Meta-analysis has emerged as an essential tool in modern genetic and genomic analysis. It can uncover a significant effect from a combined analysis as integration of a broader and/or richer collection of data has the potential to generate results that have greater confidence, and place less reliance on a single dataset. Although meta-analysis often includes large number of unrelated studies, we followed a more conservative approach in order to concentrate on microarray gene expression datasets that focused on the hippocampus dependent ASLI as assessed by MWM test. We started the data preprocessing with raw expression data, which gave us the opportunity to perform consistent quality assessment, preprocessing, and filtering of imperfect arrays and outlier values. It also allowed correction of batch effects and removal of any unexplained technical variations. Our results confirmed the findings of recent studies and demonstrated the necessity of removing batch effects from microarray data before integrating 
them in any analysis. Next, we performed the random effect size meta-analysis by keeping the individual studies separate and then only combining the probe-set specific effects. We also performed the traditional differential expression analysis in parallel to the ES analysis after merging all probe-set data into a single pool through the process of Oxysophocarpine cross-study and cross-platform data normalization. Even though the DE analysis was able to detect significant DE level, the difference was smaller compared to the ES. Overall, the ES analysis seems to be a better approach than DE analysis, particularly when combining data from different studies and platforms. Nonetheless, the DE results helped us verify the ES outcomes and better screen the aging and ASLI Salvianolic-acid-B associated genes. It is important to point out that during the data integration process we worked at the probe-set level rather than at the gene level. This is essential when combining data from independent microarray results from different platforms. It is important to note that Fischer 344 strain of rats have a median life-span of 23�C31 months in captivity. Their normal age-related incidence of neoplasms and degenerative diseases is high, particularly, once the rats pass 24 months of age. Also, the effect of aging and ASLI on brain gene expression is evident in the aged in comparison to the young rats as discussed above. Indeed, it is expected that studies on animals beyond 26 weeks of age may show involvement of additional genes in this phenomenon and the effects observed could be more pronounced at later stages of the rat’s life-span. In conclusion, we report that aged animals display a significant decrease in cell viability, axonogenesis, and inositol phosphate metabolism. They also show a significant increase in the migration of cells and differentiation of cells functions due to the altered gene expression. The regulatory interactions of the differentially expressed genes seems to affect molecular transport, cell to cell signaling and interaction, nervous system development and function, and cell death and survival. The genes that are known to be involved in the above functional changes and/or those present most significant expression changes in the aged or agedimpaired animals could be broadly classified into three major categories such as GA, GASI, and GANSI. The GA genes are mostly involved in inflicting various aging related senescence and generally are not associated with any learning impairment. The GASI genes, on the other hand, are associated with age-related neurological disease syndromes e.g. Alzheimer’s disease, which generally affect normal cognitive functioning and may result into syndromic memory.