On the sensitivity of ASL MRI in detecting regional differences in cerebral blood flow

Abstract

Arterial-spin-labeling (ASL) magnetic resonance imaging (MRI) provides a noninvasive tool to measure cerebral blood flow (CBF) and is increasingly used as a surrogate for baseline neural activity. However, the power of ASL MRI in detecting CBF differences between patient and control subjects is hampered by inter-subject variations in global CBF, which are associated with non-neural factors and may contribute to the noise in the across-group comparison. Here, we investigated the sensitivity of this technique and proposed a normalization strategy to better detect such a difference. A "model" situation was employed in which two visual stimuli (i.e. cross fixation and flashing checkerboard) were presented to two groups of subjects to mimic "control" and "patient" groups (N=7 for each group), respectively. It was found that absolute CBF (aCBF) in the occipital lobe in the checkerboard group was 26.0% greater compared to the fixation group, but the level of significance was modest (P=.03). In contrast, when normalizing the CBF with whole-brain CBF or CBF in a reference region [termed relative CBF (rCBF)], the statistical significance was improved considerably (P<.003). For voxel-based analysis, the rCBF indices correctly detected CBF differences in the occipital lobe in the across-group comparison, while aCBF failed to detect any significant cluster using the same statistical threshold. We also performed Monte Carlo simulation to confirm the experimental findings and found that the power improvement was most pronounced when signal-to-noise-ratio is moderate and the underlying CBF difference was small. The simulation also showed that, with the proposed normalization, a detection power of 80% can be achieved using a sample size of about 20. In summary, rCBF is a more sensitive index to detect small differences in CBF, rather than the much-sought-after aCBF, since it reduces data noise caused by inter-subject variations in global CBF.

Fig. 1

Averaged aCBF maps from two groups of subjects who viewed a) flashing checkerboard and b) fixation cross, respectively. Their difference is shown in c). The aCBF maps from individual subjects were spatially normalized to the MNI brain template and then averaged. CBF increases can be seen in the occipital lobe.

Fig. 2

Results of voxel based comparison across groups using a) CBF normalized by whole brain blood flow (rCBF_WB); and b) CBF normalized by central region blood flow (rCBF_CR). The analyses were performed using an unpaired Student t-test with a threshold of p<0.05 (False Discovery Rate corrected) and cluster size of 100 voxels. The results of aCBF comparison were not shown because no significant cluster was detected.

Fig. 3

Histograms of t scores from individual voxels. The voxels were delineated from the intra-subject comparison of aCBF to avoid bias in selection criteria. A total of 1375 voxels were included. The histograms shown are from inter-subject comparisons using aCBF, rCBF_WB, and rCBF_CR. Since the voxels were from the visual cortex, their t scores tend to be positive. It can be seen that the histograms of rCBF_WB and rCBF_CR were shifted to the right.

Fig. 4

Results of voxel based comparison within the same group but under different conditions using a) absolute CBF (aCBF); b) CBF normalized by whole brain blood flow (rCBF_WB); and c) CBF normalized by central region blood flow (rCBF_CR). The analyses were performed using a paired Student t-test with a threshold of p<0.05 (False Discovery Rate corrected) and cluster size of 100 voxels.

Fig. 5

Scatter plots of regional aCBF values across subjects. All comparisons had a significant correlation (p<0.005).

Fig. 6

Results of Monte Carlo simulation for across-group comparison. Top row: Detection power as a function of SNR and CBF difference between the control and patient groups. The simulation was conducted for a) aCBF and b) rCBF. Their difference is shown in c). Bottom row: Detection power as a function of sample size and statistical threshold. Relative CBF offers a greater power in detection of group differences. The simulation was conducted for d) aCBF and e) rCBF. Their difference is shown in f).