Brain structure and allelic association in Alzheimer's disease: Genetics/genetic factors of Alzheimer's disease. (7th December 2020)

• Record Type: Journal Article
• Authors: Moon, Seok Woo; Dinov, Ivo D.; Zhao, Lu; Matloff, William
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 3
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.038091
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data archive we examined dementia‐related late‐onset cognitive impairment using neuroimaging and genetics biomarkers. Method: We used FreeSurfer to parcellate the structural brain magnetic resonance imaging (MRI) data to derive homologous imaging signature vectors composed of 1, 118 computed imaging biomarkers. The demographics of the 1, 026 ADNI 1, ADNI GO and ADNI 2 arms of the ADNI study included participants ages 405 to 85, 266 normal CN's (CDR = 0, Male:138, Female:128), 572 mild cognitively impaired MCI's (CDR = 0.5, Male:227, Female:245), and 188 AD patients (CDR = 0.5/1, Male:102, Female:86). We extracted the AD‐related genetic markers and imaging markers for the network analysis. Using Plink, we modelled the genetics data and the using the Pipeline environment we identified single nucleotide polymorphisms (SNPs) associated with the clinical diagnosis. Network analyses were applied to examine multidimensional imaging‐genetics associations. Result: The expected significant correlations between the SNPs and the neuroimaging phenotypes were confirmed using neuroimaging genetics networking analyses. These results may explain some of the differences among the AD, MCI and NC groups. We identified many associations between neuroimaging markers and genomic markers. For instance, cortical thickness, e.g., left and right hemispheres mean thickness, was more sensitive than regional volume morphometrics in capturing structural brain changes. We also identified salient neuroimaging (NI) markers that played important roles in neuroimaging networks discrimination between CN and MCI groups. However, regional volumes appeared to be more sensitive than thickness measures in discriminating between the MCI and AD groups. Conclusion: Structural brain changes are important indicators of dementia progression. Network analysis pairing morphometric biomarkers with genetic indicators allows investigation of clinical and phenotypic associations that facilitate deep systematic understanding of genetic and environmental influences on aging and cognitive decline. Anatomical brain changes reflect the AD complex pathogenesis, their genetic associations, and their longitudinal propagation provide valuable clues to the progression of dementia and differences with normal aging. Further studies are necessary to untangle various deep brain‐networks and interpret their structural association with disease.