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Abstract
Purpose
To report the use of multiband accelerated echo-planar imaging (EPI) for resting-state functional MRI (rs-fMRI) to achieve rapid high temporal resolution at 3T compared to conventional EPI.
Materials and Methods
rs-fMRI data were acquired from 20 healthy right-handed volunteers by using three methods: conventional single-band gradient-echo EPI acquisition (Data 1), multiband gradient-echo EPI acquisition with 240 volumes (Data 2) and 480 volumes (Data 3). Temporal signal-to-noise ratio (tSNR) maps were obtained by dividing the mean of the time course of each voxel by its temporal standard deviation. The resting-state sensorimotor network (SMN) and default mode network (DMN) were estimated using independent component analysis (ICA) and a seed-based method. One-way analysis of variance (ANOVA) was performed between the tSNR map, SMN, and DMN from the three data sets for between-group analysis. P < 0.05 with a family-wise error (FWE) correction for multiple comparisons was considered statistically significant.
Results
One-way ANOVA and post-hoc two-sample t-tests showed that the tSNR was higher in Data 1 than Data 2 and 3 in white matter structures such as the striatum and medial and superior longitudinal fasciculus. One-way ANOVA revealed no differences in SMN or DMN across the three data sets.
Conclusion
Within the adapted metrics estimated under specific imaging conditions employed in this study, multiband accelerated EPI, which substantially reduced scan times, provides the same quality image of functional connectivity as rs-fMRI by using conventional EPI at 3T. Under employed imaging conditions, this technique shows strong potential for clinical acceptance and translation of rs-fMRI protocols with potential advantages in spatial and/or temporal resolution. However, further study is warranted to evaluate whether the current findings can be generalized in diverse settings.
References
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Fig. 1.
One sample t-test results from group maps of temporal signal-to-noise ratio (tSNR) for (a) Data set 1 (conventional echo-planar imaging [EPI]), (b) Data set 2 (multiband EPI with 480 volumes) and (c) Data set 3 (multiband EPI with 240 volumes). The SPM{t}s had a threshold of P < 0.05, with family-wise error (FWE)-correction for multiple comparisons at the whole brain level. Whole brain mean tSNR graphs were shown in (d).
Fig. 2.
(a) One-way repeated ANOVA for the three data sets (Data set 1, Data set 2, Data set 3). The SPM{F}s had a threshold of P < 0.05, with FWE-correction for multiple comparisons at the whole brain level. Post-hoc two sample t-tests showed that the difference between Data set 1 and Data set 2 (b) and between Data set 1 and Data set 3 (c) were responsible for the difference shown in analysis of variance (ANOVA). The SPM{t}s had a threshold of P < 0.05, with FWE-correction for multiple comparisons at the whole brain level.
Fig. 3.
One sample t-test results of (a) default-mode networks and (b) right motor networks obtained from independent component analysis for three data sets (Data set 1, Data set 2, Data set 3). The SPM{t}s had a threshold of P < 0.05, with FWE-correction for multiple comparisons at the whole brain level. One-way repeated ANOVA for the three data sets did not show differences between data sets.
Fig. 4.
One sample t-test results of (a) default-mode networks and (b) right motor networks obtained from seed-based analysis for three data sets (Data set 1, Data set 2, Data set 3). The SPM{t}s had a threshold of P < 0.05, with FWE-correction for multiple comparisons at the whole brain level. One-way repeated ANOVA for the three data sets did not show differences between data sets.
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