Blood oxygenation level dependent (BOLD) and arterial spin labeling (ASL) are two predominant resting-state fMRI techniques in mapping spontaneous brain activities. At single voxel level, cerebral blood flow (CBF) measured by ASL and amplitude of low frequency fluctuations (ALFF) of BOLD have been recognized as useful indices to characterize brain function in health and disease. However, no study has directly compared the test-retest reliability between BOLD and CBF contrasts on the same group of subjects at single voxel level. Moreover, both eyes-open and eyes-closed conditions have been employed as resting states, but it is still not clear which state is more reliable. Here we collected BOLD and ASL data during eyes-open and eyes-closed states across three scanning visits on twenty-two healthy young subjects. CBF-mean, BOLD- and CBF-ALFF were computed to characterize corresponding brain activities at single voxel level. Seed-based functional connectivity (FC) with the posterior cingulate cortex (PCC) was further calculated for both BOLD and CBF data. Intra-class correlation was used as the index of long-term reliability between visits 1 and 2 (two months apart) and short-term reliability between visits 2 and 3 (on the same day). Both short- and long-term reliabilities for CBF-mean and BOLD-ALFF were high, but were lower for CBF-ALFF, BOLD- and CBF-FC in both eyes-open and eyes-closed states. Direct comparisons showed that brain regions with the highest reliability of CBF-mean were mainly in the gray matter. The reliability of CBF-ALFF and BOLD-FC was lower than that of BOLD-ALFF, and the reliability of CBF-FC was lower than those of both CBF-ALFF and BOLD-FC. Furthermore, we observed that reliabilities of the eyes-open state were higher than those of the eyes-closed state for both imaging contrasts, though the effect size was small. Voxel-wise comparisons demonstrated that the long-term reliability of BOLD-ALFF was significantly higher with eyes open in the visual system, and both the short- and long-term reliability of BOLD-FC was slightly higher with eyes open in the default mode network. Moreover, we showed that denoising decreased the reliability of both ALFF and FC of both BOLD and ASL contrasts. In conclusion, our results indicated that CBF-mean and BOLD-ALFF could both be used as reliable indices for characterizing resting-state brain activities at single voxel level and recommended the eyes-open state for resting-state studies, especially for those targeting the visual system and default mode network.

Resting-state fMRI studies have increasingly focused on multi-contrast techniques, such as BOLD and ASL imaging. However, these techniques may reveal different aspects of brain activity (e.g., static vs. dynamic), and little is known about the similarity or disparity of these techniques in detecting resting-state brain activity. It is therefore important to assess the static and dynamic characteristics of these fMRI techniques to guide future applications. Here we acquired fMRI data while subjects were in eyes-closed (EC) and eyes-open (EO) states, using both ASL and BOLD techniques, at two research centers (NIDA and HNU). Static brain activity was calculated as voxel-wise mean cerebral blood flow (CBF) using ASL, i.e., CBF-mean, while dynamic activity was measured by the amplitude of low frequency fluctuations (ALFF) of BOLD, i.e., BOLD-ALFF, at both NIDA and HNU, and CBF, i.e., CBF-ALFF, at NIDA. We showed that mean CBF was lower under EC than EO in the primary visual cortex, while BOLD-ALFF was higher under EC in the primary somatosensory cortices extending to the primary auditory cortices and lower in the lateral occipital area. Interestingly, mean CBF and BOLD-ALFF results overlapped at the visual cortex to a very small degree. Importantly, these findings were largely replicated by the HNU dataset. State differences found by CBF-ALFF were located in the primary auditory cortices, which were generally a subset of BOLD-ALFF and showed no spatial overlap with CBF-mean. In conclusion, static brain activity measured by mean CBF and dynamic brain activity measured by BOLD- and CBF-ALFF may reflect different aspects of resting-state brain activity and a combination of ASL and BOLD may provide complementary information on the biophysical and physiological processes of the brain.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.