Total activation: fMRI deconvolution through spatio-temporal regularization

Confirmatory approaches to fMRI data analysis look for evidence for the presence of pre-defined regressors modeling contributions to the voxel time series, including the BOLD response following neuronal activation. As more complicated questions arise about brain function, such as spontaneous and resting-state activity, new methodologies are required. We propose total activation (TA) as a novel fMRI data analysis method to explore the underlying activity-inducing signal of the BOLD signal without any timing information that is based on sparse spatio-temporal priors and characterization of the hemodynamic system. Within a variational framework, we formulate a convex cost function-including spatial and temporal regularization terms-that is solved by fast iterative shrinkage algorithms. The temporal regularization expresses that the activity-inducing signal is block-type without restrictions on the timing nor duration. The spatial regularization favors coherent activation patterns in anatomically-defined brain regions.TA is evaluated using a software phantom and an event-related fMRI experiment with prolonged resting state periods disturbed by visual stimuli. The results illustrate that both block-type and spike-type activities can be recovered successfully without prior knowledge of the experimental paradigm. Further processing using hierarchical clustering shows that the activity-inducing signals revealed by TA contain information about meaningful task-related and resting-state networks, demonstrating good abilities for the study of non-stationary dynamics of brain activity.

Graphical abstractDownload high-res image (355KB)Download full-size imageHighlights► Total activation reveals activity-inducing signal without paradigm information. ► Temporal prior favors block-like activity driven by sparse innovation signal. ► Spatial prior incorporates anatomical atlas to cope with high spatial variability. ► Presence of visual stimuli is recovered without prior knowledge of timing. ► Hierarchical clustering of activity signals reveals resting state networks.