We evaluated periventricular white matter (PVWM) cerebral blood flow (CBF) as a mechanistically specific biomarker for small vessel ischemia and demonstrated the feasibility of its measurement using state-of-the-art arterial spin labeling. We constructed the PVWM region of interest and demonstrated that mean CBF in PVWM had higher correlation with lesion volumes than global, grey matter, or white matter CBF, even after correction for global CBF, age, and sex. PVWM CBF also showed higher correlation with Trail A and B processing speed than CBF in other regions, or lesion volumes.
A highly sensitive group-averaged CBF map (figure 1(a)) was derived from 436 healthy middle-aged subjects (age=50.4±3.5 years, 54% female) enrolled in the NHLBI CARDIA study4. The CBF map was derived from 2D pseudo-continuous ASL (PCASL) data (labeling=1.48s, post-labeling-delay (PLD)=1.5s, voxel=3.4x3.4x5mm3) and processed with advanced signal processing strategies.5,6 Subsequently, the PVWM region of interest (ROI) for ischemic risk was constructed by thresholding the group-averaged CBF map to CBF<15 ml/100g/min (figure 1(b)), a threshold shown to predict future occurrence of lesions with certainty7. This region shows the lowest CBF in the brain and is most vulnerable to develop lesions in older age.
State-of-the-art ASL MRI8 (acquired with unbalanced PCASL and 90% background-suppressed 4-shot 1D-accelerated 3D spiral imaging, resolution=2.5 mm3 isotropic, labeling=1.8s, PLD=1.8s, TR=4.25s, TEeff=9.78ms) (representative images in Figure 2) and Fluid Attenuated Inversion Recovery (FLAIR) MRI (TR/TE = 6000/382ms, resolution=0.8 mm3 isotropic, rate=2 GRAPPA acceleration) were obtained from 59 elderly, cognitively normal and amyloid negative subjects (age=73.3±6.9, 63% female) recruited from the Penn Alzheimer’s Disease Center clinical core. Automated lesion segmentation from FLAIR data was obtained using lesion segmentation toolbox9 to derive PVWM, DWM and total lesion volume measurements. Mean CBF in 1) global, 2) grey matter (GM), 3) white matter (WM) and 4) PVWM ROI (constructed as detailed above) were correlated with the lesion volumes after normalizing by total WM volume. The ROI CBFs and the lesion volumes were also correlated with neuropsychological Trail Making Test A and B time performances, which are thought to be sensitive to degeneration in WM pathways.
Figure 3 shows correlations between the various ROI CBFs and lesion volumes. GM CBF correlated with PVWM and total lesion volume (although not with DWM lesion volume) whereas global CBF showed a trend. No significant correlation was observed for WM CBF. PVWM-CBF showed the strongest correlations with PVWM, DWM and total lesion volumes, maintained even after normalized by global CBF. PVWM-CBF outside of lesions also correlated or trended with lesion volumes, which demonstrates that PVWM-CBF values are not driven by lesions only. In each case, the correlation weakened after adjusting for age and sex.
Figure 4 shows correlations of time taken for completion of trail A and B tests with CBF measures and lesion volumes at different ROIs. Although the correlations did not reach statistical significance, PVWM CBF showed a higher degree of correlation, and p values tending towards significance.
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