Détection-estimation conjointe de l'activité cérébrale en imagerie par résonance magnétique fonctionnelle

Dans cette thèse, nous discutons et proposons un certain nombre de méthodes pour l'analyse intra-sujet de l'activité cérébrale en imagerie par résonance magnétique fonctionnelle (IRMf). L'IRMf est une modalité permettant d'explorer l'activité neuronale du cerveau. L'IRMf met en évidence l'activation de zones particulières du cortex cérébral en utilisant un agent de contraste endogène : la désoxyhémoglobine qui est paramagnétique et dont la concentration varie lors d'une activation neuronale. Dans ce manuscrit, nous commençons tout d'abord par présenter les différentes méthodes qui ont été proposées dans la littérature pour détecter les régions du cerveau qui sont activées par un paradigme expérimental. Cette analyse de détection nécessite une connaissance de la fonction de réponse hémodynamique (FRH). Nous présentons ensuite certaines approches utilisées en IRMf pour estimer la dynamique temporelle la FRH, nous décrivons notamment notre contribution à ce niveau. L'estimation de cette fonction se fait dans les régions du cerveau connues a priori comment étant activées. Généralement, détection et estimation sont faites séparément, alors qu'il est bien évident que les performances de chacune dépendent de la connaissance de l'autre. C'est pour cette raison que nous avons élaboré dans cette thèse une approche régionale où détection et estimation sont faites conjointement. Nous généralisons la méthode pour pouvoir utiliser cette technique pour tous les voxels du cerveau. Nos résultats offrent ainsi une description parcel par parcel du cerveau, pour chacune une forme spécifique de la FRH est fournie. La méthode fournie aussi une carte de classification des voxels en deux ou trois classes d'activité.In this thesis, we discuss and propose methods for within-subject functional Magnetic Resonance Imaging (fMRI) data analysis. fMRI is a recently developed neuroimaging technique with the capacity to map neural activity with high spatial precision. To localize activated brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. In the following manuscript, we first present the different approaches, proposed in the literature, to detect regions of the brain that are activated in a given experimental paradigm. Generally, such detection step needs to fix a model for the hemodynamic response function (HRF). We then describe some techniques used in fMRI to estimate the temporal dynamic of the HRF, we introduce our contribution among such techniques. Such estimation needs a prior knowledge of the localization of brain regions that are activated. Detection and estimation are generally performed separately, when it is well known that the performance of the one depends on the knowledge of the other. That's why, in this thesis we propose a regional approach where both detection and estimation are done at the same time. We extend our technique to deal with all brain voxels. Results give a parcel by parcel description of the brain, for every one, a specific HRF estimation is given. In addition, the method induces a spatial map of brain voxel classification in two or three activity classes.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Modeling non-linear and non-stationary effects of the BOLD response using mixture models in fMRI

blan

Joint detectionestimation of brain activity in functional MRI: a multichannel deconvolution solution

Different approaches have been considered so far to cope with the temporal correlation of fMRI data for brain activity detection. However, it has been reported that modeling this serial correlation has little influence on the estimate of the hemodynamic response function (HRF). In this paper, we examine this issue when performing a joint detectionestimation of brain activity in a given homogeneous region of interest (ROI). Following [1], we adopt a space-varying AR(1) temporal noise model and assess its influence, on both the estimation of the HRF and the detection of brain activity, using synthetic and real fMRI data. We show that this model yields a significant gain in detection specificity (lower false positive rate). 1

Bayesian joint detection-estimation of brain activity using MCMC with a Gamma-Gaussian mixture prior model

The classical approach of within-subject analysis in event-related functional Magnetic Resonance Imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and then on (ii) an estimation step to recover the temporal dynamics of the brain response. To date, specially in region-based analysis, the two questions have been addressed separately while intrinsically connected to each other. This situation motivates the need for new methods in neuroimaging that go beyond this unsatisfactory trade-off. In this paper, we propose a generalization of a region based Bayesian detectionestimation approach that addresses (i)-(ii) simultaneously as a bilinear inverse problem. The proposed extension relies on a 2-class Gamma-Gaussian prior mixture modeling to classify the voxels of the brain region either as activated or unactivated. Our approach provides both a spatial activity map and a HRF estimation using Monte Carlo Markov Chain (MCMC) techniques. Results show that this novel mixture model yields lower false positive rates and a better sensitivity in comparison with a 2-class Gaussian mixture. 1

SEMI-BLIND DECONVOLUTION OF NEURAL IMPULSE RESPONSE IN fMRI USING A GIBBS SAMPLING METHOD

In functional Magnetic Resonance Imaging (fMRI), the Hemodynamic Response Function (HRF) represents the impulse response of the neurovascular system. Its identification is essential for a better understanding of cerebral activity since it provides a typical time course of the response to a stimulus in a given region of interest (ROI). In recent work [1], the authors have developed an HRF estimation method based on a single time course. Here, we propose an extension that takes the spatial homogeneity of the HRF into account. Our hypothesis based on biological results is that a ROI can be characterized by a single HRF but varying magnitude in space. Our goal is to estimate those magnitudes that could then be interpreted as a correlate of the neural response. We are thus faced with a semi-blind deconvolution inverse problem since the time arrivals of the neural response are known: they correspond to stimuli timing. To cope with this issue, we introduce specific prior information about the HRF and the neural response. Finally, we develop a MCMC approach to approximate the posterior mean estimates of unknown quantities. Simulation results show the improvement brought by our formulation compared to the approach developed in [1]. 1

Bayesian joint detection-estimation of brain activity using MCMC with a Gamma-Gaussian mixture prior model

International audienceblan