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Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

ABSTRACT: Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: "are there regions in the white matter where Alzheimer's disease has a different effect than aging or similar effect as aging?" and "are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer's disease but with differing multivariate effects?"

SUBMITTER: Konukoglu E

PROVIDER: S-EPMC4912936 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

Konukoglu Ender E Coutu Jean-Philippe JP Salat David H DH Fischl Bruce B

NeuroImage 20160419

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a te ...[more]

PMID: 27103138

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Project description:White matter alterations are present in the majority of patients with Alzheimer's disease type dementia. However, the spatiotemporal pattern of white matter changes preceding dementia symptoms in Alzheimer's disease remains unclear, largely due to the inherent diagnostic uncertainty in the preclinical phase and increased risk of confounding age-related vascular disease and stroke in late-onset Alzheimer's disease. In early-onset autosomal-dominantly inherited Alzheimer's disease, participants are destined to develop dementia, which provides the opportunity to assess brain changes years before the onset of symptoms, and in the absence of ageing-related vascular disease. Here, we assessed mean diffusivity alterations in the white matter in 64 mutation carriers compared to 45 non-carrier family non-carriers. Using tract-based spatial statistics, we mapped the interaction of mutation status by estimated years from symptom onset on mean diffusivity. For major atlas-derived fibre tracts, we determined the earliest time point at which abnormal mean diffusivity changes in the mutation carriers were detectable. Lastly, we assessed the association between mean diffusivity and cerebrospinal fluid biomarkers of amyloid, tau, phosphorylated-tau, and soluble TREM2, i.e. a marker of microglia activity. Results showed a significant interaction of mutations status by estimated years from symptom onset, i.e. a stronger increase of mean diffusivity, within the posterior parietal and medial frontal white matter in mutation carriers compared with non-carriers. The earliest increase of mean diffusivity was observed in the forceps major, forceps minor and long projecting fibres-many connecting default mode network regions-between 5 to 10 years before estimated symptom onset. Higher mean diffusivity in fibre tracts was associated with lower grey matter volume in the tracts' projection zones. Global mean diffusivity was correlated with lower cerebrospinal fluid levels of amyloid-β1-42 but higher levels of tau, phosphorylated-tau and soluble TREM2. Together, these results suggest that regionally selective white matter degeneration occurs years before the estimated symptom onset. Such white matter alterations are associated with primary Alzheimer's disease pathology and microglia activity in the brain.

Project description:Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.

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Dataset Information

Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

Publications

Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

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