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: | Pierre Luisi |
| Institution: | Universidad Nacional de Córdoba |
| Department: | Instituto de Investigaciones Psicológicas |
| Country: | |
| Proposed Analysis: | The ADNI data will be used as the validation data set for analyses we aim at performing in the dataset described in Cochran et al. 2020. 1. Hypotheses: 1.a Performing genome-wide association studies (GWAS) on carefully defined endophenotypes for Alzheimer’s disease (AD) instead of the clinical output provide important insights into the genetic etiology of complex diseases. 1.b brain states measured through functional dynamical connectivity change with age and neurodegeneration": we hypothesise that on Alzheimer's several traits related to brain states will change: the number of occurring states, their variety, their duration in time and their transition probability. We will use this information as endophenotypes. While functional MRI tends to have a lower heritability than structural, they may complement each other as phenotypes. Furthermore, dynamical functional (i.e. brain states) remains unexplored making its study very interesting. 1.c Polygenic Risk scores from GWAS on endophenotypes may help to better discriminate at-risk individuals for AD. 2. Aims of the study: 2.a Obtain intermediate phenotypes from neuroimaging data. For that purpose, we will process Magnetic Resonance Imaging (MRI) both structural and functional. We will extract individual indices of brain structure and function (gray/white matter density, MRI; dynamical states of functional connectivity, fMRI). 2.b Perform different GWAS: for intermediate phenotypes and clinical output separately as well as joint (multi-phenotype) analyses. 2.c Study the genetic correlation among the different endophenotypes and clinical output to better decipher the physiological mechanisms underlying AD development. 2.d Estimate PRS for quantifying individual differences in age-specific genetic risk for AD leveraging summary statistics from the different GWAS performed at step 2 and compare their calbration/discrimination power. 3. Study description One of the biggest challenges in modern medicine is to develop tools for risk assessment and diagnosis of psychiatric conditions. Risk prediction is especially needed for those conditions in which an early diagnosis is key in the prognosis of the condition, such as AD . New integral approaches are urgently needed to develop tools that allow us a precise characterization of neurocognitive and genetic markers. Although physiological, neurocognitive and genetic markers have been proposed as potential markers of AD risk (Andrews 2020; Khoury 2019; Mantzavinos 2017), they have not been integrated into multimodal biomarker strategies During the awake resting state, spontaneous brain activity is highly structured. Functional magnetic resonance imaging (fMRI) recordings indicate that brain activity constantly waxes and wanes in a tightly correlated manner across distant brain regions, forming reproducible patterns of functional connectivity. Exploring these patterns have shown a far richer picture of the nature of human resting-state activity than previously thought. How these patterns change with age and disease, and their potential as an early biomarker of neurodegeneration, is still unclear. In this work we will develop risk scores for AD based on a Polygenic Risk Score (PRS). The construction of the PRS will start with new risk variant identification performing a GWAS for endophenotypes or the clinical output separately or jointly (Turley et al. 2018). The intermediate phenotypes will consist of a combination of neuroimaging estimates (Barttfeld 2015, Demertzi 2020). Once obtained size effects for each variant, they will be integrated into PRS to predict risk of AD. These PRS will be estimated from the previously mentioned GWAS and their calibration and discrimination power will be compared to evaluate which approach provides predicting power. We will also study the genetic correlation among the endophenotypes and with the clinical output following (Eliot et al. 2018). References Andrews, S. J. et al. 2020. The Lancet Neurology. Barttfeld, P. et al. 2015. PNAS. Demertzi 2019. Science Advances. Eliot et al. 2018. Nature Khoury, R. et al 2019. Biomarkers in Neuropsychiatry. Mantzavinos, V., et al. 2017. Current Alzheimer Research. Shriner, D. (2017). Current protocols in human genetic Turlery P. et al. 2018. Nature Genetics. Vilhjálmsson, B. J. et al. 2015. The american journal of human genetics. |
| Additional Investigators |
