Furthermore, because functional coupling has been reported between some genes in the AA or DHA cascades, we expected that genes within the AA and DHA metabolic cascades would be expressed cooperatively. We examined age variations throughout life span in human brain GSI-IX expression levels of a limited set of genes involved in PUFA metabolism. We chose AA and DHA metabolism because these PUFAs and their metabolites influence multiple brain processes, including neurotransmission, synaptic growth, gene transcription, membrane fluidity, and the pathological processes of apoptosis, neuroinflammation and excitotoxicity. We analyzed two postnatal age intervals, Development, and Aging, chosen on the basis of known functional and structural brain changes. Confirming these intervals as separate time periods involving distinct aspects of brain function and structure, we showed that expression patterns of most genes were statistically different between Development and Aging. Correlations between gene expression level and age were generally lower in the Aging interval than the Development interval, suggesting that with aging, gene expression regulation is less connected to programmed brain changes. Thus as an individual ages, gene expression likely depends more on individual factors, such as health status, environmental stress, nutrition, and other factors influencing lipid metabolism. Generally, significant correlations between genes were not related to chromosomal location. First, its expression data are obtained only from postmortem prefrontal cortex gray matter. This brain region has comparatively prolonged myelination and is reported to show disproportionate degeneration with aging as compared to other neocortical regions. Expression patterns would be expected to differ between regions and many age related changes in brain occur in white matter, which is not analyzed in the BrainCloud project. Finally, BrainCloud does not distinguish between cell types. The Allen Brain Atlases found that astrocytes, oligodendrocytes, and neurons exhibit different age-related changes in gene expression. On the other hand, to-date BrainCloud has the largest number of samples of gene expression data in the prefrontal cortex. The Allen Human Brain Atlas contains data from only 3 individuals, all male, while the Loerch study contains data from 28 human samples. As such, BrainCloud is an extremely powerful tool for studying age-related gene expression changes in a diverse sample population. In the future, it would be of interest to investigate possible mechanisms of the age-related changes in mRNA levels. Methylation of gene promoters, histone acetylation and methylation state, transcription factors, miRNAs, DNA sequences of ciselements, and feedback regulation by AA and DHA and their metabolites likely play a role in changing mRNA expression levels. Generally, gene groups whose expression decreases with age appear to have higher promoter GC content than other genes, suggesting differences in methylation state, and human brain aging is associated with a global hypomethylation. Gene-specific promoter methylation can now be analyzed in BrainCloudMethyl, a database similar to BrainCloud that contains CpG methylation data.