Section: New Results

Statistical models for Neuroscience

Variational approach for the joint estimation-detection of Brain activity from functional MRI data

Participants : Florence Forbes, Lotfi Chaari, Thomas Vincent.

Joint work with: Michel Dojat (Grenoble Institute of Neuroscience) and Philippe Ciuciu from Neurospin, CEA in Saclay.

In standard within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model mis-specification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery. Main corresponding paper [13]

Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework

Participants : Florence Forbes, Lotfi Chaari, Thomas Vincent.

Joint work with: Philippe Ciuciu from Team Parietal and Neurospin, CEA in Saclay.

Identifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool. Corresponding papers [29] , [28] .

Variational variable selection to assess experimental condition relevance in event-related fMRI

Participants : Florence Forbes, Christine Bakhous, Lotfi Chaari, Thomas Vincent, Farida Enikeeva.

Joint work with: Michel Dojat (Grenoble Institute of Neuroscience) and Philippe Ciuciu from Neurospin, CEA in Saclay.

Brain functional exploration investigates the nature of neural processing following cognitive or sensory stimulation. This goal is not fully accounted for in most functional Magnetic Resonance Imaging (fMRI) analysis which usually assumes that all delivered stimuli possibly generate a BOLD response everywhere in the brain although activation is likely to be induced by only some of them in specific brain regions. Generally, criteria are not available to select the relevant conditions or stimulus types (e.g. visual, auditory, etc.) prior to activation detection and the inclusion of irrelevant events may degrade the results, particularly when the Hemodynamic Response Function (HRF) is jointly estimated. To face this issue, we propose an efficient variational procedure that automatically selects the conditions according to the brain activity they elicit. It follows an improved activation detection and local HRF estimation that we illustrate on synthetic and real fMRI data. This approach is an alternative to our previous approach based on Monte-Carlo Markov Chain (MCMC) inference [25] . Corresponding paper [26] .

Bayesian BOLD and perfusion source separation and deconvolution from functional ASL imaging

Participants : Florence Forbes, Thomas Vincent.

In the context of ARC AINSI project, joint work with: Philippe Ciuciu from Neurospin, CEA in Saclay.

In many neuroscience applications, the Arterial Spin Labeling (ASL) fMRI modality arises as a preferable choice to the standard BOLD modality due to its ability to provide a quantitative measure of the Cerebral Blood Flow (CBF). Such a quantification is central but generally performed without consideration of a specific modeling of the perfusion component in the signal often handled via standard GLM approaches using the BOLD canonical response function as regressor. In this work, we propose a novel Bayesian hierarchical model of the ASL signal which allows activation detection and both the extraction of a perfusion and a hemodynamic component. Validation on synthetic and real data sets from event-related ASL show the ability of our model to address the source separation and double deconvolution problems inherent to ASL data analysis.

Extraction of physiological components in functional ASL data

Participants : Florence Forbes, Thomas Vincent, Lotfi Chaari, Marc Guillotin.

In the context of ARC AINSI project, joint work with: Jan Warnking (Grenoble Institute of Neuroscience) and Philippe Ciuciu from Neurospin, CEA in Saclay.

The internship of Marc Guillotin has been supported by Le pole Cognition de Grenoble.

The goal of this work was to investigate Independent component analysis techniques to identify the part of the ASL signal due to physiological sources such as respiratory and cardiac components. Once identified those physiological components should be removed to produce an uncontaminated ASL signal. This preliminary work showed that the physiological effects were affecting all signal components and were therefore not easy to extract without removing some of the useful signal. More experiments should be made on real data from the GIN.

Comparison of processing workflows for ASL data analysis

Participant : Thomas Vincent.

In the context of ARC AINSI project, joint work with: Michel Dojat (Grenoble Institute of Neuroscience), Philippe Ciuciu from Neurospin, CEA in Saclay, Remi Dubujet, Elise Bannier, Isabelle Courouge, Christian Barillot, Camille Maudet from EPI Visages in Rennes.

We assessed and compared the performance of different ASL processing pipelines in order to promote one using specific indexes (Contrast to noise ratio, partial volume effect, et ). We proposed to assess the impact of the pipelines based on the quality of the final corrected ASL images using a common set of subjects for all workflows. We leaned on the expertise of the Visages and GIN teams on ASL, and first started from existing attempts made in the teams. At the moment, there is a striking lack of such guidelines. The recent toolbox ASLtbx proposes a number of procedures that are based on very standard tools (e.g. SPM) and do not make use of more efficient approaches from more recent literature. Similarly, in the BIRN project, processing pipelines are mentioned but none are currently available.