Cerebral blood volume in Alzheimer's disease and correlation with tissue structural integrity
Introduction
Alzheimer's disease (AD) is neurodegenerative disease associated with neuritic plaques composed of beta amyloid and neurofibrillary tangles composed of hyperphosphorylated tau protein. While amyloid/tau pathology is the primary focus in the field, recent evidence indicates that vascular factors are important in the pathogenesis of AD (de la Torre, 2004). This evidence comes from a wide spectrum of studies, including postmortem studies showing severe cerebral angiopathy in AD patients (Chui et al., 2006, Jagust et al., 2008, Tian et al., 2006), epidemiologic studies showing that many of the risk factors for AD are also associated with vascular disease (hypertension, hypercholesterolemia, diabetes, and hyperhomocysteinemia) (reviewed in de la Torre (2002)), and neuroimaging studies showing that AD patients have greater volume of white matter hyperintensities of possible ischemic origin (Delano-Wood et al., 2008, Prins et al., 2004).
Brain vascular function can be assessed by several different methods. For in vivo studies, cerebral blood flow (CBF), the amount of blood reaching the tissue per unit time (Kety and Schmidt, 1948), is the most widely used parameter. Previous CBF studies in AD patients have found pronounced blood flow deficits in temporoparietal cortex, posterior cingulate cortex, and in some cases, frontal cortex (Alsop et al., 2000, Bartenstein et al., 1997, Ishii et al., 1997, Johnson et al., 2005, Kogure et al., 2000). CBF is known to be coupled to metabolic demand. Thus, although reduced CBF may be an indication of vascular dysfunction, it may also be simply due to lower metabolic demand in these regions (de Leon et al., 2001, Reiman et al., 2005, Small et al., 2000) in the face of relatively intact brain vasculature. In addition, CBF is also governed by many factors external to the brain, such as cardiac output, autonomic activity, and blood pressure. Therefore, it is necessary to study brain vasculature in AD with alternative vascular parameters.
Cerebral Blood Volume (CBV), the amount of blood per 100 ml of brain parenchyma, is an indicator of blood vessel lumen size and density. CBV has been less extensively studied than CBF, but was recently shown to be useful in assessing neovascularization in brain tumors (Law et al., 2004) and a good marker for angiogenesis and synaptogenesis (Pereira et al., 2007, Swain et al., 2003). In addition, the sensitivity of CBV to physiologic variation is about 38% of that of CBF (Grubb et al., 1974). Thus, CBV may be less dependent on the subject's depth and rate of respiration. We have recently developed a Vascular-Space-Occupancy (VASO) Magnetic Resonance Imaging (MRI) technique to quantitate CBV (Lu et al., 2005). In contrast with the Dynamic Susceptibility Contrast (DSC) MRI method, the VASO approach is based on steady-state signal and does not depend on arterial input function, which itself may be changed with disease. CBV measured by VASO shows a moderate correlation with that using DSC MRI, but also displays some deviations (Lu et al., 2005), suggesting that the two measures may have slightly different physiologic bases. The VASO technique also has the advantage that the sensitivity is sufficient to assess vascular health in white matter, which is ordinarily very difficult to assess due to very low vascularity.
In this study, we measured CBV in a group of patients with probable AD or Mild Cognitive Impairment (MCI), and identified regions with significant CBV decline compared to elderly non-demented controls. The relationship of CBV deficits to parenchymal damage was examined by correlating the regional CBV values to tissue water diffusion index as measured by diffusion tensor imaging (DTI), and the volume of white matter hyperintensities measured by FLAIR MRI. In addition, the CBV result was compared to neuropsychological test scores.
Section snippets
Participants
A group of probable AD/MCI patients (n = 16, 9M, 7F, age (years ± S.D.) = 70.7 ± 9.3) and a group of elderly controls (n = 10, 3M, 7F, age = 73.1 ± 4.4) were examined. The participants were recruited from the longitudinal cohorts maintained by the Alzheimer's Disease Center of University of Texas Southwestern Medical Center. The Health Insurance Portability and Accountability Act (HIPAA) compliant protocol was proved by the Institutional Review Board and written informed consent was obtained from all
Results
Fig. 2a and b shows the averaged CBV maps of the AD/MCI and control groups, respectively. Fig. 3 shows a voxel-by-voxel group comparison. After applying statistical threshold (p < 0.005 for each voxel, cluster size >1250 mm3), the regions showing CBV decline were overlaid on the T1 weighted anatomic image. Significant CBV deficits were observed primarily in white matter, located bilaterally in frontal lobes and extending to parietal lobes (Fig. 3). No significant clusters were observed for the
Discussion
We compared CBV in a group of patients with probable AD or MCI with CBV in an elderly control group. We found that CBV in these patients declines in frontal and parietal lobes; primarily in white matter. The CBV changes are correlated with structural damage as measured by diffusion tensor imaging, which appears to be specific to CBV deficit regions and was not found in regions where CBV was normal. The structural consequences of the CBV deficit are further confirmed by WMH volume, which
Conclusion
Vascular factors may contribute to the pathogenesis of AD. While both gray and white matter were examined, significant CBV deficit regions were primarily located in the white matter of the frontal and parietal lobes, in which CBV was reduced by ∼20% in the patient group. These CBV deficits correlated with parenchymal damage as assessed by diffusion tensor imaging and FLAIR MRI. In addition, the amount of CBV deficit also appeared to correlate with impairment of psychomotor speed.
Conflict of interest
The authors declare that they have no conflict of interest, financial or otherwise, related to the present work.
Acknowledgments
The authors are grateful to Dr. Guanghua Xiao for assistance with data analysis. This work was supported by Alzheimer Association NIRG 05-14056, NIH R21 NS054916, NIH P30 AG12300, and the Texas Instruments Foundation.
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