Abstract Background: Although Down syndrome (DS) is a trisomy of chromosome 21 being the most frequent human chromosomal disorder mainly associated with variable levels of intellectual disability and other dysfunctions, the only dose disbalance effect would not enough to explain its genetic and functional complexity neurophenotype. In this context, we aimed to analyze and compare the disruption of transcriptome of several brain areas from individuals with DS and euploid controls as a new approach to consider a global systemic differential disruption of gene expression beyond of chromosome 21. Methodology: To perform the analysis carried out in the present study, we used data from a DNA microarray experiment with ID GSE59630 previously deposited in the GEO DataSet of NCBI database. The array contained log2 values of 17,537 human genes expressed in several aeras of human brain. The data was collected from 58 postmortem brain samples of individuals with DS and 58 samples from euploid controls. We calculated the differential gene expression (Z-ratio) of all genes from the microarray according to the several brain areas, gene distribution per chromosome and age ranks. Results: We found several differences in gene expression along the DS brain transcriptome, not only in the genes located at chromosome 21 but in other chromosomes. Moreover, we registered the lowest Z-ratio correlation between the age ranks of 16-22 weeks of gestation and 39-42 years (R 2 =0.06) and the highest Z-ratio correlation between the age ranks of 30-39 years and 40-42 years (R 2 =0.89). The analysis per brain areas showed that the hippocampus and the cerebellar cortex had the most different gene expression pattern when compared to the brain as a whole. Conclusions: Our results revealed the complexity of gene expression networks in the transcriptome profiles of hippocampus, dorsolateral prefrontal cortex (DFC) and cerebellar cortex (CBC). Moreover, our approach opens a new vision of the genomic complexity of DS as a pathology of multiple and complex variables that are playing altogether to modeling their pathogenesis.