Assessing utility of fieldmapless SDC in ENIGMA Addiction Cue Reactivity tb-fMRI datasets
Poster No:
1594
Submission Type:
Abstract Submission
Authors:
Anthony Juliano1, Devarshi Pancholi1, Griffin Curtin1, McKinney Pitts1, Mehran Zare-Bidoky2, Anna Zilverstand3, Hamed Ekhtiari4, Hugh Garavan5
Institutions:
1University of Vermont, Burlington, VT, 2Tehran University of Medical Sciences, Tehran, Iran, Islamic Republic of, 3University of Minnesota, Minneapolis, MN, 4University of Minnesota, Wayzata, MN, 5University of Vermont College of Medicine, Burlington, VT
First Author:
Co-Author(s):
Introduction:
Susceptibility Distortion Correction (SDC) in EPI data is complex, notably when pooling multiple datasets for mega-analyses. Datasets that were collected long ago may not have acquired fieldmaps in order to address B0 magnetic inhomogeneities. The current study examined the impact of using a fieldmapless approach for SDC on temporal Signal-to-Noise ratio (tSNR) and overall voxel coverage in Cue Reactivity tb-fMRI data.
Methods:
Two ENIGMA Addiction datasets with Cue Reactivity tb-fMRI (block design) were included in the current study. Dataset 1 was acquired on a GE Discovery scanner (89 subjects) and dataset 2 was acquired on a Siemens PRISMA fit scanner (28 subjects). Each dataset was preprocessed twice using fmriprep-23.2.0a1: Once without SDC and once using the fieldmapless approach [--use-syn-sdc flag]. For each method and for each dataset, a group mask was calculated using fslmaths -Tmin. Consistent with the MRIQC package, tSNR was calculated as average bold signal across time divided by its standard deviation across time. tSNR was calculated for each subjects' timeseries, as well as for each of the block conditions (i.e. alcohol cue and neutral cue).
Results:
fMRIPrep's fieldmapless approach yielded more voxels in group masks [dataset 1: t=7.280, pFDR<0.001, hedges' g=0.622; dataset 2: t=8.169, pFDR<0.001, hedges' g=0.926] and higher tSNR [dataset 1: t=7.280, pFDR<0.001, hedges' g=0.622; dataset 2: t=6.687, pFDR<0.001, hedges' g=0.087] compared to not performing any SDC. Higher tSNR was also found in both datasets for the alcohol cue [dataset 1: t=10.718, pFDR<0.001, hedges' g=0.063; dataset 2: t=6.106, pFDR<0.001, hedges' g=0.079] and neutral cue [dataset 1: t=10.833, pFDR<0.001, hedges' g=0.063; dataset 2: t=6.228, pFDR<0.001, hedges' g=0.094] conditions within their respective block designs. The number of voxels in a given group mask was not correlated with mean framewise displacement (FD) or tSNR, though mean FD was highly correlated with tSNR in both datasets [dataset 1: r = -0.688; dataset 2: r = -0.639].
Conclusions:
Fieldmap options in preprocessing fMRI data are numerous and complex. The current study examined whether or not using a fieldmapless approach in data preprocessed with fMRIPrep would yield more voxels in the converged, group mask and whether it would improve tSNR compared to performing no SDC. In two separate datasets that were acquired on two separate scanners, using a fieldmapless approach to SDC outperformed doing no SDC on the aforementioned metrics. Future directions for this work include directly comparing a fieldmapless approach with acquired fieldmaps in a given dataset. Additionally, comparing a fieldmapless approach with various fieldmap types (i.e. PEpolar, phase difference and magnitude images, etc.) across different scanning platforms.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Motion Correction and Preprocessing 1
Other Methods 2
Keywords:
Addictions
Data analysis
Data Registration
Design and Analysis
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
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