0267
Assessment of Cerebrovascular Disease and White Matter Neurite Density in Alzheimer’s Disease1Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 2Medicine, University of Wisconsin - Madison, Madison, WI, United States, 3Radiology, University of Wisconsin - Madison, Madison, WI, United States, 4Pediatrics, University of Wisconsin - Madison, Madison, WI, United States, 5Psychiatry, University of Wisconsin - Madison, Madison, WI, United States
Synopsis
White matter (WM) microstructural alterations have been shown to occur in Alzheimer’s disease (AD) and may be partially mediated by cerebrovascular disease (CVD). The objective of this study is to use neurite orientation dispersion and density imaging (NODDI) to assess differences in neurite density (NDI) and its relationship to measures of CVD from 4D flow MRI in cognitively normal (CN) and AD subjects. Our results showed differences in NDI between groups in various WM tracts and found correlations between NDI and cerebral blood flow in CN subjects in several WM structures.
Introduction
White matter (WM) microstructural alterations have been observed in patients with Alzheimer’s disease (AD)(1-4) and have been hypothesized to be influenced by cerebrovascular disease (CVD) through vascular-mediated neuronal dysfunction and impaired Aβ drainage(5). This hypothesis is supported by recent findings that indicate that blood-brain barrier dysfunction(6), hypoperfusion(7), altered cerebrovascular hemodynamics(8-10), and various cardiovascular risk factors(11) are associated with AD progression and may even precede amyloid deposition. However, it is still unclear the degree to which macroscopic CVD influences WM at the microstructural level. In this study, correlations between CVD on WM neurite density, as measured by neurite density index (NDI), are assessed in a cohort of AD and cognitively normal (CN) control subjects using 4D flow MRI and neurite orientation dispersion and density imaging (NODDI)(12).Methods
A total of 41 age-matched CN control (females=23, mean age=74±6.8y) and 20 AD (females=13, mean age=73±9.2y) subjects were recruited for this study. AD individuals were clinically characterized as “dementia due to probable AD” by a multidisciplinary consensus using applicable clinical, laboratory, and imaging criteria(13,14).MR imaging data were acquired on a 3T Discovery MR750 scanner (GE Healthcare, Waukesha, WI) using a 32-channel head coil (Nova Medical, Wilmington, MA). 4D flow data were acquired using a 3D radial sequence(15,16) using 5-point encoding (Venc=80cm/s; TR/TE=7.4/2.7ms; flip angle=8°; resolution=0.7mm3 isotropic; projections≈11,000). Two separate reconstructions were performed with retrospective cardiac gating: (1) a standard reconstruction with temporal resolutions≈50ms and (2) an ultrahigh temporal resolution (≈8ms) reconstruction using locally low rank constraints and flow encode splitting(17). The standard reconstruction was used to compute carotid pulsatility index, averaged between left and right internal carotid arteries (ICAs), and total cerebral blood flow (tCBF), sum of flow in the ICAs and basilar artery, using custom-built MATLAB (The Mathworks, Natick, MA) tools(18). Using the ultrahigh temporal resolution reconstruction, carotid pulse wave velocity measurements were obtained using previously described methods(17) and were averaged between the left and right ICAs. A multi-shell spin-echo echo-planar imaging sequence was used to acquire 69 total diffusion-weighted images across 4 shells (6×b=0 s/mm2, 9×b=500s/mm2, 18×b=800s/mm2, and 36×b=2000s/mm2; TR/TE=8575/76.8ms; resolution=2mm3 isotropic). Standard diffusion image preprocessing was performed using FSL(19) and MRtrix3(20), including denoising(21), Gibb’s ringing correction(22), brain extraction(23), and eddy current correction(24). Diffusion tensors were estimated using dtifit in FSL. NDI parameter maps were generated using the NODDI Matlab Toolbox v1.0.4 in MATLAB 2020b.
Two-sample t-tests and Fisher’s exact tests were used to assess demographic differences. Whole-brain voxel-wise group differences in NDI measures, as well as correlations between NDI and carotid pulse wave velocity, pulsatility index, and tCBF for both groups, were explored using tract-based spatial statistics (TBSS)(25) and randomise (permutations=5,000, threshold-free cluster enhancement)(26) in FSL. The TBSS pipeline included nonlinear registration of images to 1mm FMRIB58_FA standard space(27). Significant WM tracts were identified using the pre-labeled ICBM-DTI-81 WM atlas. These significant tracts were then analyzed post hoc using a region of interest (ROI)-based analysis from the WM atlas. Analysis of covariance (ANCOVA) tested differences in NDI between AD and control groups in ROIs identified from TBSS. Subsequently, correlations between NDI and CVD metrics were tested using fixed-effect robust linear regression models including age and sex as covariates.
Results
No demographic differences existed between cohorts (age: p=0.96, sex: p=0.58). TBSS showed significant group differences in NDI in multiple tracts (Figure 1). Individualized post hoc ROI analysis further substantiated these differences in the corpus callosum, corona radiata, posterior thalamic radiation, external capsule, cingulum, cingulate cortex, superior longitudinal fasciculus, and uncinate fasciculus (Table 1). TBSS revealed some areas of negative correlations between pulsatility index and NDI in the control group; however, upon performing ROI analysis in these areas, there were no significant correlations after Bonferroni correction. There were a number of regions with positive correlations between tCBF and NDI in the control group (Figure 2). ROI analysis demonstrated regions of positive correlation between tCBF and NDI (Table 2); however, only the superior longitudinal fasciculus (SLF) and body of corpus callosum were significant after multiple comparison correction. Correlation plots of NDI versus tCBF in the SLF are shown in Figure 3. There were no significant correlations between any CVD measure and NDI in the AD group.Discussion
Differences in WM neurite density between the control and AD groups in various subcortical structures support findings from other studies of similar scope(1,2). Additionally, correlations between tCBF and NDI were observed in control subjects. The lack of an observed association between CVD and NDI in the AD group may partly be due to the relatively low number of subjects in this cohort. Increasing sample sizes, and incorporating subjects along the AD continuum, may strengthen statistical inferences made about these relationships. Furthermore, longitudinal studies assessing NDI changes in individuals could elucidate the relationships between trajectories of cerebral blood flow and WM degeneration in preclinical AD.Conclusion
NODDI and 4D flow MRI allowed for the assessment of white matter neurite density (NDI) and cerebrovascular hemodynamics in cognitively normal and AD subjects. Differences in NDI between groups were observed, and there was a relationship between cerebral blood flow and NDI in several white matter structures. Increased sample sizes and longitudinal assessments are warranted for future studies.Acknowledgements
We gratefully acknowledge research support from GE Healthcare and funding support from the Alzheimer’s Association and NIH grants: AARFD-20-678095, P50-AG033514, RO1EB027087, and ROIAG021155.References
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Figures
Abbreviations: NDI=neurite density index; AD=Alzheimer's disease; CN=cognitively normal; TBSS=tract-based spatial statistics; R=right; L=left.
Mean NDI values are listed as mean ± 1 standard deviation.
*p-values <0.05; **p-values <0.002 (Bonferroni correction for 25 ROIs).
Abbreviations: tCBF=total cerebral blood flow; NDI=neurite density index; AD=Alzheimer's disease; CN=cognitively normal; TBSS=tract-based spatial statistics; R=right; L=left.
β: regression coefficient; R2: adjusted coefficient of determination.
*p-values <0.05; **p-values <0.004 (Bonferroni correction for 13 ROIs).