Activation in Finger-Tapping Scans
Both BOLD (Figure 1a) and pCASL scans (Figure 1b) showed strong activation in the motor cortex at both scanner locations. In the BOLD scans, signal change in the motor cortex showed significant Pearson correlations with the finger-tapping task at both the PIT (0.539, p < 0.001) and MGH (0.490, p < 0.001) scan locations (Figure 1c), and showed a strong correlation of 0.944, p < 0.001 between the two locations. In the pCASL scans, percent signal change in the motor cortex showed a significant Pearson correlation with the tapping task at PIT (0.375, p < 0.001) and at MGH (0.312, p < 0.001) (Figure 1d). Additionally, percent signal change was significantly correlated between the two locations for the pCASL scans (0.976, p < 0.001).
Resting State Functional Connectivity
Functional connectivity analyses with the PCC as the seed region identified elements of the DMN in both the BOLD (Figure 2a) and pCASL scans (Figure 2b) at both locations, although activation was clearer and more consistent between subjects and locations in the BOLD images, while the DMN is not clearly identified in the PCC seed-based pCASL images. Nonetheless, high intraclass correlations between the PIT and MGH locations were found for DMN connectivity in both types of scans. In the BOLD scans (Figure 2c), high intraclass correlations were found for PCC to RLP (0.726), ACC (0.876), and left insula (0.612), for the LLP to RLP (0.617), ACC (0.608), and left insula (0.689), and for the RLP to ACC pathways (0.606). In the pCASL scans (Figure 2d), high intraclass correlations were found for the PCC to LLP (0.638) and right insula (0.701).
Multivariate repeated measures ANOVA showed that functional connectivity Z-scores for both BOLD scans did not have significant variability for location, but did have significant variability between subjects (Figure 2c). In other words, variability was greater between subjects than between locations. For BOLD resting state functional connectivity Z-scores, within-subjects variability was not significantly different for both the MPFC seed region, F(4, 6) = 2.431, p = 0.205, and the PCC seed region, F(4, 6) = 5.546, p = 0.06. For BOLD scans, between-subjects variability was significantly different for both the MPFC seed region, F(4, 6) = 62.333, p = 0.001, and for the PCC seed region, F(4, 6) = 54.616, p = 0.001.
Similar to the BOLD scans, the pCASL resting state scans did not have significant within-subjects variability for either the MPFC seed region, F(2, 6) = 3.611, p = .233, or the PCC seed region, F(2,6) = 1.545, p = 0.443 (Figure 2d). Between-subjects variability for pCASL scans was not significant for the MPFC seed region, F(2, 6) = 2.786, p = 0.288, but was significant for the PCC seed region, F(2, 6) = 97.824, p = 0.01.
Dice Similarity Coefficients were higher for BOLD resting state scans (Figure 2c) than for pCASL resting state scans (Figure 2d). For the MPFC, PCC, LLP and RLP seed region, the DSC was 0.659, 0.672, 0.667, and 0.664 respectively for the BOLD scans, while the pCASL scans had DSC values of 0.603, 0.617, 0.473 and 0.451 in these regions. Generally, ICC and DSC coefficients greater than or equal to 0.6 are considered to be at least “good” correlations.
Because of the worse than expected appearance of 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 the Matlab-based CONN Toolbox (https://www.nitrc.org/projects/conn/). White matter signal and the effect of rest were removed during denoising. The component that appeared to best represent the DMN was selected and thresholded at Z = 2. Each area of the DMN was identified and isolated in a mask 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.
The results of the ICA-based analysis are displayed in Figure 3a. The DMN in each subject’s map is much more clearly defined when compared to the seed-based pCASL resting state connectivity maps in Figure 2. The group map for each location shows a much cleaner picture of DMN activity, and the two locations resemble each other more closely. As with the seed-based pCASL analysis, the pCASL resting state scans did not have significant within-subjects variability for either the MPFC seed region, F(4, 6) = 1.944, p = .271, or the PCC seed region, F(4,6) = 3.878, p = 0.105. Between-subjects variability for pCASL scans was significant both for the MPFC seed region, F(4, 6) = 32.302, p = 0.002 and for the PCC seed region, F(4, 6) = 34.730, p = 0.002.
Although ICA-based analysis produced cleaner images of DMN functional connectivity, the correlation coefficients were similar or slightly worse compared to ICC and DSC values for seed-based analysis (Figure 3b). ICC values for ICA-based pCASL images were only above the threshold of 0.6 for two pathways: PCC – LLP, with an ICC = 0.789, and PCC – RLP, ICC = 0.669. No DSC values were above the 0.6 threshold for ICA-based pCASL images.
Cerebral Blood Flow
Global CBF was very similar between the two scanning locations (Figure 4a), and repeated measures ANOVAs showed no significant differences between the two scanning locations. Mean (SD) global CBF during resting state scans was 34.26 (5.56) at MGH and 34.45 (5.83) at PIT, F(1, 9) = 0.009, p = 0.925. Mean global CBF during the resting portion of the finger-tapping scans was 33.27 (4.55) at MGH and 32.78 (5.32) at PIT, F(1, 9) = 0.087, p = 0.775. Mean global CBF during the tapping portion of the finger-tapping scans was 33.12 (5.47) at MGH and 33.37 (5.00) at PIT, F(1, 9) = 0.027, p = 0.874.
Regional flow in the motor cortex was higher during the finger-tapping scans compared to rest once the hemodynamic delay was taken into account (Figure 4b). Differences were not found between the MGH and PIT scanning sites (F(1,9) = 0.21, p = 0.89), but motor cortex CBF did, as expected, vary between tapping and resting tasks (F(1,9) = 41.77, p < 0.001). Mean motor cortex flow during the resting portion of the finger-tapping scans was 43.56 (6.22) at MGH and 41.42 (7.57) at PIT, F(1, 9) = 2.704, p = .134. Mean flow during the tapping portion of the scans was 46.64 (7.00) at MGH and 48.16 (7.64) at PIT, F(1, 9) = 3.112, p = 0.112.