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• W2895955372 abstract "A better understanding of baseline neuroimaging predictors of longitudinal change in amyloid deposition and neurodegeneration across the AD spectrum would be particularly relevant for clinical trials, disease monitoring and physiopathological mechanisms understanding. Therefore, we assessed regional associations between baseline and longitudinal changes in volume, glucose metabolism and amyloid deposition in cognitively unimpaired and impaired older adults. 118 participants (43 normal older adults, 20 patients with SCD, 37 with MCI and 18 with AD dementia) underwent 2-to-3 MRI, 18F-FDG-PET and 18F-AV45-PET scans (Table_1). Baseline and follow-up PET data were spatially normalized using MRI data and scaled using the brain stem and cerebral white matter respectively. The mean gray matter volume, FDG- and AV45-SUVR of 53 bilateral ROIs from the Harvard–Oxford atlas were used to perform mixed-effect models to assess intra- and inter-modality relationships between baseline and annual rate of change. Models predicting longitudinal changes with baseline values of more than one modality were also performed, and their fit was compared using the Akaike Information Criterion. Intra-modality relationships revealed relatively weak associations between baseline and rate of change: the strongest relationships involved baseline medial temporal lobe volume and baseline parietal lobe metabolism (Fig_1). Inter-modality relationships showed i) weak associations with baseline volume, ii) strong relationships in fronto-parieto-temporal regions between baseline FDG-SUVR and rate of volume and AV45-SUVR changes, and iii) strong associations between baseline AV45-SUVR in most brain regions and rate of volume change in fronto-parieto-temporal regions. Comparisons between the fit of distinct nested models revealed that baseline volume poorly contributed to the prediction of rate of volume change when baseline FDG- and/or AV45-SUVR were included in the models (Fig_2). By contrast, both baseline FDG- (especially in temporal areas) and AV45-SUVR (in most brain regions) improved the prediction of rate of volume change in tempo-parietal regions. Heatmaps showing the significance (t-values) of intra- and inter-modality associations between baseline and longitudinal change of each imaging modality. Linear mixed effects analyses were performed using R (nlme package) to estimate the effect of baseline on rate of change over time, whilst incorporating the longitudinal nature of the data by including within-person variation as random effects. Age at baseline and sex were included as covariates in all models. Models can be summarized as follow: - Intra-modality relationship → (Modalityl - Annual Rate of Change)Region i ∼ (Baseline Modalityl)Region j + Age + Sex + (1 | Subject) - Inter-modality relationship → (Modalityl - Annual Rate of Change)Region i ∼ (Baseline Modality2)Region js + Age + Sex + (1 | Subject) where Modality corresponds to volume, FDG- or AV45-SUVR, and Region i and Region j represent one bilateral ROIs of the Harvard-Oxford structural atlas. Note that on the heatmaps, ROIs are shown in the same order for x- and y-axis. Therefore, the upper diagonal represents the relationships when the same regions was considered (i.e. Region i = Region j). Heatmaps showing the significance (p-values) of the comparisons between the fit of distinct nested linear mixed effects models using the Akaike Information Criterion (AIC). Columns show the influence of including baseline volume (heatmaps of the first column), baseline FDG-SUVR (heatmaps of the second column) and baseline AV45-SUVR (heatmaps of the third column) in the statistical models to predict annual rate of volume change. Linear mixed effects analyses were performed using R (nlme package) and included subjects as random effect, and both age at baseline and sex as covariates. Models can be summarized as follow: -1 Modality as predictor → (Annual Rate of Volume Change)Region i ∼ (Baseline Modalityl)Region j+ Age + Sex + (1 | Subject) -2 Modalities as predictor → (Annual Rate of Volume Change)Region i ∼ (Baseline Modalityl)Region j + (Baseline Modality2)Regjon j + Age + Sex + (1 | Subject) -3 Modalities as predictor (Annual Rate of Volume Change)Region i, ∼ (Baseline Modalityl)Region j + (Baseline Modality2)Regjon i + (Baseline Modality3)Region j + Age + Sex + (1 | Subject) where Modality corresponds to volume, FDG- or AV45-SUVR, and Region i and Region j represent one bilateral ROIs of the Harvard-Oxford structural atlas. Note that on the heatmaps, ROIs are shown in the same order for x- and y-axis (upper diagonal: Region i = Region j). Longitudinal changes in neuroimaging markers were associated in a region-specific manner with baseline metabolism versus in a non region-specific manner with baseline AV45-SUVR. Interestingly, baseline metabolism and amyloid deposition better predicted longitudinal volume changes than baseline volume, which may have clinical implications for detecting patients with the highest rate of atrophy over a follow-up period." @default.
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• W2895955372 date "2018-07-01" @default.
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• W2895955372 title "IC‐P‐134: INTERPLAY BETWEEN BASELINE AND LONGITUDINAL CHANGE IN NEUROIMAGING MARKERS OF AMYLOID DEPOSITION AND NEURODEGENERATION ACROSS THE ALZHEIMER'S DISEASE SPECTRUM" @default.
• W2895955372 doi "https://doi.org/10.1016/j.jalz.2018.06.2200" @default.
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