136 Chapter 6 Statistics All analyses were performed in R version 4.0.3. For all analyses, BPND and R1 were transformed into z-scores, for comparability of effect sizes. Z-scores were based on baseline PET scans (n=187). We used the false discovery rate (FDR) to correct for multiple testing, and FDR corrected P values <0.05 were considered significant. We used linear mixed models (LMM) with time as determinant to estimate slopes for imaging measures and cognitive tests for the whole group. First, we investigated the relationship between baseline R1, baseline BPND and cognitive test performance, using LMM. For this set of models, the composite ROI was used. Model 1 included R1, time and R1*time as predictors and cognitive test results as outcome. Separate models were run with different cognitive tests as outcome measure (n=10 neuropsychological tests). Next, we repeated the analyses with BPND instead of R1 as predictor (model 2). Then we included both R1 and BPND as predictors in the model (predictors: R1, BPND, time, R1*time, BPND*time; model 3). When we ran model 3, we tested whether there was an interaction between R1*BPND*time for all neuropsychological tests. When this interaction term was significant, we provide the betas full model including the three-way interaction term. When the three-way interaction was not significant, it was removed from the model. All models were corrected for age, sex, education and PET and MRI scanner type. Models included a random intercept, and a random slope if this improved the model fit, which was the case for RAVLT immediate, RAVLT delayed, Stroop II, Stroop III and MMSE. We then used LMM to assess the cross-sectional and longitudinal relationship between BPND and R1. We first assessed the effect of baseline BPND on R1. Model 1 included baseline BPND, time and BPND*time as predictors, and R1 as outcome (including baseline and follow-up R1 values). Model 2 was additionally corrected for age, sex and PET and MRI scanner type. In the models, ‘BPND’ represents the effect of BPND on R1, when time=0. The interaction term ‘BPND*time’ reflects the effect of BPND on annual change in R1. We analyzed the associations in frontal, temporal, parietal, occipital and composite regions separately, such that for each analysis, the ROI used for BPND was the same as the ROI used for R1. Subsequently, we performed an additional set of analyses, where predictors and outcome were reversed so that baseline R1, time and R1*time were used as predictors, and longitudinal BPND as outcome. Models included a random intercept. For visualization in the figures, we used tertiles to divide our sample based, on R1 (low, intermediate and high baseline R1) and BPND (low, intermediate and high baseline BPND).
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