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Principal Investigator  
Principal Investigator's Name: Christoforos Hadjichrysanthou
Institution: Imperial College London
Department: Department of Infectious Disease Epidemiology
Country:
Proposed Analysis: ORIGINAL Imaging data (e.g. Hippocampal and Whole brain volume) will be of primary interest in this research. More specifically, it will be useful to review and analyse the actual brain scan images to better understand how volumes in the ADNI database are calculated and to assess whether associations with other biomarkers (e.g. AB42, t-tau and p-tau) are present. UPDATED Preliminary work has included the mathematical modelling of disease progression (from CN, through MCI to AD) as a function of a number of covariates including, age, gender, education and APOE status, as presented in Hadjichrysanthou at al. (2018). The mathematical model that was developed was then used within a stochastic simulation framework to simulate disease progression over time. Future work in this area will aim to expand upon this concept by considering more refined disease states and by simulating biomarker levels (e.g. CSF A-beta and total-tau) instead of discrete clinical states. Other work has considered the minimum detectable effect size (MDES) in longitudinal studies of AD across a range of potential endpoints, as presented in Evans et al. (2018). Longitudinal data within ADNI was utilised to quantify the variability of these markers over time. Future work may consider novel potential targets identified in the literature, for example, levels of neurofilament light (NfL). Ongoing work in other areas includes the analysis of structural magnetic response imaging data over time, the variability of cognitive scores at the item level (e.g. response rates to individual question in the MMSE) over time as well as the mathematical and statistical modelling of the temporal dynamics of biological and cognitive markers that are associated to AD, at both the population and individual level (Hadjichrysanthou et al. (2020)). The mathematical and statistical models will be used within a stochastic simulation framework to simulate disease progression over time and assess different clinical trial designs. UPDATED Ongoing work involving the clinical diagnoses over time has provided insights into the occurrence of reversions (or back-transitions) from MCI and AD, as presented in Hadjichrysanthou et al. (2018), as a function of a number of covariates. Focus going forward is now shifting to pre-clinical samples of individuals within ADNI. Further interest into the biomarkers of disease has led to insights into the dynamic nature of markers over time. In particular, how rates of change in hippocampal volume (HV) over time helps explain disease progression more than absolute values of HV on their own, as presented in McRae-McKee et al. (2019). Further research going forward will involve the consideration of longitudinal measures of non-invasive markers such as plasma NFL and others. UPDATED Ongoing work is being conducted to investigate the potential existence of a gray area (or area of uncertainty) around currently used threshold of amyloid positivity/negativity in clinical trials and as inclusion criteria into observational studies. The utility of a dichotomous threshold in selecting individuals for participation in such studies has many limitations and we aim to demonstrate that a more flexible approach is required. We have published a perspective piece in Alzheimer's and Dementia: Journal of the Alzheimer's Association on this subject and are in the process of developing a research article to follow this up. In addition to this, substantial work is being done relating to plasma NfL and the long-term trajectories of other biomarkers found in ADNI.
Additional Investigators