Section: New Results

Statistical models for Neuroscience

Hemodynamically informed parcellation of cerebral fMRI data

Participants : Florence Forbes, Aina Frau-Pascual, Thomas Vincent.

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

Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemodynamic response function (HRF). To cope with this issue, the joint detection-estimation (JDE) framework has been proposed. This formalism enables to estimate a HRF per region but for doing so, it assumes a prior brain partition (or parcellation) regarding hemodynamic territories (eg. [14] ). This partition has to be accurate enough to recover accurate HRF shapes but has also to overcome the detection-estimation issue: the lack of hemodynamics information in the non-active positions. During the internship of A. Frau Pascual at Neurospin, we proposed an hemodynamically-based parcellation, consisting first of a feature extraction step, followed by a Gaussian Mixture-based parcellation, which considers the injection of the activation levels in the parcellation process, in order to overcome the detection-estimation issue and find the underlying hemodynamics. The work has been submitted to the ICASSP conference in 2014.

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 as in the JDE framework mentioned in the previous section. 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 [63] . Corresponding papers [31] , [45] . A synthesis can also be found in the PhD manuscript of C. Bakhous (Grenoble University, December 2013) [11] .

Bayesian Joint Detection-Estimation of cerebral vasoreactivity from ASL fMRI data

Participants : Florence Forbes, Thomas Vincent.

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

Functional MRI (fMRI) is the method of choice to non-invasively probe cerebral activity evoked by a set of controlled experimental conditions. A rising fMRI modality is Arterial Spin Labeling (ASL) which enables to quantify the cerebral perfusion, namely the cerebral blood flow (CBF) and emerges as a more direct biomarker of neuronal activity than the standard BOLD (Blood Oxygen Level Dependent) fMRI.

Although the study of cerebral vasoreactivity using fMRI is mainly conducted through the BOLD fMRI modality (see the two previous sections), owing to its relatively high signal-to-noise ratio (SNR), ASL fMRI provides a more interpretable measure of cerebral vasoreactivity than BOLD fMRI. Still, ASL suffers from a low SNR and is hampered by a large amount of physiological noise. Our contribution, described in [43] , [44] aims at improving the recovery of the vasoreactive component from the ASL signal. To this end, a Bayesian hierarchical model is proposed, enabling the recovery of perfusion levels as well as fitting their dynamics. On a single-subject ASL real data set involving perfusion changes induced by hypercapnia, the approach is compared with a classical GLM-based analysis. A better goodness-of-fit is achieved, especially in the transitions between baseline and hypercapnia periods. Also, perfusion levels are recovered with higher sensitivity and show a better contrast between gray- and white matter.

Physiologically-inspired Bayesian analysis of BOLD and ASL fMRI data

Participants : Florence Forbes, Thomas Vincent, Jennifer Sloboda.

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

The ASL modality is most commonly used as a static measure where the average perfusion is computed over a volume sequence lasting several minutes. Recently, ASL has been used in functional activation protocols and hence gives access to a dynamic measure of perfusion, namely the variations of CBF which are elicited by specific tasks. ASL MRI mainly consists of acquiring pairs of control and label images and looking at the average control-label difference. The Signal-to-Noise Ratio (SNR) of this difference is very low so that several hundreds of image pairs need to be acquired, thus increasing significantly the time spent by the subject in the scanner and making the acquisition very sensitive to the patient's movement. In addition, this averaging requires that the perfusion signal is at a steady state, limiting the scope of fMRI task experiments to baseline perfusion measurements or long block designs. In contrast, it is highly desirable to measure change in perfusion due to an effect of interest in activation paradigms from event-related designs. It is technically possible to collect event-related ASL data but most approaches to functional ASL data analysis use a standard linear model (GLM-based) formulation with regressors encoding differences in control/tag scans and both ASL and BOLD activation signals being associated with the same canonical response function. The canonical hemodynamic response function (HRF) is generally used although it has been been calibrated on BOLD experiments only, thus reflecting simultaneous variations of CBF, cerebral blood volume (CBV) and cerebral oxygen consumption (CMRO2). In contrast, the perfusion signal only reflects variation in CBF so that the associated response, the perfusion response function (PRF), is likely to differ from the HRF. In the internship proposal of Jennifer Sloboda, we proposed to recover both a hemodynamic (BRF for BOLD response function) and a perfusion (PRF) response functions from event-related functional ASL data. To do so, a joint detection estimation (JDE) formalism was used. In the BOLD context, the JDE framework has proven to successfully extract the HRF while also performing activation detection. We had recently extended this formalism (see Section 6.2.3 and [43] , [44] ) to model an additional perfusion component linked to the BOLD one through a common activation detection. The main issue addressed then was to characterize the link between BOLD and perfusion components. To establish this link, we proposed a methodological axis which consists of developing a physiologically-inspired approach. To do so, dynamical non-linear equations available in physiological models were linearized and approximated in a parsimonious way so as to establish prior relations between the perfusion and BOLD responses which can be injected in our Bayesian setting. The inference of the initial model is currently done through a Markov Chain Monte Carlo approach but a Variational Expectation-Maximization implementation is also conceivable. As such, the tasks were two-fold: (1) investigate the physiological model and (2) inject it into the JDE setting. Investigation of the physiological model allows for: (1) creation of artificial fMRI data, (2) investigation of the relationship between physiological changes and the resulting simulated BOLD or ASL signal, and (3) characterization of the link between BOLD and perfusion responses. Injection of the physiologically inspired prior into the JDE model, is to (1) improve perfusion response recovery, (2) determine physiologically quantified units to the JDE recovered values This work is going to serve as a preliminary investigation into the incorporation of physiological information in the Bayesian JDE setting from which to determine the trajectory of future model developments.