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 average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements.
The decoding scores were considerably lower for eye movements relative to other movement types tested. We validated the framework using simultaneous electromyography (EMG)–fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. 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 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. In this study, we developed a decoding method for SPFM using a coordinate‐based meta‐analysis method of activation likelihood estimation (ALE).
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. Most functional MRI (fMRI) studies map task‐driven brain activity using a block or event‐related paradigm.
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