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Hum Brain Mapp. 2017 Nov;38(11):5778-5794. doi: 10.1002/hbm.23767. Epub 2017 Aug 16.

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

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School of Physics and Astronomy and Sir Peter Mansfield Imaging Centre, The University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom.
Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo, 315100, People's Republic of China.
Basque Center of Cognition, Brain and Language, San Sebastian, 20009, Spain.
Birmingham University Imaging Centre, School of Psychology, University of Birmingham, Birmingham, B15 2TT, United Kingdom.
School of Mathematical Sciences, The University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom.


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.


activation likelihood estimation; decoding; functional MRI; meta-analysis; paradigm free mapping

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