Structural connectivity to reconstruct brain activation and effective connectivity between brain regions

International audienceUnderstanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two non-invasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering spatio-temporal constraints but only few of them use structural information to do so. In this work, we present a source reconstruction algorithm that uses brain structural connectivity, estimated from diffusion MRI (dMRI), to constrain the EEG/MEG source reconstruction. Contrarily to most source reconstruction methods which reconstruct activation for each time instant, the proposed method estimates an initial reconstruction for the first time instants and a multivariate autoregressive model that explains the data in further time instants. This au-toregressive model can be thought as an estimation of the effective connectivity between brain regions. We called this algorithm iterative Source and Dynamics reconstruction (iSDR). This paper presents the overall iSDR approach and how the proposed model is optimized to obtain both brain activation and brain region interactions. The accuracy of our method is demonstrated using synthetic data in which it shows a good capability to reconstruct both activation and connectivity. iSDR is also tested with real data obtained from [dataset] (face recognition task). The results are in phase with other works published with the same data and others that used different imaging modalities with the same task showing that the choice of using an autoregressive model gives relevant results

Using Structural Connectivity to Reconstruct Brain Activation and Effective Connectivity

OPAL-MesoInternational audienceIntroduction:Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two noninvasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering spatio-temporal constraints but only few of them use structural informa on to do so. We present a source estimation algorithm that uses brain structural connectivity, obtained from diffusion MRI (dMRI), to constrain the EEG/MEG source reconstruction. Contrarily to most source reconstruction methods which reconstruct activation for each time instant, the proposed method estimates an initial reconstruction for the first time instants and a multivariate auto-regressive model that explains the data in further time instants. This auto-regressive model can be thought as an estimation of the effective connectivity between brain regions.Methods:We use diffusion MRI (dMRI) in conjunction with EEG/MEG measurements to reconstruct brain activity. dMRI is processed using probabilistic fiber tracking from FSL. These fiber tracks are used in two ways: 1) to parcellate the cortex into functional regions (defined as having an homogeneous connectivity profile) and 2) to create a network of connexions between these regions. The parameters were tuned to obtain cortical regions of around 100 mm2, which is about the minimum size of detectable activations with M/EEG. We further assume a constant activation (a scalar value) per cortical region at a given time instant. This allows to greatly reduce the dimension of the source space from the ~10k nodes of the cortical mesh to ~600 extracted cortical regions. This spatial source model is completed by a temporal multivariate auto-regressive (MAR) model where a region activation at time t is obtained as linear combination of the activations at the p previous time instants t=1, ..., t-p of brain regions to which it is connected. The unknowns in this model are the region activations J(t) for the first t=1..p time instants and the coefficients A of the linear combination. Given some M/EEG measurements for a time window of size T, the goal is to estimate these unknowns to fit these data. To do so, we introduce a slightly modified MxNE criterion U(J) (Gramfort etal, 2012) that promotes spatial sparsity along with temporal continuity of the activations. A second criterion V(A) measures how well the estimated activations J(t), t=1..T are obeying the MAR model. The optimisa on procedure iterates between two steps, which alternatively improve the estimates of J(t), t=1..T to decrease U(J) and the linear coefficients A, given J(t), t=1..T. The process stops when a maximum number of iterations is reached or when there is no significant changes between two iterations.Results:The method was evaluated using the real dataset described in Wakeman et al (2015). MEG/EEG data were simultaneously recorded during a face recogni on task where a subject is shown famous, unknown or scrambled faces. The dataset also contains dMRI andT1 images. Four MAR models were tested:p∈ {1,2,3,4}. We show that the acquired data can be explained by our model with few regions as soon as p>1 (Fig. 1). Reconstructions using EEG and MEG show a clear negative peak at the FG around 200ms for p>1 which matches what can be found in the literature (Fig. 2). The non-null coefficient in the final A can be considered as the effective connectivity used during the task.Conclusions:We have presented a way of reconstructing brain activation and effective connectivity between the brain regions using an extension of the MxNE solver. Sources are constrained to follow a MAR model of order p. We have shown that such a model can fit real M/EEG measurements with relatively few activated regions as soon as p>1 and that the recovered activated regions are coherent with the task used to acquire the dataset

Iterative two-stage approach to estimate sources and their interactions

International audienceNon-iterative two-stage approaches have been used to estimate source interactions. They first reconstruct sources and then compute the MAR model for the localized sources. They showed good results when working in high signal-to-noise ratio (SNR) settings, but fail in detecting the true interactions when working in low SNR. Our framework is based on two steps. First, we estimate sources activations for a given MAR model. Then, we estimate the MAR model. We repeat the two steps until a stopping criterion is achieved

Large brain effective network from EEG/MEG data and dMR information

International audienceOver the past 30 years, neuroimaging has become a predominant technique. One might envision that over the next years it will play a major role in disclosing the brain's functional interactions. In this work, we use information coming from diffusion magnetic resonance imaging (dMRI) to reconstruct effective brain network from two functional modalities: electroencephalography (EEG) and magnetoen-cephalography (MEG)

Multivariate Autoregressive Model Constrained by Anatomical Connectivity to Reconstruct Focal Sources

International audienceIn this paper, we present a framework to reconstruct spatially localized sources from Magnetoencephalogra-phy (MEG)/Electroencephalography (EEG) using spatiotempo-ral constraint. The source dynamics are represented by a Mul-tivariate Autoregressive (MAR) model whose matrix elements are constrained by the anatomical connectivity obtained from diffusion Magnetic Resonance Imaging (dMRI). The framework assumes that the whole brain dynamic follows a constant MAR model in a time window of interest. The source activations and the MAR model parameters are estimated iteratively. We could confirm the accuracy of the framework using simulation experiments in both high and low noise levels. The proposed framework outperforms the two-stage approach

Diffusion Magnetic Resonance information as a regularization term for MEG/EEG inverse problem

International audienceSeveral regularization terms are used to constrain the Magnetoencephalography (MEG) and the Electroencephalography (EEG) inverse problem. It has been shown that the brain can be divided into several regions[1] with functional homogeneity inside each one of them. To locate these regions, we use the structural information coming from the diffusion Magnetic Resonance (dMRI) and more specifically, the anatomical connectivity of the distributed sources computed from dMRI. To invistigate the importance of the dMRI in the source reconstruction, we compare the solution based on dMRI-based parcellation to random parcellation

MEM-diffusion MRI framework to solve MEEG inverse problem

International audienceIn this paper, we present a framework to fuse information coming from diffusion magnetic resonance imaging (dMRI) with Magnetoencephalography (MEG)/ Electroencephalography (EEG) measurements to reconstruct the activation on the cortical surface. The MEG/EEG inverse-problem is solved by the Maximum Entropy on the Mean (MEM) principle and by assuming that the sources inside each cortical region follow Normal distribution. These regions are obtained using dMRI and assumed to be functionally independent. The source reconstruction framework presented in this work is tested using synthetic and real data. The activated regions for the real data is consistent with the literature about the face recognition and processing network

Using diffusion MRI information in the Maximum Entropy on Mean framework to solve MEG/EEG inverse problem

International audienceMagnetoencephalography (MEG) and Electroencephalography (EEG) inverse problem is well-known to require regularization to avoid ill-posedness. Usually, regularization is based on mathematical criteria (minum norm, ...). Physiologically, the brain is organized in functional parcels and imposing a certain homogeneity of the activity within these parcels was proven to be an efficient way to analyze the MEG/EEG data [1][6]. The parcels information can be computed from diffusion Magnetic Resonances Imaging (dMRI) by grouping together source positions shared the same connectivity profile (computed as tractograms from diffusion images). In this work, three parcel-based inverse problem approaches have been tested. The first two approaches are based on minimum norm with added regularization terms to account for the parcel information. They differ by the use of a hard/soft constraint in the way they impose that the activity is constant within each parcel [4]. The third approach is based on the Maximum Entropy on Mean (MEM) framework [2]. The dMRI-base and random cortex parcellation, we test also the use of Multivariate Source Pre-localization (MSP) [5] in the source reconstruction

Utilisation de l’IRM de diffusion pour la reconstruction de réseaux d’activations cérébrales à partir de données MEG/EEG

Understanding how brain regions interact to perform a given task is a very challenging task. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive functional imaging modalities used to record brain activity with high temporal resolution. As estimating brain activity from these measurements is an ill-posed problem. We thus must set a prior on the sources to obtain a unique solution. It has been shown in previous studies that structural homogeneity of brain regions reflect their functional homogeneity. One of the main goals of this work is to use this structural information to define priors to constrain more anatomically the MEG/EEG source reconstruction problem. This structural information is obtained using diffusion magnetic resonance imaging (dMRI), which is, as of today, the unique non-invasive structural imaging modality that provides an insight on the structural organization of white matter. This makes its use to constrain the EEG/MEG inverse problem justified. In our work, dMRI information is used to reconstruct brain activation in two ways: (1) In a spatial method which uses brain parcels to constrain the sources activity. These parcels are obtained by our whole brain parcellation algorithm which computes cortical regions with the most structural homogeneity with respect to a similarity measure. (2) In a spatio-temporal method that makes use of the anatomical connections computed from dMRI to constrain the sources’ dynamics. These different methods are validated using synthetic and real data.Comprendre comment différentes régions du cerveau interagissent afin d’exécuter une tâche, est un défi très complexe. La magnéto- et l’électroencéphalographie (MEEG) sont deux techniques non-invasive d’imagerie fonctionnelle utilisées pour mesurer avec une bonne résolution temporelle l’activité cérébrale. Estimer cette activité à partir des mesures MEEG est un problème mal posé. Il est donc crucial de le régulariser pour obtenir une solution unique. Il a été montré que l’homogénéité structurelle des régions corticales reflète leur homogénéité fonctionnelle. Un des buts principaux de ce travail est d’utiliser cette information structurelle pour définir des a priori permettant de contraindre de manière plus anatomique ce problème inverse de reconstruction de sources. L’imagerie par résonance magnétique de diffusion (IRMd) est, à ce jour, la seule technique non-invasive qui fournisse des informations sur l’organisation structurelle de la matière blanche. Cela justifie son utilisation pour contraindre notre problème inverse. Nous utilisons l’information fournie par l’IRMd de deux manière différentes pour reconstruire les activations du cerveau : (1) via une méthode spatiale qui utilise une parcellisation du cerveau pour contraindre l’activité des sources. Ces parcelles sont obtenues par un algorithme qui permet d’obtenir un ensemble optimal de régions structurellement homogènes pour une mesure de similarité donnée sur tout le cerveau. (2) dans une approche spatio-temporelle qui utilise les connexions anatomiques, calculées à partir des données d’IRMd, pour contraindre la dynamique des sources. Ces méthodes sont appliquée à des données synthétiques et réelles

Using diffusion MR information to reconstruct networks of brain activations from MEG and EEG measurements

Comprendre comment différentes régions du cerveau interagissent afin d’exécuter une tâche, est un défi très complexe. La magnéto- et l’électroencéphalographie (MEEG) sont deux techniques non-invasive d’imagerie fonctionnelle utilisées pour mesurer avec une bonne résolution temporelle l’activité cérébrale. Estimer cette activité à partir des mesures MEEG est un problème mal posé. Il est donc crucial de le régulariser pour obtenir une solution unique. Il a été montré que l’homogénéité structurelle des régions corticales reflète leur homogénéité fonctionnelle. Un des buts principaux de ce travail est d’utiliser cette information structurelle pour définir des a priori permettant de contraindre de manière plus anatomique ce problème inverse de reconstruction de sources. L’imagerie par résonance magnétique de diffusion (IRMd) est, à ce jour, la seule technique non-invasive qui fournisse des informations sur l’organisation structurelle de la matière blanche. Cela justifie son utilisation pour contraindre notre problème inverse. Nous utilisons l’information fournie par l’IRMd de deux manière différentes pour reconstruire les activations du cerveau : (1) via une méthode spatiale qui utilise une parcellisation du cerveau pour contraindre l’activité des sources. Ces parcelles sont obtenues par un algorithme qui permet d’obtenir un ensemble optimal de régions structurellement homogènes pour une mesure de similarité donnée sur tout le cerveau. (2) dans une approche spatio-temporelle qui utilise les connexions anatomiques, calculées à partir des données d’IRMd, pour contraindre la dynamique des sources. Ces méthodes sont appliquée à des données synthétiques et réelles.Understanding how brain regions interact to perform a given task is a very challenging task. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive functional imaging modalities used to record brain activity with high temporal resolution. As estimating brain activity from these measurements is an ill-posed problem. We thus must set a prior on the sources to obtain a unique solution. It has been shown in previous studies that structural homogeneity of brain regions reflect their functional homogeneity. One of the main goals of this work is to use this structural information to define priors to constrain more anatomically the MEG/EEG source reconstruction problem. This structural information is obtained using diffusion magnetic resonance imaging (dMRI), which is, as of today, the unique non-invasive structural imaging modality that provides an insight on the structural organization of white matter. This makes its use to constrain the EEG/MEG inverse problem justified. In our work, dMRI information is used to reconstruct brain activation in two ways: (1) In a spatial method which uses brain parcels to constrain the sources activity. These parcels are obtained by our whole brain parcellation algorithm which computes cortical regions with the most structural homogeneity with respect to a similarity measure. (2) In a spatio-temporal method that makes use of the anatomical connections computed from dMRI to constrain the sources’ dynamics. These different methods are validated using synthetic and real data