A Paradigm Free Regularization Approach to Recover Brain Activation from Functional MRI Data

ContextFunctional MRI is a non-invasive imaging technique that provides an indirect view into brain activity, via the blood-oxygen-level-dependent (BOLD) response. In particular, resting-state fMRI poses challenges to the recovery of brain activity without prior knowledge on the experimental paradigm, as it is the case for task-fMRI. Conventional methods to infer brain activity from the fMRI signals, for example the general linear model (GLM), require the knowledge of the experimental paradigm to define regressors and estimate the contribution of each voxels time course to the task. To overcome this limitation, approaches to deconvolve the BOLD response and recover the underlying neural activation without a priori information on the task have been proposed. State-of-the-art techniques, and in particular the Total Activation (TA), formulates the deconvolution as an optimization problem with decoupled spatial and temporal regularization terms. This increases the number of hyperparameters to be set and requires an optimization strategy that alternates between the constraints. ApproachIn this work, we propose a paradigm-free regularization algorithm named Paradigm-Free fMRI (PF-fMRI) that is applied on the 4-D fMRI image, acting simultaneously in the 3-D space and 1-D time dimensions. Based on the idea that large image variations should be preserved as they occur during brain activation, whereas small variations considered as noise should be removed, the PF-fMRI applies an anisotropic regularization, thus recovering the location and the duration of brain activation. ResultsUsing the experimental paradigm as ground truth, the PF-fMRI is validated on synthetic and real task-fMRI data from 51 subjets, and its performance is compared to the TA. Results show higher correlations of the recovered time-courses with the ground truth compared to the TA and lower computational times. In addition, we show that the PF-fMRI recovers activity that agrees with the GLM, without requiring or using any knowledge of the experimental paradigm.