Iris Asllani

Cerebral blood flow (CBF) is a well-established correlate of brain function and therefore an essential parameter for studying the brain at both normal and diseased states. Arterial spin labeling (ASL) is a noninvasive fMRI technique that uses arterial water as an endogenous tracer to measure CBF. ASL provides reliable absolute quantification of CBF with higher spatial and temporal resolution than other techniques. And yet, the routine application of ASL has been somewhat limited. In this review, we start by highlighting theoretical complexities and technical challenges of ASL fMRI for basic and clinical research. While underscoring the main advantages of ASL versus other techniques such as BOLD, we also expound on inherent challenges and confounds in ASL perfusion imaging. In closing, we expound on several exciting developments in the field that we believe will make ASL reach its full potential in neuroscience research.

By comparing hemodynamic signals acquired immediately before and during activation, functional magnetic resonance imaging (fMRI) is well suited for mapping acute changes in brain function. However, it remains unclear whether fMRI can map functional changes over longer periods. Here, we address this issue by empirically testing the feasibility of arterial spin labeling (ASL) fMRI to detect changes in cerebral blood flow (CBF) with baseline and task separated by 1 month. To increase the sensitivity of the method, we applied an algorithm that yielded flow density (CBFd) images that were independent of tissue content. To increase the accuracy, we developed a technique that generated arterial transit time at each voxel, independently. Results showed that activation changes in CBFd during the same session were statistically the same as across 30 days. The activation CBFd on day-30 was 34% (motor) and 25% (visual) higher than the respective baselines of 83 and 107 mL/100 g/min obtained on day-1. Furthermore, the signal-to-noise ratio of the CBFd measurement was 2.1 and 2.9 times higher than that of the conventional CBF for within-subject and across-subjects comparisons, respectively (n=9 healthy young subjects). Taken together, these results indicate that CBFd measure could be better suited than net CBF to map long-term changes in brain function.

The accuracy of cerebral blood flow (CBF) imaging in humans has been impeded by the partial volume effects (PVE), which are a consequence of the limited spatial resolution. Because of brain atrophy, PVE can be particularly problematic in imaging the elderly and can considerably overestimate the CBF difference with the young. The primary goal of this study was to separate the structural decline from the true CBF reduction in elderly. To this end, a PVE‐correction algorithm was applied on the CBF images acquired with spin‐echo EPI continuous arterial spin labeling MRI (voxel size = 3.4 × 3.4 × 8 mm³). Tissue‐specific CBF images that were independent of voxels' tissue fractional volume were obtained in elderly (N = 30) and young (N = 26); mean age difference was 43 years. Globally, PVE‐corrected gray matter CBF was 88.2 ± 16.1 and 107.3 ± 17.5 mL/100 g min⁻¹ in elderly and young, respectively. The largest PVE contribution was found in the frontal lobe and accounted for an additional 10% and 12% increase in the age‐related CBF difference between men and women, respectively. The GM‐to‐WM CBF ratios were found to be on average 3.5 in elderly and 3.9 in young. Whole brain voxelwise comparisons showed marked CBF decrease in anterior cingulate (bilateral), caudate (bilateral), cingulate gyrus (bilateral), cuneus (left), inferior frontal gyrus (left), insula (left), middle frontal gyrus (left), precuneus (bilateral), prefrontal cortex (bilateral), and superior frontal gyrus (bilateral) in men and amygdala (bilateral), hypothalamus (left), hippocampus (bilateral), and middle frontal gyrus (right) in women. Hum Brain Mapp 2009.

Partial volume effects (PVE) are a consequence of limited spatial resolution in brain imaging. In arterial spin labeling (ASL) MRI, the problem is exacerbated by the nonlinear dependency of the ASL signal on magnetization contributions from each tissue within an imaged voxel. We have developed an algorithm that corrects for PVE in ASL imaging. The algorithm is based on a model that represents the voxel intensity as a weighted sum of pure tissue contribution, where the weighting coefficients are the tissue's fractional volume in the voxel. Using this algorithm, we were able to estimate cerebral blood flow (CBF) for gray matter (GM) and white matter (WM) independently. The average voxelwise ratio of GM to WM CBF was ∼3.2, in good agreement with reports in the literature. As proof of concept, data from PVE‐corrected method were compared with those from the conventional, PVE‐uncorrected method. As hypothesized, the two yielded similar CBF values for voxels containing >95% GM and differed in proportion with the voxels' heterogeneity. More importantly, the GM CBF assessed with the PVE‐corrected method was independent of the voxels' heterogeneity, implying that estimation of flow was unaffected by PVE. An example of application of this algorithm in motor‐activation data is also given. Magn Reson Med, 2008.

Continuous arterial spin labeling (CASL) magnetic resonance imaging (MRI) was combined with multivariate analysis for detection of an Alzheimer's disease (AD)-related cerebral blood flow (CBF) covariance pattern. Whole-brain resting CBF maps were obtained using spin echo, echo planar imaging (SE-EPI) CASL in patients with mild AD ( n=12, age=70.7±8.7 years, 7 males, modified Mini-Mental State Examination (mMMS)=38.7/57±11.1) and age-matched healthy controls (HC) ( n=20; age=72.1±6.5 years, 8 males). A covariance pattern for which the mean expression was significantly higher ( Pcorrected=0.05). The area under the ROC curve for discriminating AD versus HC was 0.97 and 0.94 for covariance pattern and gray matter ROI, respectively. Fewer areas of depressed CBF in AD were detected using voxelwise analysis (corrected, P<0.05). These areas were superior temporal, cingulate, middle temporal, fusiform gyri, as well as inferior parietal lobule and precuneus. When tested on extensive split-half analysis to map out the replicability of both multivariate and univariate approaches, the expression of the pattern from multivariate analysis was superior to that of the univariate.