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Section: New Results

Statistical models for Neuroscience

Physiologically informed Bayesian analysis of ASL fMRI data

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

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

ASL fMRI data provides a quantitative measure of blood perfusion, that can be correlated to neuronal activation. In contrast to BOLD measure, it is a direct measure of cerebral blood flow. However, ASL data has a lower SNR and resolution so that the recovery of the perfusion response of interest suffers from the contamination by a stronger BOLD component in the ASL signal. In this work [38] , [39] we consider a model of both BOLD and perfusion components within the ASL signal. A physiological link between these two components is analyzed and used for a more accurate estimation of the perfusion response function in particular in the usual ASL low SNR conditions.

Physiological models comparison for the analysis of ASL fMRI data

Participants : Florence Forbes, Aina Frau Pascual.

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

Physiological models have been proposed to describe the processes that underlie the link between neural and hemodynamic activity in the brain. Among these, the Balloon model describes the changes in blood flow, blood volume and oxygen concentration when an hemodynamic response is ensuing neural activation. Next, a BOLD signal model links these variables to the measured BOLD signal. Taken together, these equations allow the precise modeling of the coupling between the cerebral blood flow (CBF) and hemodynamic response (HRF). However, several competing versions of BOLD signal model have been described in the past. In this work, we compare different physiological models linking CBF to HRF and different BOLD signal models too in terms of least squares error and log-likelihood, and we assess the impact of this setting in the context of Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI) data analysis.

Variational EM for the analysis of ASL fMRI data

Participants : Florence Forbes, Aina Frau Pascual.

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

In this work, the goal is to analyse ASL data by accounting jointly for both the BOLD and perfusion components in the signal. Using the model proposed in [77] , we design a variational EM approach to estimate the model parameters as a faster alternative to the MCMC approach used in [77] and [39] .

Metaheuristics for the analysis of fMRI data

Participants : Florence Forbes, Pablo Mesejo Santiago.

Joint work with: Jan Warnking from Grenoble Institute of Neuroscience.

The undergoing work is focused on the optimization of nonlinear models for fMRI data analysis, specially Blood-oxygen-level dependent (BOLD) MR modality. The current optimization procedure consists of a Bayesian inversion of the nonlinear model using a Gauss-Newton/Expectation-Maximization algorithm. Such an optimization procedure is time-consuming and achieves sub-optimal results. Therefore, the current research work is mainly focused on improving these results by experimenting with global search optimization methods, like metaheuristics (MHs). Secondly, MHs can also be of great help in the development of minimization algorithms for solving problems with orthogonality constraints (like in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, matrix rank minimization, etc.). Thus, another main research line is concerned with the application of MHs to this problem and, if necessary, the design and implementation of new evolutionary operators that preserve orthogonality. And, finally, we are also trying to create advanced statistical models for coupling Arterial Spin Labeling (ASL) and BOLD MR modalities to study brain function.

Model selection for hemodynamic brain parcellation in fMRI

Participant : Florence Forbes.

Joint work with: Lotfi Chaari, Mohanad Albughdadi, Jean-Yves Tourneret from IRIT-ENSEEIHT in Toulouse and Philippe Ciuciu from Neurospin, CEA in Saclay.

Brain parcellation into a number of hemodynamically homogeneous regions (parcels) is a challenging issue in fMRI analyses. This task has been recently integrated in the joint detection-estimation (JDE) resulting in the so-called joint detection-parcellation-estimation (JPDE) model. JPDE automatically estimates the parcels from the fMRI data but requires the desired number of parcels to be fixed. This is potentially critical in that the chosen number of parcels may influence detection-estimation performance. In this paper [30] , we propose a model selection procedure to automatically fix the number of parcels from the data. The selection procedure relies on the calculation of the free energy corresponding to each concurrent model, within the variational expectation maximization framework. Experiments on synthetic and real fMRI data demonstrate the ability of the proposed procedure to select an adequate number of parcels. We also investigated the use of Latent Dirichlet Processes.

Partial volume estimation in brain MRI revisited

Participant : Florence Forbes.

Joint work with: Alexis Roche from Siemens Advanced Clinical Imaging Technology, Department of Radiology, CHUV, Signal Processing Laboratory (LTS5), EPFL, Lausanne, Switzerland.

Image-guided diagnosis of brain disease calls for accurate morphometry algorithms, e.g., in order to detect focal atrophy patterns relating to early-stage progression of particular forms of dementia. To date, widely used brain morphometry packages rest upon discrete Markov random field (MRF) image segmentation models that ignore, or do not fully account for partial voluming, leading to potentially inaccurate estimation of tissue volumes. Although several partial volume (PV) estimation methods have been proposed in the literature from the early 90's, none of them seems to be in common use. In [43] , we propose a fast algorithm to estimate brain tissue concentrations from conventional T1-weighted images based on a Bayesian maximum a posteriori formulation that extends the "mixel" model developed in the 90's. A key observation is the necessity to incorporate additional prior constraints to the "mixel" model for the estimation of plausible concentration maps. Experiments on the ADNI standardized dataset show that global and local brain atrophy measures from the proposed algorithm yield enhanced diagnosis testing value than with several widely used soft tissue labeling methods.

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. Future work should include the integration in a joint statistical model of both automatic ROI delineation and clustering for whole brain data analysis, with a better use of anatomical information. This work has been submitted to the ISMRM 2015 conference and accepted in the SFMRMB 2015 conference [45] .