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Section: New Results
Statistical models for Neuroscience
Comparison of stochastic and variational solutions to ASL fMRI data analysis
Participants : Florence Forbes, Aina Frau Pascual.
Joint work with: Philippe Ciuciu from Team Parietal and Neurospin, CEA Saclay.
Functional Arterial Spin Labeling (fASL) MRI can provide a quantitative measurement of changes of cerebral blood flow induced by stimulation or task performance. fASL data is commonly analysed using a general linear model (GLM) with regressors based on the canonical hemodynamic response function. In this work [37] , we consider instead a joint detection-estimation (JDE) framework which has the advantage of allowing the extraction of both task-related perfusion and hemodynamic responses not restricted to canonical shapes. Previous JDE attempts for ASL have been based on computer intensive sampling (MCMC) methods. Our contribution is to provide a comparison with an alternative variational expectation-maximization (VEM) algorithm on synthetic and real data. Other investigations were related to the use of appropriate physiological information and priors [39] , [38] .
A differential evolution-based approach for fitting a nonlinear biophysical model to fMRI BOLD data
Participants : Florence Forbes, Pablo Mesejo.
Joint work with: Jan Warnking from Grenoble Institute of Neuroscience.
Physiological and biophysical models have been proposed to link neuronal activity to the Blood Oxygen Level-Dependent (BOLD) signal in functional MRI (fMRI). Those models rely on a set of parameter values that cannot always be extracted from the literature. In some applications, interesting insight into the brain physiology or physiopathology can be gained from an estimation of the model parameters from measured BOLD signals. This estimation is challenging because there are more than 10 potentially interesting parameters involved in nonlinear equations and whose interactions may result in identifiability issues. However, the availability of statistical prior knowledge about these parameters can greatly simplify the estimation task. In this work we focus on the extended Balloon model and propose the estimation of 15 parameters using two stochastic approaches: an Evolutionary Computation global search method called Differential Evolution (DE) and a Markov Chain Monte Carlo version of DE. To combine both the ability to escape local optima and to incorporate prior knowledge, we derive the target function from Bayesian modeling. The general behavior of these algorithms is analyzed and compared with the de facto standard Expectation Maximization Gauss-Newton (EM/GN) approach, providing very promising results on challenging real and synthetic fMRI data sets involving rats with epileptic activity. These stochastic optimizers provided a better performance than EM/GN in terms of distance to the ground truth in 4 out of 6 synthetic data sets and a better signal fitting in 12 out of 12 real data sets. Non-parametric statistical tests showed the existence of statistically significant differences between the real data results obtained by DE and EM/GN. Finally, the estimates obtained from DE for these parameters seem both more realistic and more stable or at least as stable across sessions as the estimates from EM/GN. This work will appear in [29] . A preliminary version has also been accepted at the conference MICCAI 2015 [40] .
Multi-subject joint parcelation detection estimation in functional MRI
Participant : Florence Forbes.
Joint work with: Lotfi Chaari, Mohanad Albughdadi, Jean-Yves Tourneret from IRIT-ENSEEIHT in Toulouse and Philippe Ciuciu from Neurospin, CEA Saclay.
fMRI experiments are usually conducted over a population of interest for investigating brain activity across different regions, stimuli and subjects. Multi-subject analysis usually proceeds in two steps: an intra-subject analysis is performed sequentially on each individual and then a group-level analysis is carried out to report significant results at the population level. This work considers an existing Joint Parcellation Detection Estimation (JPDE) model which performs joint hemodynamic parcellation, brain dynamics estimation and evoked activity detection. The hierarchy of the JPDE model is extended for multi-subject analysis in order to perform group-level parcellation. Then, the corresponding underlying dynamics is estimated in each parcel while the detection and estimation steps are iterated over each individual. Validation on synthetic and real fMRI data shows its robustness in inferring group-level parcellation and the corresponding hemodynamic profiles. This work has been accepted at ISBI 2016.
Tumor classification and prediction using robust multivariate clustering of multiparametric MRI
Participants : Florence Forbes, Alexis Arnaud.
Joint work with: Emmanuel Barbier and Benjamin Lemasson from Grenoble Institute of Neuroscience.
Advanced statistical clustering approaches are promising tools to better exploit the wealth of MRI information especially on large cohorts and multi-center studies. In neuro-oncology, the use of multiparametric MRI may better characterize brain tumor heterogeneity. To fully exploit multiparametric MRI (e.g. tumor classification), appropriate analysis methods are yet to be developed. They offer improved data quality control by allowing automatic outlier detection and improved analysis by identifying discriminative tumor signatures with measurable predictive power. In this work, we show on small animals data that advanced statistical learning approaches can help 1) in organizing existing data by detecting and excluding outliers and 2) in building a dictionary of tumor fingerprints from a clustering analysis of their microvascular features. The work also now includes the integration in a joint statistical model of both automatic ROI delineation and clustering for whole brain data analysis. A preliminary version of this work has been accepted to the ISMRM 2015 conference and in the SFMRMB 2015 conference [41] .
Functional specifications of a brain segmentation software
Participants : Florence Forbes, Priscillia Previtero.
Joint work with: Michel Dojat from Grenoble Institute of Neuroscience and Senan Doyle from Pixyl.
The goal of P. Previtero's internship was to help with a number of software engineering tasks and communications actions around the P-Locus software and the Pixyl start-up. The internship resulted in particular in a new web site for Pixyl.