Ethics approval and consent to participate
All study methods were carried out in accordance with the relevant guidelines and regulations according to the Declaration of Helsinki.
Subjects were ten healthy adults (5 males and 5 females) with an age range of 23 – 48 years old. All subjects were recruited in accordance with the Institutional Review Boards at the University of Pittsburgh and Massachusetts General Hospital. Written informed consent was obtained from all subjects prior to scanning at each location. For each subject the scans at both sites were conducted at approximately the same time of day. Subjects were advised to maintain the same caffeine intake on scan days and same sleep schedule the nights before. They were advised not to exercise on the day prior to scanning and on the day of scanning before the scan.
All images were collected on 3 T MAGNETOM Skyra MRI scanners (Siemens, Erlagen, Germany) with 70 cm Open Bore. Scanners were located at UPMC Children’s Hospital of Pittsburgh (PIT) and the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital/Harvard-MIT Division of Health Sciences & Technology (MGH). Five of the subjects were scanned at PIT first, and five were scanned at MGH first. The mean time between scans was 109 days, the median was 83, and the range was 2 to 218.
A 32-channel head coil equipped with a rear-facing mirror was used for imaging at both sites. Task instructions were projected onto a screen at the rear of the bore. Task instructions were displayed using E-Prime 2.0 (Psychology Software Tools, Inc., Sharpsburg, PA). A blank screen was projected during resting-state and structural scans.
Scans were collected in the following order for each subject: (1) resting-state BOLD, (2) resting-state pCASL, (3) finger-tapping pCASL, (4) MPRAGE structural scan, (5) finger-tapping BOLD, (6) resting-state pCASL (repeat of #2), (7) finger-tapping pCASL (repeat of #3). For resting-state and structural scans, subjects were instructed to relax and keep their eyes open. For the finger-tapping task, subjects were instructed to hold their right hand against their chest and tap the thumb against the other 4 fingers in a 2–3-4–5-4–3-2 sequence at a rate of approximately 2 Hz. Tapping occurred in 20-s blocks cued by E-Prime display with pseudo-random ISIs of 15, 20, 25, or 30 s. A total of 10 tapping blocks occurred during each scan.
Two dimensional BOLD images were collected using an EPI sequence (field of view (FOV) of 196 mm × 196 mm, voxel size of 2.0 × 2.0 mm2 with 32 slices of 4.0 mm thickness in interleaved fashion with no slice gap) using a repetition time (TR) of 2.5 s, an echo time (TE) of 33 ms, and a flip angle of 80°. A total of 120 volumes were collected in each resting-state BOLD scan. Finger-tapping BOLD scans were 175 volumes at MGH and 190 volumes at PIT, although only the first 175 volumes were analyzed at each location. High-resolution structural images were collected for each participant using a T1-weighted scanning technique (3D MPRAGE sequence, TR/TE/Flip = 1.35 s/2.54 ms/9°; field of view = 256 mm × 256 mm; voxels size = 1.0 × 1.0 × 1.0 mm3; 144 slices per slab).
pCASL images were collected using an FOV of 256 × 256 mm2 and matrix of 64 × 64, yielding 4.0 × 4.0 mm2 voxels. 25 slices of 5 mm thickness were collected with no gap in an interleaved fashion with a TR of 3.8 s, a TE of 15 s, and a flip angle of 90°. The labeling duration was 1.48 s and the post-labeling delay was 1.2 s. A total of 92 volumes were collected in each resting-state pCASL scan, and 114 volumes were collected in each finger-tapping pCASL scan. This specific pCASL sequence has been used in previous fMRI studies [18,19,20].
All DICOM images were anonymized using custom Matlab (The MathWorks, Inc., v. 2018a) scripts and converted to NIFTI format for processing and analysis. Structural image origins were set to the anterior commissure using SPM 12 (http://www.fil.ion.ucl.ac.uk/spm/). Brain extraction was performed in SPM by segmenting the structural image for each subject and creating a brain mask by adding the segmented grey matter, white matter, and cerebrospinal fluid (CSF) images together with a threshold of 0.01. The brain mask was then applied to the functional BOLD and pCASL images to extract the brain.
Finger-tapping fMRI analysis
BOLD and pCASL signal activation was calculated using FEAT analysis in FSL Version 5.0 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). 4D Brain-extracted images were preprocessed in FSL with MCFLIRT motion correction and spatial smoothing of 5 mm FWHM for BOLD images and 8 mm for pCASL images. Images were registered to the standard space MNI52 T1 2 mm brain atlas (built into FSL) using full 12 degrees of freedom affine transformation (translation, rotation, zoom, and sheer).
BOLD signal activation was modeled using the tapping task as the explanatory variable (EV), convolved with the default hemodynamic response function in FSL, a canonical double gamma function. Z-statistic BOLD images were rendered using a Z-threshold of > 2.3 and a corrected cluster significance threshold of p = 0.05. Site specific group maps of the BOLD images were calculated using FSL FEAT higher-level analysis with FLAME 1 mixed effects.
pCASL signal activation was modeled with 3 EVs: (1) control – tag baseline, (2) pseudoBOLD activation using the tapping task, and (3) perfusion activation. Positive and negative contrasts and F-tests were calculated for each EV. Z-statistic pCASL images were rendered with an uncorrected threshold of p = 0.05 on account of the reduced temporal resolution. Individual subject mean pCASL images were calculated for each scanning location using FSL FEAT higher-level analysis. Site-specific mean pCASL images were calculated using FEAT higher-level analysis with fixed effects.
Motor cortex regions of interest (ROIs) for the specific areas activated by our finger tapping task were generated so that comparisons of BOLD and pCASL signal change could be performed. This was done using all of the finger-tapping results (both BOLD and pCASL, as described above) as first level analyses for a group mean calculation in FSL FEAT. A Z-threshold of 20 was used to limit our comparison to only motor areas strongly activated in all scans. The resulting mean cluster image was converted to a mask image using SPM12’s Image Calculator function with a threshold of 0. This activated motor cortex mask was applied to all BOLD and pCASL Z-statistic images at the individual subject level. Signal change was then computed for each individual relative to their whole brain mean signal. Pearson correlation analysis of these signal change values was performed in SPSS (IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.) on BOLD and pCASL signal change values using 95% confidence intervals.
Resting state functional connectivity analysis
The CONN Functional Connectivity Toolbox (version 17.f, https://www.nitrc.org/projects/conn/) in Matlab was used for all functional connectivity analyses . 4D BOLD images were put through the CONN default preprocessing pipeline where they were motion corrected, slice timing corrected, outlier scrubbed, segmented into white matter, gray matter, and CSF maps, normalized, and smoothed with an 8.0 mm Gaussian kernel. BOLD images were then denoised for white matter, CSF, and effect of rest. 4D label – control subtracted pCASL cerebral blood flow (CBF) images were created using the ASL toolbox  and preprocessing scripts provided by Chris Rorden (https://crnl.readthedocs.io/asl/index.html). CBF images were smoothed to 8.0 mm FWHM in the CONN toolbox and denoised for white matter and effect of rest.
ROI-to-ROI and Seed-to-Voxel weighted functional connectivity analyses were calculated for the preprocessed BOLD and CBF images using bivariate correlation and hemodynamic response function weighting. Using the atlas provided within Conn, the DMN subregions medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and left and right lateral parietal lobe (LLP and RLP, respectively) as well as the Anterior Cingulate Cortex (ACC) and right and left Anterior Insula (based on their involvement in pain scans and our interest in using these areas for future analysis) were used as seed regions. All functional connectivity images were thresholded at 0.25.
Intraclass correlation coefficients (ICC)  were calculated for the functional connectivity Z-scores for each seed region using custom Matlab scripts and the Matlab IPN toolbox developed by Xi-Nian Zuo [24, 25]. Negative ICC values are known to be difficult to interpret and were changed to zero . Multivariate repeated measures ANOVA was used to compare functional connectivity Z-scores for the MPFC and PCC seed regions in the DMN at all ROIs using scanning location as the within-subjects variable. Additionally, Dice Similarity Coefficients (DSC) [15, 27, 28] were calculated for 3D functional connectivity image matrices. The DSC quantifies the spatial overlap for two or more images, ranging from 0 (no spatial overlap) to 1 (indicating complete overlap). DSC’s were generated by the CONN toolbox comparing BOLD and pCASL resting state images collected at MGH with images collected at PIT.
To further analyze the DMN in the seed-based pCASL resting state scans, a second analysis was done which used ICA to determine the data-derived DMN locations, and the above analysis was then repeated. This is referred to as dual-regression fcMRI . To accomplish this, two separate ICA runs were performed; one for each pCASL set at each of the two sites. All 10 subjects’ data for the site were entered into CONN Toolbox. Again, white matter signal and the effect of rest were removed during denoising. To ensure that artifactual independent components were not identified (the DMN is robustly connected at rest and is typically easy to identify), 50 components were used. The component that appeared to best represent the DMN thorgh manual visual inspection was selected and thresholded at Z = 2. Each area of the DMN was then isolated from that component in individual masks and fed back into CONN for a ROI-to-ROI analysis using the ICA-defined PCC as the seed. The rest of the analysis mirrored that described for pCASL above.
Cerebral blood flow analysis
CBF maps were generated for each pCASL image set using the ASL toolbox and Rorden preprocessing scripts described above. Mean CBF maps were generated for each subject at each scanning location using SPM12. CBF maps were generated for the resting state scans and separately for the tapping and resting portions of the finger-tapping scans. CBF maps were generated in SPM12.
Global mean CBF was calculated from these mean CBF maps using custom Matlab scripts. CBF values for the motor cortex ROI were calculated by applying the motor cortex mask described above to each mean CBF image using FSLSTATS. Repeated measures ANOVA analyses were performed in SPSS to compare CBF values between the two scanning locations.
All data collected for this study, including physiologic signals where available, has been uploaded for public sharing on OpenNeuro (https://openneuro.org).