Abstract

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

Highlights

  • Alzheimer’s disease (AD) is the most common type of dementia, affecting approximately 10% of the population over age 65 (Alzheimer’s Association, 2018)

  • We report the average age, Mini-Mental State Examination (MMSE), Clinical Dementia Rating scale sum-of-boxes (CDR-sob), and AD Assessment scale 13 (ADAS-cog) measures, and their standard deviations. ∗Data not available for all participants: MMSE N = 315; CDR-sob N = 316 and ADAS-cog N = 278. +We recognize the limitations of these assessments as proxy measures of specific cognitive abilities (Balsis et al, 2015)

  • Age Effects in Cognitively Normal Participants From ADNI2 and ADNI3 Protocols When data were pooled across ADNI2 and ADNI3, significant associations with age were detected throughout the white matter (WM)

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Summary

Introduction

Alzheimer’s disease (AD) is the most common type of dementia, affecting approximately 10% of the population over age 65 (Alzheimer’s Association, 2018). DMRI has since been used in numerous studies to understand the effects of AD on white matter (WM) microstructure and brain connectivity (Daianu et al, 2013a,b; Nir et al, 2013; Prasad et al, 2013) Some of these approaches use scalar dMRI measures to evaluate microstructural WM changes not detectable with anatomical T1-weighted images (Giulietti et al, 2018), while others use tractography and graph-theory analysis to study abnormalities in structural brain networks (Nir et al, 2015; Hu et al, 2016; Maggipinto et al, 2017; Sulaimany et al, 2017; Powell et al, 2018; Sanchez-Rodriguez et al, 2018). These studies point to WM abnormalities in AD, which may play a key role in early pathogenesis and diagnosis (Sachdev et al, 2013)

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