Data were analyzed using open source software packages from Bioconductor (http://bioconductor.org) designed for the statistical language R (http://www.r-project.org) and Microsoft Excel unless otherwise noted. Data from each brain region were analyzed independently. Gene Expression Console (version 1.4, Affymetrix, Santa Clara, CA) was employed for data preprocessing (detection above background, DABG [30], and robust multichip analysis, RMA [31]) and identification of sample outliers using all mouse probesets. Five samples (one AMY 120h control, one PFC 0h control, two NAC 8h controls and one NAC 0h CIE-treated) were identified as outliers using Gene Expression Console and were removed. Data were filtered to include only mature mouse microRNAs with a detection p value < 0.06 on 80% of arrays. Mouse microRNAs were updated to miRbase version 21 annotations using miRbaseTracker [32]. Differential expression analysis for each time point was conducted using empirical Bayes moderated t-statistics from the Bioconductor package limma [33] to compare treated and control mice. MicroRNAs were considered DE at a nominal value ≤ 0.05. A nominal, rather than FDR-corrected, p value was utilized to preserve as much DE information as
