2021-09-01
The effects of multi-echo fMRI combination and rapid T<sub>2</sub>*-mapping on offline and real-time BOLD sensitivity
Publication
Publication
Cell , Volume 184 - Issue 18 p. 4784- 4818.e17
<p>A variety of strategies are used to combine multi-echo functional magnetic resonance imaging (fMRI) data, yet recent literature lacks a systematic comparison of the available options. Here we compare six different approaches derived from multi-echo data and evaluate their influences on BOLD sensitivity for offline and in particular real-time use cases: a single-echo time series (based on Echo 2), the real-time T<sub>2</sub>*-mapped time series (T<sub>2</sub>*FIT) and four combined time series (T<sub>2</sub>*-weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T<sub>2</sub>*FIT-weighted). We compare the influences of these six multi-echo derived time series on BOLD sensitivity using a healthy participant dataset (N = 28) with four task-based fMRI runs and two resting state runs. We show that the T<sub>2</sub>*FIT-weighted combination yields the largest increase in temporal signal-to-noise ratio across task and resting state runs. We demonstrate additionally for all tasks that the T<sub>2</sub>*FIT time series consistently yields the largest offline effect size measures and real-time region-of-interest based functional contrasts and temporal contrast-to-noise ratios. These improvements show the promising utility of multi-echo fMRI for studies employing real-time paradigms, while further work is advised to mitigate the decreased tSNR of the T<sub>2</sub>*FIT time series. We recommend the use and continued exploration of T<sub>2</sub>*FIT for offline task-based and real-time region-based fMRI analysis. Supporting information includes: a data repository (https://dataverse.nl/dataverse/rt-me-fmri), an interactive web-based application to explore the data (https://rt-me-fmri.herokuapp.com/), and further materials and code for reproducibility (https://github.com/jsheunis/rt-me-fMRI).</p>
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| doi.org/10.1016/j.neuroimage.2021.118244, hdl.handle.net/1765/136030 | |
| Cell | |
| Organisation | Erasmus MC: University Medical Center Rotterdam |
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arcOGEN Consortium, HUNT All-In Pain, ARGO Consortium, Regeneron Genetics Center, C.G. (Cindy) Boer, Konstantinos Hatzikotoulas, … J.B.J. (Joyce) van Meurs. (2021). Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell, 184(18), 4784–4818.e17. doi:10.1016/j.cell.2021.07.038
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