Premium
Dynamic‐flip‐angle ECG‐gating with nuisance signal regression improves resting‐state BOLD functional connectivity mapping by reducing cardiogenic noise
Author(s) -
Hu Chenxi,
Tokoglu Fuyuze,
Scheinost Dustin,
Qiu Maolin,
Shen Xilin,
Peters Dana C.,
Galiana Gigi,
Constable R. Todd
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27775
Subject(s) - resting state fmri , gating , flip angle , communication noise , functional magnetic resonance imaging , white matter , computer science , neuroscience , magnetic resonance imaging , medicine , psychology , linguistics , philosophy , radiology
Purpose To investigate an ECG‐gated dynamic‐flip‐angle BOLD sequence with improved robustness against cardiogenic noise in resting‐state fMRI. Methods ECG‐gating minimizes the cardiogenic noise but introduces T 1 ‐dependent signal variation, which is minimized by combination of a dynamic‐flip‐angle technique and retrospective nuisance signal regression (NSR) using signals of white matter, CSF, and global average. The technique was studied with simulations in a wide range of T 1 and B 1 fields and phantom imaging with pre‐programmed TR variations. Resting‐state fMRI of 20 healthy subjects was acquired with non‐gated BOLD (NG), ECG‐gated constant‐flip‐angle BOLD (GCFA), ECG‐gated BOLD with retrospective T 1 ‐correction (GRC), and ECG‐gated dynamic‐flip‐angle BOLD (GDFA), all processed by the same NSR method. GDFA was compared to alternative methods over temporal SNR (tSNR), seed‐based connectivity, and whole‐brain voxelwise connectivity based on intrinsic connectivity distribution (ICD). A previous large‐cohort data set ( N = 100) was used as a connectivity gold standard. Results Simulations and phantom imaging show substantial reduction of the T 1 ‐dependent signal variation with GDFA alone, and further reduction with NSR. The resting‐state study shows improved tSNR in the basal brain, comparing GDFA to NG, after both processed with NSR. Furthermore, GDFA significantly improved subcortical–subcortical and cortical–subcortical connectivity for several representative seeds and significantly improved ICD in the brainstem, thalamus, striatum, and prefrontal cortex, compared to the other 3 approaches. Conclusion GDFA with NSR improves mapping of the resting‐state functional connectivity of the basal–brain regions by reducing cardiogenic noise.