Skip to main content

Research Repository

Advanced Search

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

Tan, Francisca M.; Caballero-Gaudes, César; Mullinger, Karen J.; Cho, Siu-Yeung; Zhang, Yaping; Dryden, Ian L.; Francis, Susan T.; Gowland, Penny A.

Authors

Francisca M. Tan

César Caballero-Gaudes

Siu-Yeung Cho

Yaping Zhang

IAN DRYDEN IAN.DRYDEN@NOTTINGHAM.AC.UK
Professor of Statistics



Abstract

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.

Citation

Tan, F. M., Caballero-Gaudes, C., Mullinger, K. J., Cho, S., Zhang, Y., Dryden, I. L., …Gowland, P. A. (2017). Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates: Decoding fMRI Events in SMN. Human Brain Mapping, 38(11), 5778-5794. https://doi.org/10.1002/hbm.23767

Journal Article Type Article
Acceptance Date Aug 2, 2017
Online Publication Date Aug 16, 2017
Publication Date 2017-11
Deposit Date Aug 7, 2017
Publicly Available Date Aug 17, 2018
Journal Human Brain Mapping
Print ISSN 1065-9471
Electronic ISSN 1097-0193
Publisher Wiley
Peer Reviewed Not Peer Reviewed
Volume 38
Issue 11
Pages 5778-5794
DOI https://doi.org/10.1002/hbm.23767
Keywords functional MRI; decoding; meta-analysis; activation likelihood estimation; paradigm free mapping
Public URL https://nottingham-repository.worktribe.com/output/886668
Publisher URL http://onlinelibrary.wiley.com/doi/10.1002/hbm.23767/abstract
Additional Information This is the peer reviewed version of the following article: Tan, F. M., Caballero-Gaudes, C., Mullinger, K. J., Cho, S.-Y., Zhang, Y., Dryden, I. L., Francis, S. T. and Gowland, P. A. (2017), Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates. Hum. Brain Mapp., 38: 5778–5794, which has been published in final form at doi:10.1002/hbm.23767. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Files





You might also like



Downloadable Citations