Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Abstract

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter 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 (FA DTI ) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FA TDF ), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FA DTI . 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 Alzheimer's disease: the Alzheimer's Disease 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 model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampalcingulum 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 and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FA TDF detected robust widespread associations with clinical measures, while FA DTI 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.

White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study

“…Scanner type was used as the batch effect and diagnosis (patients versus controls) and syndrome (GGE, TLE-HS, TLE-NL, and ExE) were used as the biological phenotypes of interest. This technique has been recently applied in other ENIGMA DTI investigations of brain disorders (Villalón-Reina et al, 2019;Zavaliangos-Petropulu et al, 2019).…”

Replication and generalization in applied neuroimaging

“…Medicine studies such as ADNI3 for Alzheimer disease (Reid et al, 2017;Zavaliangos-Petropulu et al, 2019) and TRACK-TBI for traumatic brain injury (Yuh et al, 2013). This approach is applicable to coordinated multi-center studies.…”

Apparent propagator anisotropy from single‐shell diffusion MRI acquisitions

“…We have selected these two cases as they are illustrative of very different clinical studies: according to the literature, significant differences in diffusion anisotropy can be easily found between MCI and controls in a large number of brain regions. 29,30 In contrast, although patient-control anisotropy differences have been reported in white matter regions for PD, 31 such differences are harder to find using standard dMRI analysis. This way, the ability of the new measures to detect pathology is evaluated under two different difficulty levels.…”