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Uncovering superficial white matter changes in young‐onset Alzheimer’s disease
Author(s) -
Veale Thomas,
Malone Ian B,
Poole Teresa,
Parker Thomas D,
Slattery Catherine F,
Schott Jonathan M,
Zhang Hui,
Fox Nick C,
Cash David M
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.039746
Subject(s) - fractional anisotropy , diffusion mri , white matter , region of interest , orientation (vector space) , neuroscience , psychology , nuclear medicine , nuclear magnetic resonance , magnetic resonance imaging , physics , medicine , mathematics , radiology , geometry
Background Superficial white matter (SWM), subjacent to the cortex, has unique vulnerabilities to Alzheimer’s disease (AD) but is understudied. The organisational complexity of SWM means conventional diffusion tensor imaging (DTI) has difficulty characterising both the degeneration and dispersion of fibres. We used both DTI and Neurite Orientation Dispersion and Density Imaging (NODDI) to assess AD‐related SWM changes while accounting for fibre density and dispersion. Method 51 participants were included: 22 healthy controls (mean age 61 (+/‐6) years) and 29 individuals with young onset AD (YOAD: mean age 62 (+/‐5) years; 18 with typical AD, 11 posterior cortical atrophy). We used single‐shell (b=1000) and multi‐shell (b=300; b=700; b=2000) diffusion MRI (dMRI) data. Preprocessing involved correcting for eddy currents, motion and susceptibility artefacts followed by fitting DTI and NODDI models. DTI and NODDI metrics were sampled across the GM/WM FreeSurfer surface in 15 cortical regions of interest (ROI) (Figure 1). Linear mixed effect models of fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI) and orientation dispersion index (ODI) were fit for each ROI while controlling for cortical thickness (correcting for multiple comparisons using False Discovery Rate of 0.05). Average Marginal Effects (AMEs) representing the change in YOAD versus controls, while holding other fixed effects constant, were calculated. Result In the GM, widespread significant differences were observed in both DTI and NODDI metrics ( pFDR < 0.05) (Figure 2). In the SWM, YOAD patients had lower FA and/or changes in MD in five ROIs ( pFDR < 0.05) (including parahippocampal gyrus and superior temporal cortex). NODDI metrics showed more widespread differences in the SWM, as the YOAD group had lower NDI and/or higher ODI across ten ROIs ( pFDR < 0.05) (including parahippocampal gyrus and inferior parietal cortex). Conclusion Diffusion imaging can detect microstructural changes occurring within the SWM of YOAD individuals in regions associated with AD‐pathology. These changes in SWM not only persist, but are more prominent, when independently accounting for the density and dispersion of fibres using NODDI. These novel NODDI SWM measures may uncover previously under‐recognised degeneration and organisational WM alterations in AD.