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05. In addition, a bootstrap resampling procedure was implemented to estimate http://www.selleckchem.com/products/rxdx-106-cep-40783.html the variability of the regional weights in the patterns about their point estimate values with a 500 iteration resampling procedure with replacement [Habeck et al., 2005]. A Z-value threshold ��|1| for FDG-PET and VBM data and a Z-value threshold ��|3| for PIB-PET data were adopted purely for visualization purposes to show the full extent of brain regions contributing to the combined SSM pattern. It is important to note that bootstrapping was conducted to estimate voxel-wise variability, which is analogous to univariate approaches, and the resulting statistics are not for hypothesis testing unlike the permutation tests that were adopted to test hypotheses about the association between PIB index and covariance patterns. All nonimage analyses were conducted using SPSS software (version 19). Multiple regressions were used to assess the relationship between the degree of pattern expression as quantified by the SSFs and cognitive performance. That is, we conducted multiple regressions for a total http://www.selleckchem.com/products/CP-673451.html of three pattern expression scores (i.e., SSF-PIB, SSF-FDG, and SSF-VBM) and five cognitive factor scores (i.e., EM, VM, EXE, SM, and WM) with each SSF score being a predictor of interest and each cognitive factor score being a dependent measure, resulting in 15 regression models. Age, sex, and education were controlled in all analyses. Statistical significance was determined at P https://en.wikipedia.org/wiki/Crotamiton SM; digit span subtests for WM; and Trail Making subtests and Stroop for EXE. Because of the nature of factor analysis, however, all neuropsychological test scores contributed to each factor score to a varying degree. The association between the global PIB index and A�� deposition topography was tested by a multiple regression model with SSM subject factor scores for the eight component patterns selected by AIC. The linear combination of the selected eight PCs (i.e., PCs 1, 2, 3, 4, 5, 6, 8, and 10) accounted for 98% of the variance in the PIB data (i.e., R2 = 0.98), and the permutation test showed that the patterns of a linear combination of PCs significantly predicted the PIB index, P