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: | John John |
| Institution: | National Institute of Mental Health and Neurosciences |
| Department: | Department of Psychiatry |
| Country: | |
| Proposed Analysis: | Multiple direct and indirect factors contribute significantly to healthy and pathological ageing. The risk factors affecting pathological ageing can be broadly classified into modifiable (cardiovascular disease, physical activity, sensory impairment, cognitive reserve, social engagement) and non-modifiable (age, gender, APOE ε4 genotype, occupation) risk factors. The main objective is to identify the role of modifiable and non-modifiable risk/protective factors, cognition, latent factors in late middle-aged participants, and characterize it using neuroimaging techniques. Several available modeling methods such as multivariable linear regression, multi modal bayesian modeling and statistical factor modeling (Sun et al., 2019; Neth et al., 2020) can be performed to examine the independent or synergistic contributions of these factors. We thus aim to analyze clinical predictors and thereby Identify the contributing factors for pathological ageing in Alzheimer’s disease. References Neth, B. J. et al. (2020) ‘Relationship Between Risk Factors and Brain Reserve in Late Middle Age: Implications for Cognitive Aging’, Frontiers in Aging Neuroscience, 11. doi: 10.3389/fnagi.2019.00355. Sun, N. et al. (2019) ‘Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Alzheimer’s disease’, NeuroImage, 201, p. 116043. doi: 10.1016/j.neuroimage.2019.116043. |
| Additional Investigators | |
| Investigator's Name: | Himanshu Joshi |
| Proposed Analysis: | Multiple direct and indirect factors contribute significantly to healthy and pathological ageing. The risk factors affecting pathological ageing can be broadly classified into modifiable (cardiovascular disease, physical activity, sensory impairment, cognitive reserve, social engagement) and non-modifiable (age, gender, APOE ε4 genotype, occupation) risk factors. The main objective is to identify the role of modifiable and non-modifiable risk/protective factors, cognition, latent factors in late middle-aged participants, and characterize it using neuroimaging techniques. Several available modeling methods such as multivariable linear regression, multi modal bayesian modeling and statistical factor modeling (Sun et al., 2019; Neth et al., 2020) can be performed to examine the independent or synergistic contributions of these factors. We thus aim to analyze clinical predictors and thereby Identify the contributing factors for pathological ageing in Alzheimer’s disease. References Neth, B. J. et al. (2020) ‘Relationship Between Risk Factors and Brain Reserve in Late Middle Age: Implications for Cognitive Aging’, Frontiers in Aging Neuroscience, 11. doi: 10.3389/fnagi.2019.00355. Sun, N. et al. (2019) ‘Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Alzheimer’s disease’, NeuroImage, 201, p. 116043. doi: 10.1016/j.neuroimage.2019.116043. |
| Investigator's Name: | Setu Havanur |
| Proposed Analysis: | Multiple direct and indirect factors contribute significantly to healthy and pathological ageing. The risk factors affecting pathological ageing can be broadly classified into modifiable (cardiovascular disease, physical activity, sensory impairment, cognitive reserve, social engagement) and non-modifiable (age, gender, APOE ε4 genotype, occupation) risk factors. The main objective is to identify the role of modifiable and non-modifiable risk/protective factors, cognition, latent factors in late middle-aged participants, and characterize it using neuroimaging techniques. Several available modeling methods such as multivariable linear regression, multi modal bayesian modeling and statistical factor modeling (Sun et al., 2019; Neth et al., 2020) can be performed to examine the independent or synergistic contributions of these factors. We thus aim to analyze clinical predictors and thereby Identify the contributing factors for pathological ageing in Alzheimer’s disease. References Neth, B. J. et al. (2020) ‘Relationship Between Risk Factors and Brain Reserve in Late Middle Age: Implications for Cognitive Aging’, Frontiers in Aging Neuroscience, 11. doi: 10.3389/fnagi.2019.00355. Sun, N. et al. (2019) ‘Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Alzheimer’s disease’, NeuroImage, 201, p. 116043. doi: 10.1016/j.neuroimage.2019.116043. |
| Investigator's Name: | Ashika Roy |
| Proposed Analysis: | Multiple direct and indirect factors contribute significantly to healthy and pathological ageing. The risk factors affecting pathological ageing can be broadly classified into modifiable (cardiovascular disease, physical activity, sensory impairment, cognitive reserve, social engagement) and non-modifiable (age, gender, APOE ε4 genotype, occupation) risk factors. The main objective is to identify the role of modifiable and non-modifiable risk/protective factors, cognition, latent factors in late middle-aged participants, and characterize it using neuroimaging techniques. Several available modeling methods such as multivariable linear regression, multi modal bayesian modeling and statistical factor modeling (Sun et al., 2019; Neth et al., 2020) can be performed to examine the independent or synergistic contributions of these factors. We thus aim to analyze clinical predictors and thereby Identify the contributing factors for pathological ageing in Alzheimer’s disease. References Neth, B. J. et al. (2020) ‘Relationship Between Risk Factors and Brain Reserve in Late Middle Age: Implications for Cognitive Aging’, Frontiers in Aging Neuroscience, 11. doi: 10.3389/fnagi.2019.00355. Sun, N. et al. (2019) ‘Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Alzheimer’s disease’, NeuroImage, 201, p. 116043. doi: 10.1016/j.neuroimage.2019.116043. |
