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: | Jing Cui |
Institution: | GSK |
Department: | Human Genetics |
Country: | |
Proposed Analysis: | Motivation Alzheimer disease (AD) is a heterogeneous disease. With the new definition of AD as a biological disease, biomarkers are extremely valuable in identification of AD associated genetic variants. Objectives We aim to add neuroimaging analysis to integrate pathology correlated information to identify AD associated variants. Longitudinal analysis of biomarkers derived from neuroimaging techniques can reveal AD progression. Identifying disease progression associated genetic variants can bring light to casual genes and eliminate variants associating to dementia due to other diseases. Pathologically defined AD subtypes have been defined and studied. Regarding variation in the clinical presentation, age at onset, disease duration and rate of cognitive decline, identifying different genetic basis across subtypes is a step forward towards subtype-specific therapies. Hypothesis We hypotheses that some GWAS AD variants correlate to regional structural change over time. We hypotheses that some variants correlate to AD subtypes stratified using MRI data and tau-PET data. |
Additional Investigators | |
Investigator's Name: | Dave Pulford |
Proposed Analysis: | Motivation Alzheimer disease (AD) is a heterogeneous disease. With the new definition of AD as a biological disease, biomarkers are extremely valuable in identification of AD associated genetic variants. Objectives We aim to add neuroimaging analysis to integrate pathology correlated information to identify AD associated variants. Longitudinal analysis of biomarkers derived from neuroimaging techniques can reveal AD progression. Identifying disease progression associated genetic variants can bring light to casual genes and eliminate variants associating to dementia due to other diseases. Pathologically defined AD subtypes have been defined and studied. Regarding variation in the clinical presentation, age at onset, disease duration and rate of cognitive decline, identifying different genetic basis across subtypes is a step forward towards subtype-specific therapies. Hypothesis We hypotheses that some GWAS AD variants correlate to regional structural change over time. We hypotheses that some variants correlate to AD subtypes stratified using MRI data and tau-PET data. |
Investigator's Name: | Chun-Fang Xu |
Proposed Analysis: | Motivation Alzheimer disease (AD) is a heterogeneous disease. With the new definition of AD as a biological disease, biomarkers are extremely valuable in identification of AD associated genetic variants. Objectives We aim to add neuroimaging analysis to integrate pathology correlated information to identify AD associated variants. Longitudinal analysis of biomarkers derived from neuroimaging techniques can reveal AD progression. Identifying disease progression associated genetic variants can bring light to casual genes and eliminate variants associating to dementia due to other diseases. Pathologically defined AD subtypes have been defined and studied. Regarding variation in the clinical presentation, age at onset, disease duration and rate of cognitive decline, identifying different genetic basis across subtypes is a step forward towards subtype-specific therapies. Hypothesis We hypotheses that some GWAS AD variants correlate to regional structural change over time. We hypotheses that some variants correlate to AD subtypes stratified using MRI data and tau-PET data. |
Investigator's Name: | Daniel Seaton |
Proposed Analysis: | Motivation Alzheimer disease (AD) is a heterogeneous disease. With the new definition of AD as a biological disease, biomarkers are extremely valuable in identification of AD associated genetic variants. Objectives We aim to add neuroimaging analysis to integrate pathology correlated information to identify AD associated variants. Longitudinal analysis of biomarkers derived from neuroimaging techniques can reveal AD progression. Identifying disease progression associated genetic variants can bring light to casual genes and eliminate variants associating to dementia due to other diseases. Pathologically defined AD subtypes have been defined and studied. Regarding variation in the clinical presentation, age at onset, disease duration and rate of cognitive decline, identifying different genetic basis across subtypes is a step forward towards subtype-specific therapies. Hypothesis We hypotheses that some GWAS AD variants correlate to regional structural change over time. We hypotheses that some variants correlate to AD subtypes stratified using MRI data and tau-PET data. |
Investigator's Name: | John Eicher |
Proposed Analysis: | Motivation Alzheimer disease (AD) is a heterogeneous disease. With the new definition of AD as a biological disease, biomarkers are extremely valuable in identification of AD associated genetic variants. Objectives We aim to add neuroimaging analysis to integrate pathology correlated information to identify AD associated variants. Longitudinal analysis of biomarkers derived from neuroimaging techniques can reveal AD progression. Identifying disease progression associated genetic variants can bring light to casual genes and eliminate variants associating to dementia due to other diseases. Pathologically defined AD subtypes have been defined and studied. Regarding variation in the clinical presentation, age at onset, disease duration and rate of cognitive decline, identifying different genetic basis across subtypes is a step forward towards subtype-specific therapies. Hypothesis We hypotheses that some GWAS AD variants correlate to regional structural change over time. We hypotheses that some variants correlate to AD subtypes stratified using MRI data and tau-PET data. |