Biomedical subjects

Guillaume Marrelec

Publications and source records attributed to Guillaume Marrelec.

5 recordsLinked to original sources

PubMed2006

Partial correlation for functional brain interactivity investigation in functional MRI.

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 interactions. While many methods exist to select regions (Step 1), very little is available to complete Step 2. The two main methods developed so far, structural equation modeling (SEM) and dynamical causal modeling (DCM), usually require precise prior information to be used, while such information is sometimes lacking. Assuming that Step 1 was successfully completed, we here propose a data-driven method to deal with Step 2 and extract functional interactions from fMRI datasets through partial correlations. Partial correlation is more closely related to effective connectivity than marginal correlation and provides a convenient graphical representation for functional interactions. As an instance of brain interactivity investigation, we consider how simple hand movements are processed by the bihemispheric cortical motor network. In the proposed framework, Bayesian analysis makes it possible to estimate and test the partial statistical dependencies between regions without any prior model on the underlying functional interactions. We demonstrate the interest of this approach on real data.

Cerebral Cortex↗

PubMed2004

Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference.

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-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how it can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data.

Adolescent↗

PubMed2003

Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information.

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 previously proposed method that makes use of basic yet relevant temporal information about the underlying physiological process of the brain BOLD response in order to infer the HRF in a Bayesian framework. A general hypothesis test is also proposed, allowing to take advantage of the knowledge gained regarding the HRF to perform activation detection. The performances of the method are then evaluated by simulation. Great improvement is shown compared to the Maximum-Likelihood estimate in terms of estimation error, variance, and bias. Robustness of the estimators with regard to the actual noise structure or level, as well as the stimulus sequence, is also proven. Lastly, fMRI data with an event-related paradigm are analyzed. As suspected, the regions selected from highly discriminating activation maps resulting from the method exhibit a certain inter-regional homogeneity in term of HRF shape, as well as noticeable inter-regional differences.

Adult↗

PubMed2003

Estimation of the Hemodynamic Response Function in event-related functional MRI: directed acyclic graphs for a general Bayesian inference framework.

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 Bayesian model for HRF estimation and show how it can beneficially be translated in terms of graphical models, leading to (i) a clear and efficient representation of all structural and functional relationships entailed by the model, and (ii) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data.

Adolescent↗

PubMed2003

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment.

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 region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

Algorithms↗