There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | David Park |
| Institution: | Columbia University |
| Department: | Biomedical Engineering |
| Country: | |
| Proposed Analysis: | Despite continuing efforts for developing therapeutic measures, effective intervention of AD remains elusive due to multiple genetic risk factors involved in its complex causal pathways. For instance, the inconclusive evidence from recent trials [1] of anti-amyloid suggests that the amyloid cascade hypothesis alone cannot explain the disease, and thus call for a systematic approach [2] to account for the heterogeneous genetic pathways and alternative biomarkers. Thanks to large-scale datasets, such as the UK Biobank [7] and ADNI [8], genome-wide association studies (GWAS) (as well as whole genome (WGS) and exome sequencing (WES)) are now successfully uncovering the association between causal gene variants and corresponding phenotype. GWAS analyses have discovered dozens of gene variants associated with AD endophenotype such as Aβ, tau, cerebrospinal fluid [3], or brain structure [4]. The discovered gene variants are subsequently utilized for studying the causal pathways [5] or a direct prediction of AD progression [6]. As the number of known gene variants increases, the needs for adequate methods in understanding individual variants and relevant disease pathways are also growing. This research project aims to analyze known genetic variants for AD based on morphological evidence from the brain-imaging data, and further characterize the morphological traits of each AD pathway in an interpretable manner [2]. [1] Hardy et al. (2014). Pathways to Alzheimer’s disease. Journal of Internal Medicine, 275(3), 296–303. https://doi.org/10.1111/joim.12192 [2] Medina et al. (2017). Toward common mechanisms for risk factors in Alzheimer’s syndrome. Alzheimer’s and Dementia: Translational Research and Clinical Interventions, 3(4), 571–578. https://doi.org/10.1016/j.trci.2017.08.009 [3] Deming et al. (2017). Genome-wide association study identifies four novel loci associated with Alzheimer’s endophenotypes and disease modifiers. Acta Neuropathologica, 133(5), 839–856. https://doi.org/10.1007/s00401-017-1685-y [4] Elliott et al. (2018). Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature, 562(7726), 210–216. https://doi.org/10.1038/s41586-018-0571-7 [5] Jones et al. (2010). Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer’s disease. PLoS ONE, 5(11). https://doi.org/10.1371/journal.pone.0013950 [6] Oriol et al. (2019). Predicting late-onset Alzheimer’s disease from genomic data using deep neural networks. BioRxiv, 629402. https://doi.org/10.1101/629402 [7] Bycroft et al. "The UK Biobank resource with deep phenotyping and genomic data." Nature 562.7726 (2018): 203. [8] Petersen et al. "Alzheimer's disease neuroimaging initiative (ADNI): clinical characterization." Neurology 74.3 (2010): 201-209. |
| Additional Investigators |
