Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization

Pablo Mesejo 1 Sandrine Saillet 2 Olivier David 2 Christian Bénar 3 Jan M. Warnking 2 Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIN - Grenoble Institut des Neurosciences
3 INS - Institut de Neurosciences des Systèmes
Abstract : Physiological and biophysical models have been proposed to link neural activity to the Blood Oxygen Level-Dependent (BOLD) signal in functional MRI (fMRI). They rely on a set of parameter values that cannot always be extracted from the literature. Their estimation is challenging because there are more than 10 potentially interesting parameters involved in non-linear equations and whose interactions may result in identifiability issues. However, the availability of statistical prior knowledge on 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 an Evolutionary Computation (EC) global search method. To combine both the ability to escape local optima and to incorporate prior knowledge, we derive the EC objective function from Bayesian modeling. This novel method provides promising results on a challenging real fMRI data set involving rats with epileptic activity and compares favorably with the conventional Expectation Maximization Gauss-Newton approach.
Keywords : Expectation Maximization Differential Evolution Evolutionary Computation Biophysical parameters BOLD signal functional MRI
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Submitted on : Tuesday, October 27, 2015 - 2:41:50 PM
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Pablo Mesejo, Sandrine Saillet, Olivier David, Christian Bénar, Jan M. Warnking, et al.. Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization. 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’15), Oct 2015, Munich, Germany. pp.528-535, ⟨10.1007/978-3-319-24571-3_63⟩. ⟨hal-01221126⟩

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