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| Home > Publications database > The effects of multi-echo fMRI combination and rapid T*-mapping on offline and real-time BOLD sensitivity > print |
| 001 | 904407 | ||
| 005 | 20220131120426.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2021.118244 |2 doi |
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| 100 | 1 | _ | |a Heunis, Stephan |0 P:(DE-Juel1)187419 |b 0 |e Corresponding author |
| 245 | _ | _ | |a The effects of multi-echo fMRI combination and rapid T*-mapping on offline and real-time BOLD sensitivity |
| 260 | _ | _ | |a Orlando, Fla. |c 2021 |b Academic Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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 T2*-mapped time series (T2*FIT) and four combined time series (T2*-weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T2*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 T2*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 T2*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 T2*FIT time series. We recommend the use and continued exploration of T2*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). |
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| 700 | 1 | _ | |a Breeuwer, Marcel |0 P:(DE-HGF)0 |b 1 |
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| 700 | 1 | _ | |a Aldenkamp, Albert P |0 P:(DE-HGF)0 |b 8 |
| 773 | _ | _ | |a 10.1016/j.neuroimage.2021.118244 |g Vol. 238, p. 118244 - |0 PERI:(DE-600)1471418-8 |p 118244 - |t NeuroImage |v 238 |y 2021 |x 1053-8119 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/904407/files/1-s2.0-S1053811921005218-main.pdf |y OpenAccess |
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