Regional CBF obtained with ASL MRI provides a means to assess the links between resting BOLD imaging-derived SBA measures and underlying brain metabolism. This study provides the first evidence that the resting BOLD imaging-based SBA measures are related to regional CBF.
Group level CBF, FC, ReHo, and ALFF patterns were first assessed for each of the 2 scan sessions to check the similarity of the distribution patterns of CBF and each of the 3 SBA measures (FC, ReHo, and ALFF). Consistent with the findings reported in the literature 
, a set of reliable high CBF (higher than whole brain mean) regions was found in both sessions, consisting of mOFC, lOFC, PFC, putamen, insula, temporal cortex, ACC, PCC, VC, precuneus, and bilateral PC. The significant high ReHo regions overlapped with the high CBF regions in mOFC, PFC, ACC, putamen, PCC, precuneus, and bilateral PC, suggesting that the relatively high coherent resting brain activity in those regions is associated with increased resting brain metabolism. A set of high ALFF (higher than whole brain average) regions was identified in mOFC, insula, iTC, putamen, VC, PCC, precuneus, VC, and bilateral PC, which also overlaps with the high CBF and high ReHo regions described above. This overlap suggests that increased brain metabolism might be required to support regionally coherent and slowly fluctuating resting brain activity. WM showed lower than average CBF/ReHo/ALFF as expected, and WM CBF is known to be lower than GM CBF 
. Low WM ReHo and WM ALFF suggest that resting brain activity in WM is sporadic across voxels and random across time, which may require relatively lower energy support than that for GM, as reflected by the relatively lower CBF. The overlap between the high CBF regions and PCC-FC and dACC-FC network regions suggest a potential CBF modulation for each of the FC in the overlapped regions.
Repeatable regional PCC-FC vs regional CBF correlations were observed in bilateral PC, precuneus, VC, DLPFC, and mOFC, indicating a direct CBF modulation to the resting PCC-FC within the DMN (bilateral PA and precuneus), visual system, and the executive cognitive network (DLPFC and OFC). Similar to what was reported in 
, dACC-FC and vACC-FC (to save space, the latter was not displayed in Results
) showed different networks. Only dACC-FC demonstrated reliable (across two time points) correlations to regional CBF which were located in bilateral PFC, left inferior and superior temporal cortex. Reproducible vACC-FC vs CBF correlations were identified in bilateral PFC, PCC, and superior temporal cortex when the significance level was reduced to p<0.05. This difference of regional ACC-FC vs regional CBF association might be induced by the small sample size involved in this study as well as the use of CASL, which is noisier than current methods 
. No correlations were found between global CBF and global or regional FC, indicating no linear modulations of global CBF to FC. Since CBF modulations on regional FC were only observed in certain regions, the region constrained CBF vs FC correlations are likely suppressed when considering the whole brain average, which might explain why there were no correlations between global CBF and FC.
Stable correlations between regional CBF and SBA coherence (ReHo) or the low frequency fluctuation magnitudes (ALFF) were demonstrated in most of the brain cortex. The ReHo/CBF ratio and ALFF/CBF ratio-based analyses also suggest that a spatially uniform and stable linear relation between regional CBF and the two local SBA measures exists in GM, except for VC and precuneus. Global CBF showed no significant correlations to both regional ReHo, suggesting that regional SBA is not linearly modulated by the overall brain energy supply. A trend of correlation was found between the global CBF and global ReHo or global ALFF, which can be understood from the massive regional CBF vs ReHo or ALFF correlation as the correlation of global CBF vs global ReHo or ALFF can be approximated to certain extent by a summation of the regional CBF vs regional ReHo or ALFF. As compared to the regional FC vs regional CBF correlation, regional ReHo and ALFF showed spatially more distributed correlations to regional CBF. FC is derived from the correlation of any brain voxel's time series to that of the seed region, and might be modulated by CBF of the current voxel as well as that of the seed. Consequently, regional CBF might contribute only certain part of the FC variations across subject, which explains why regional FC vs regional CBF showed correlations in fewer brain regions than regional ReHo and ALFF vs regional CBF.
The value of these SBA vs CBF associations is twofold. First, it helps to link the apparent fMRI-derived parameters to a physiological meaningful measure. Though resting BOLD fMRI is assumed to be able to capture resting brain activity 
, these fMRI-derived parameters using either SRFC, ReHo, ALFF or even ICA cannot directly refer any physiological meanings. As baseline CBF reflects the baseline brain energy demand, relating those measures to CBF provides a way to appreciate their physiological underpinnings in brain metabolism. Second, it provides a way to normalize these purely data-dependent SBA metrics by using resting regional CBF, or alternatively the variations due to regional CBF can be removed in order to improve across subject comparisons, which might be very useful for clinical treatment or medicine study since both treatment and medicine can change regional CBF. Various brain networks might also be ranked or quantified using their CBF values. For example, in an additional analysis, we identified two resting brain networks, default mode network (DMN) and executive control network (ECN), using ICA on the BOLD data. We found that DMN regions showed higher mean CBF than ECN regions consistently at both time points ().
Default Mode Network (DMN) and executive control network (ECN) presenting significant (p<0.0004) different CBF at both sessions.
It is worth to note that the significant FC vs CBF correlation clusters appeared to be small, which raises a concern of noise interference. To reduce noise confounds, we used several preprocessing strategies including filtering, WM/CSF and global signal regression. More importantly, we used test-retest data from the same cohort of subjects with careful screening for any possible factors that may affect blood flow or even resting brain activity. As FC measures the correlations between two spatially distinct regions, it might be modulated by CBF from both regions.
Two limitations exist in this CBF-SBA association study. First, the data sample size is moderate and an uncorrected significance level was used for thresholding the results. Second, the retest data were acquired 2 months later. The small sample size might explain why we did not find any significant (even an uncorrected threshold was used) correlation between vACC FC and regional CBF and why we did not find any correlation between SBA and the global CBF if there were. The multiple comparison issue applies to the FC vs CBF correlation analysis but not to the ReHo vs CBF or ALFF vs CBF analyses since the suprathreshold clusters of the correlation of ReHo and ALFF to regional CBF survived the false detection rate (FDR)-based multiple comparison correction (q<0.05) 
. For FC vs CBF correlation analysis, although 15 subjects might not have enough power to reveal all possible SBA-CBF correlations, our findings were based on within-subject test-retest data, which partly compromised the sample size issue since these SBA-CBF associations repeated in 15 subjects should be reproducible when more subjects are recruited. A larger sample size and a more stringent thresholding criterion will be required in future work to confirm the findings reported here.
The test-retest data used in this paper were acquired in a project designed to test the stability of SBA measures and resting CBF with a long time interval (results were reported in a separate paper). Although a 2 month gap could conceivably introduce physiological or psychological variations to the data, we still found repeated SBA-CBF associations. One reason could be that these associations are stable over this duration in healthy subjects. Another explanation could be that the physiological and psychological variations affect SBA and CBF in the same way so their effects on the SBA-CBF correlation are canceled. Although we have screened for caffeine use and neurological disorders, several other factors, including breathing pattern, consumption of alcohol or caffeine, blood pressure, and activities of the autonomic nerve systems could alter blood flow. Since resting BOLD signals are modulated by CBF 
, CBF fluctuations will likely be propagated into the resting BOLD-based SBA measures, resulting in apparent SBA-CBF correlations. One way to reduce these artificial SBA-CBF correlations is to use relative CBF maps instead of absolute CBF maps in the correlation analysis. Using the relative CBF maps (obtained by dividing absolute CBF map by the whole brain average absolute CBF) we found very similar correlation results (data not shown), which suggests that the demonstrated SBA-CBF correlations are not affected by those physiological variations and supports the findings of no-significant correlations of regional SBA to global CBF.
Various additional network-related measures 
have also been used to assess SBA. Limited by the scope of this paper, we did not include them, though future work will cover the relations between more resting BOLD-based SBA measures like functional brain network properties 
. Resting ASL MRI has been demonstrated to be capable for assessing SBA 
. But we did not include the ASL MRI-based SBA measures due to the limit of space.
In summary, we investigated the correlations between different SBA measures with CBF and their test-retest reproducibility. In our analysis, reliable correlations were demonstrated between CBF and PCC FC as well as dACC FC in many of the significant FC regions, respectively. Reliable correlations to regional CBF were found in ReHo and ALFF in most of gray matter area. No correlations were found between global CBF and these SBA measures. These results demonstrate that dynamic SBA measures based on BOLD fMRI are related to the static baseline CBF, which confirms a link between these apparent BOLD fMRI-based metrics and underlying SBA.