Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimatesTools Tan, Francisca M., Caballero-Gaudes, César, Mullinger, Karen J., Cho, Siu-Yeung, Zhang, Yaping, Dryden, Ian L., Francis, Susan T. and Gowland, Penny A. (2017) Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates. Human Brain Mapping, 38 (11). pp. 5778-5794. ISSN 1097-0193 Full text not available from this repository.AbstractMost 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.
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