mostly recruited during the early phase of explicit motor learning, while sensorimotor regions may increase their activity during the late learning phases. Distinct brain networks are...

In this paper we propose a novel approach for characterizing effective connectivity in functional magnetic resonance imaging (fMRI) data. Unlike most other methods, our approach is nonlinear and does not rely on a priori specification of a model that contains structural information of neuronal populations. Instead, it relies on a nonlinear autoregr...

An important field of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is the investigation of effective connectivity, that is, the actions that a given set of regions exert on one another. We recently proposed a data-driven method based on the partial correlation matrix that could provide some insight regarding the...

Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between fu...

Functional magnetic resonance imaging (fMRI) allows for the indirect measurement of whole brain neuronal activity using local blood oxygenation level. Functional connectivity, i.e., the correlation between the temporal activity of re-mote regions, may be used to track brain reorganization while, for example, a sub-ject learns a new skill. However,...

Recent research has shown that intrinsic brain activity as observed by functional magnetic resonance imaging (fMRI) manifest itself as coherent signal changes in networks encompassing brain regions that span long-range neuronal pathways. One of these networks, the so called default mode network, has become the primary target in recent investigation...

Let be a multivariate Gaussian variable with covariance matrix [Sigma]. For i and j in , we show that if the conditional covariance between xi and xj given any conditioning set is equal to zero, then [Sigma] is block diagonal and i and j belong to two different blocks.

In neuroscience, the notion has emerged that the brain abides by two principles: segregation and integration. Segregation into functionally specialized systems and integration of information flow across systems are basic principles that are thought to shape the functional architecture of the brain. A measure called integration, originating from inf...

Functional magnetic resonance imaging (fMRI) has recently proved its utility in studying brain large-scale networks through fluctuations in resting-state data. To process such rest acquisitions, exploratory methods such as independent component analysis (ICA) are of particular interest. Yet, while successfully applied at the individual level, exist...

A large-scale brain network can be defined as a set of segregated and integrated regions, that is, distant regions that share strong anatomical connections and functional interactions. Data-driven investigation of such networks has recently received a great deal of attention in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imagi...

In functional magnetic resonance imaging (fMRI) data analysis, effective connectivity investigates the influence that brain regions exert on one another. Structural equation modeling (SEM) has been the main approach to examine effective connectivity. In this paper, we propose a method that, given a set of regions, performs partial correlation analy...

In this work, we propose a symmetrical multimodal EEG/fMRI information fusion approach dedicated to the identification of event-related bioelectric and hemodynamic responses. Unlike existing, asymmetrical EEG/fMRI data fusion algorithms, we build a joint EEG/fMRI generative model that explicitly accounts for local coupling/uncoupling of bioelectric...

Although the prominence of delusional ideas are known in psychopathology, notably in schizophrenia, their etiology is still not clearly understood. Three theoretical approaches are proposed in the literature to explain the formation and maintenance of delusional ideas, yet none of these approaches makes consensus. The objective of this article is t...

Résumé Bien que l’on connaisse la proéminence des idées délirantes dans la psychopathologie, notamment dans la schizophrénie, leur étiologie n’est pas encore clairement comprise. Trois approches théoriques sont proposées dans la littérature pour expliquer la formation et le maintien des idées délirantes, mais aucune ne fait consensus. L’objectif de...

Increasing emphasis has been recently put on large-scale network processing of brain functions. To explore these networks, many approaches have been proposed in functional magnetic resonance imaging (fMRI). Their objective is to answer the following two questions: (1) what brain regions are involved in the functional process under investigation? an...

Examination of functional interactions through effective connectivity requires the determination of three distinct levels of information: (1) the regions involved in the process and forming the spatial support of the network, (2) the presence or absence of interactions between each pair of regions, and (3) the directionality of the existing interac...

The theory of Gaussian graphical models is a powerful tool for independence analysis between continuous variables. In this framework, various methods have been conceived to infer independence relations from data samples. However, most of them result in stepwise, deterministic, descent algorithms that are inadequate for solving this issue. More rece...

Characterizing the cortical activity from electro- and magneto-encephalography (EEG/MEG) data requires solving an ill-posed inverse problem that does not admit a unique solution. As a consequence, the use of functional neuroimaging, for instance, functional Magnetic Resonance Imaging (fMRI), constitutes an appealing way of constraining the solution...

Many measures have been proposed so far to extract brain functional interactivity from functional magnetic resonance imaging (fMRI) and magnetoencephalography/electroencephalography (MEG/EEG) data sets. Unfortunately, none has been able to provide a relevant, self-contained, and common definition of brain interaction. In this paper, we propose a fi...

A recent issue in functional magnetic resonance imaging (fMRI) data analysis has been the investigation of functional brain interactivity. Two standpoints have been considered so far. On the one hand, effective connectivity describes the influence that regions exert on each other. Yet, it requires the prior definition of a structural model that oft...

A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill...

In functional magnetic resonance imaging (fMRI), cerebral activity has been increasingly considered as the consequence of a network activation. Selecting the brain regions relevant for the network has thus become a key issue. We propose to define the so-called large-scale functional network involved in a particular task as a set of regions exhibiti...

In functional magnetic resonance imaging (fMRI), functional connectivity of brain regions is defined as the temporal correlation of their average time courses. A key question is to determine which processes contribute to functional connectivity. Independent component analysis (ICA) is a recent data-driven method that has proven efficient to identif...

In this paper, we present an approach for building paths representing the anatomical connections between some given regions of the brain. The method combines diffusion tensor imaging (DTI) and fast marching techniques, representing an anatomical connection as the shortest path for some global minimization criterion. Paths are generated by growing l...

Sampling from probability density functions (pdfs) has become more and more important in many areas of applied science, and has therefore been the subject of great attention. Many sampling procedures proposed allow for approximate or asymptotic sampling. On the other hand, very few methods allow for exact sampling. Direct sampling of standard pdfs...

This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain...

A convenient way to analyze BOLD fMRI data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the Hemodynamic Response Function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general B...

In BOLD fMRI data analysis, robust and accurate estimation of the Hemodynamic Response Function (HRF) is still under investigation. Parametric methods assume the shape of the HRF to be known and constant throughout the brain, whereas non-parametric methods mostly rely on artificially increasing the signal-to-noise ratio. We extend and develop a pre...

Hemodynamic Response Function (HRF) estimation in noisy functiomal Magnetic Resonance Imaging (fMRD is essential for a better understanding of cerebral activations. Previous works have proposed robust non-parametric estimates of the HRF within a regularized framework [1, 2]. They are not adapted for event-related paradigms that are either asynchron...

This paper deals with the estimation of the bloodoxygen level-dependent response to a stimulus, as measured infunctional magnetic resonance imaging (fMRI) data. A preciseestimation is essential for a better understanding of cerebralactivations. The most recent works have used a nonparametricframework for this estimation, considering each brain regi...

Functional MRI (fMRI) is a recent, non-invasive technique allowing for the evolution of brain processes to be dynamically followed in various cognitive or behavioral tasks. In BOLD fMRI, what is actually measured is only indirectly related to neuronal activity through a process that is still under investigation. A convenient way to analyze BOLD fMR...

A recent concern in BOLD fMRI data analysis is extraction of connectivity information between regions. Functional connectivity and effective connectivity are the two notions defined so far, but the former lacks the ability to reveal direct interactions, while use of the latter requires the connectivity model to be set a priori. We propose that cond...

Hemodynamic Response Function (HRF) estimation in noisy functional Magnetic Resonance Imaging (fMRI) is essential for a better understanding of cerebral activations. Previous works have proposed robust non-parametric estimates of the HRF within a regularized framework [C. Goutte et al., 2000, G. Marrelec et al. 2001]. They are not adapted for event...

Bien que l'on connaisse la proéminence des idées délirantes dans la psychopathologie, notamment dans la schizophrénie, leur étiologie n'est pas encore clairement comprise.Trois approches théoriques sont proposées dans la littérature pour expliquer la formation et le maintien des idées délirantes, mais aucune ne fait consensus. L'objectif de cet art...