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• W2897862246 abstract "Converging evidence suggests that the Alzheimer's disease (AD) associated changes in the brain begin decades before symptoms occur. Quantifying microstructural alterations in white-matter tracts may help with early disease detection. We used conventional DTI metrics, as well as novel indices from neurite orientation dispersion and density imaging (NODDI), and a q-space method to detect microstructural changes across the AD spectrum. One hundred subjects, including 40 with normal cognitive function (CN), 38 with subjective cognitive decline (SCD), and 22 with mild cognitive impairment (MCI), underwent Hybrid Diffusion Imaging (HYDI) and detailed neuropsychological assessment (Table 1). HYDI data were used for DTI, NODDI, and q-space computation. Twenty-seven white-matter tracts-of-interest were generated from probabilistic for each subject (Figure 1). Seven diffusion metrics (Table 2) were extracted for each tract-of-interest. Statistical analyses included ANCOVA with Bonferroni correction to test for group differences, penalized logistic regression (LASSO) to identify best predictors, and linear regression to test associations of diffusion metrics with neuropsychological outcomes. White-matter tracts-of-interest generated using probabilistic tractography for one cognitive normal subject. The illustration of tracts was rendered in sagittal (left), coronal (center), and axial (right) views. The 27 tracts-of-interest were grouped into five categories: the brain stem (not shown here), the limbic fibers connecting the limbic system (1st row), the projection fibers (2nd row), the thalamic radiations (3rd row), and the association fibers (4th row). Diffusion metrics differed significantly between MCI and both CN or SCD groups in five tracts-of-interest including the bilateral parahippocampal cingulum (cgh), left posterior thalamic radiation (ptr), forceps major (fma), and left inferior fronto-occipital fasciculi (ifo) (Figure 2). In these tracts, significantly higher radial diffusivity (Dr, Figure 2B) and mean diffusivity (MD), and smaller fractional anisotropy (FA, Figure 2C) were observed in MCI compared to CN or SCD. Consistently, cgh, ptr, and fma showed the best discrimination between MCI and SCD and between MCI and CN with area under the ROC curves (AUC) of up to 0.83 (Figure 3A) and 0.88 (Figure 3B), respectively. RAVLT-Immediate Recall was associated with most diffusion metrics. The MCI group demonstrated strongest correlations with the correlation coefficient (r) of up to 0.82 for Dr in fma (Figure 4). ANOVA results for tracts-of-interest showing significant group-wise differences in diffusion metrics after Bonferroni correction of multiple comparisons over the 27 tracts. Five tracts were identified including the bilateral parahippocampal cingulum (cgh), posterior thalamic radiation (ptr), forceps major (fma), and inferior fronto- occipital fasciculi (ifo). A. Significant group-wise differences are highlighted in orange for higher diffusion measurements in advanced disease stage within the pair or blue for lower. B. White matter tracts that had significant group-wise differences in Dr, with color indicating levels of significance. The illustration of tracts was rendered in sagittal (left), coronal (center), and axial (right) views. C. Tracts that had significant group-wise differences in FA. Best predictors identified by penalized logistic regressions (LASSO). A. The receiver operating characteristic (ROC) curves for each combination of predictors (i.e., diffusion and tract-of-interest pairs) selected by LASSO to discriminate between SCD and MCI. The best predictors for SCD and MCI were Dr in the left parahippocampal cingulum (cgh_I), MD in the forceps major (fma), and FA in the left posterior thalamic radiation (ptr_I). The Area under the ROC curve (AUC) were 0.83 with all three predictors (bold black line), 0.81 with Dr-cgh_I and MD- fma (grey line), and 0.80 with only Dr-cgh_I (light grey line). B. The ROC curves for each combination of predictors selected by LASSO to discriminate between CN and MCI. The best predictors for CN and MCI were FA-ptr_I and OD-cgh_I. The AUC were 0.88 using both predictors (bold black line), and 0.82 using only FA-ptr_I (grey line). C. The anatomical locations of the three most sensitive tracts-of-interest: the left parahippocampal cingulum (cgh_I), the left posterior thalamic radiation (ptr_I) and the forceps major (fma). D. Boxplots of LASSO-selected diffusion parameters (vertical axes) in the three most sensitive tracts-of-interest (horizontal axes) for all groups. Linear regression analyses between the Rey's Auditory Verbal Learning Test immediate response (RAVLT-IR) and radial diffusivity (Dr) in the left parahippocampal cingulum, posterior thalamic radiation, and forceps major. Goodness of fit (r) is labeled with black for all subjects combined (n=100, black regression line), and with red for MCI (n=22; red regression line). Significant level is labeled as: * for p < 0.05, ** for p < 0.01, and *** for p < 0.001. Across the AD spectrum, MCI consistently demonstrated increased radial and mean diffusivity, and decreased fractional anisotropy in the left parahippocampal cingulum, posterior thalamic radiation, and forceps major. While SCD tended to be intermediate between CN and MCI, lower sensitivity might reflect smaller effect size and greater heterogeneity risk factors and outcomes in SCD." @default.
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• W2897862246 date "2018-07-01" @default.
• W2897862246 modified "2023-10-12" @default.
• W2897862246 title "IC‐P‐172: WHITE‐MATTER MICROSTRUCTURE IN EARLY STAGE ALZHEIMER'S DISEASE" @default.
• W2897862246 doi "https://doi.org/10.1016/j.jalz.2018.06.2239" @default.
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