Total activation: fMRI deconvolution through spatio-temporal regularization

Abstract

a b s t r a c t a r t i c l e i n f oConfirmatory 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 termsthat 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 nonstationary dynamics of brain activity.

Beyond the “Pain Matrix,” inter-run synchronization during mechanical nociceptive stimulation

“…Some recently proposed methods have used the detection of point processes to model the BOLD responses in both resting state and task-related activations. Another approach, the “total activation model,” was proposed by Karahanoglu et al (2013). This method consists in finding an innovation system that should be sparse if the response evoked by the task (or at rest) is driven specifically by the task.…”

Nonparametric Hemodynamic Deconvolution of fMRI Using Homomorphic Filtering

“…The performance of the algorithm was tested using simulated data and it was also applied to experimental data using both block and event-related designs. Recently Karahanoğlu et al [37] proposed a method called "total activation" which implemented a variational framework. A cost function was formulated by including spatial and temporal regularization terms which were solved using iterative shrinkage algorithms.…”

Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap

“…We used the generalized forward-backward splitting algorithm in order to solve the regularization problem in temporal and space domains [22]. The algorithm was presented in detail in our previous work [20].…”

“…In order to extract the iCAPs, we first employ the Total Activation (TA) framework to detect intrinsic transient activations, represented as a sparse signal, for each voxel [20]. TA is cast as an optimization problem using regularization terms with spatial and temporal priors specifically tuned for fMRI data analysis.…”