×
  • Select the area you would like to search.
  • ACTIVE INVESTIGATIONS Search for current projects using the investigator's name, institution, or keywords.
  • EXPERTS KNOWLEDGE BASE Enter keywords to search a list of questions and answers received and processed by the ADNI team.
  • ADNI PDFS Search any ADNI publication pdf by author, keyword, or PMID. Use an asterisk only to view all pdfs.
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: 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: 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: 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.