P3‐104: GENE‐BRAIN STRUCTURE NETWORKING ANALYSIS IN ALZHEIMER'S DISEASE USING THE PIPELINE ENVIRONMENT

This article uses subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate and understand dementia-related late-onset cognitive impairment using neuroimaging and genetics biomarkers. The 1,245 subjects implemented using ADNI 1, ADNI GO and ADNI 2 were divided into three groups: those with Alzheimer's disease (AD), those with mild cognitive impairment (MCI), and those in the normal control (NC) group. Two hundred twenty eight of the subjects qualified for AD diagnosis at the baseline; 684 had MCI; and 333 were included in the NC group. The structural ADNI data were parcellated using FreeSurfer metrics, and all the SNPs for the 1,245 subject were extracted using Plink and the Pipeline environment. Network analyses were applied for all the subjects. Our previous study for the AD, MCI and NC subjects (using ADNI 1 - 808 subjects), indicated the significant associations between the SNPs and the neuroimaging phenotypes for the AD, MCI and NC subjects. The previous findings included that the set of 140 genes chosen (from 416 SNPs with p < 0.00005) represented commonly appearing genes in known AD gene networks. So we expect that we can get more meaningful result from the 1,245 subjects implemented using ADNI 1, ADNI GO and ADNI 2, because more big data have built up. We expect the significant correlations between the SNPs and the neuroimaging phenotypes in the 1,245 subjects in terms of neuroimaging genetics networking analyses. These analyses may explain some of the differences among the AD, MCI and NC groups.