Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information, but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of activation likelihood estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the sensorimotor network (SMN) to six motor functions (left/right fingers, left/right toes, swallowing, and eye blinks). We validated the framework using simultaneous electromyography (EMG)-fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55% and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this article discusses methodological implications and improvements to increase the decoding performance. Hum Brain Mapp 38:5778-5794, 2017. © 2017 Wiley Periodicals, Inc.

Paradigm free mapping with sparse regression automatically detects single-trial functional magnetic resonance imaging blood oxygenation level dependent responses

The ability to detect single trial responses in functional magnetic resonance imaging (fMRI) studies is essential, particularly if investigating learning or adaptation processes or unpredictable events. We recently introduced paradigm free mapping (PFM), an analysis method that detects single trial blood oxygenation level dependent (BOLD) responses without specifying prior information on the timing of the events. PFM is based on the deconvolution of the fMRI signal using a linear hemodynamic convolution model. Our previous PFM method (Caballero-Gaudes et al., 2011: Hum Brain Mapp) used the ridge regression estimator for signal deconvolution and required a baseline signal period for statistical inference. In this work, we investigate the application of sparse regression techniques in PFM. In particular, a novel PFM approach is developed using the Dantzig selector estimator, solved via an efficient homotopy procedure, along with statistical model selection criteria. Simulation results demonstrated that, using the Bayesian information criterion to select the regularization parameter, this method obtains high detection rates of the BOLD responses, comparable with a model-based analysis, but requiring no information on the timing of the events and being robust against hemodynamic response function variability. The practical operation of this sparse PFM method was assessed with single-trial fMRI data acquired at 7T, where it automatically detected all task-related events, and was an improvement on our previous PFM method, as it does not require the definition of a baseline state and amplitude thresholding and does not compromise on specificity and sensitivity.