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  • ACTIVE INVESTIGATIONS Search for current projects using the investigator's name, institution, or keywords.
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Active Investigations

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: Yunjia Cao
Institution: Capital Medical University
Department: public health
Country:
Proposed Analysis: Purpose: 1. Fully describe the endophenotypes of Alzheimer’s disease(AD). 2. Explore the relationships among genes, MRI or PET images of AD and AD’s biomarkers. 3. Construct a more accurate diagnosis or prediction model. Data collection: Genomics/SNPs, clinical information, biomarkers, neuropsychological assessments, MRI and PET images. Data analysis: 1. Using genomic data describes the endophenotypes of AD, and relating the endophenotypes with image data. 2. Find the correlations among genes, image data, neuropsychological assessments and biomarkers. 3. Building more accurate prediction models using deep learning algorithms based on multi-dimension data.
Additional Investigators  
Investigator's Name: Jiajia Wang
Proposed Analysis: Purpose: 1. Fully describe the endophenotypes of Alzheimer’s disease(AD). 2. Explore the relationships among genes, MRI or PET images of AD and AD’s biomarkers. 3. Construct a more accurate diagnosis or prediction model. Data collection: Genomics/SNPs, clinical information, biomarkers, neuropsychological assessments, MRI and PET images. Data analysis: 1. Using genomic data describes the endophenotypes of AD, and relating the endophenotypes with image data. 2. Find the correlations among genes, image data, neuropsychological assessments and biomarkers. 3. Building more accurate prediction models using deep learning algorithms based on multi-dimension data.
Investigator's Name: Rui Chen
Proposed Analysis: Purpose: 1. Fully describe the endophenotypes of Alzheimer’s disease(AD). 2. Explore the relationships among genes, MRI or PET images of AD and AD’s biomarkers. 3. Construct a more accurate diagnosis or prediction model. Data collection: Genomics/SNPs, clinical information, biomarkers, neuropsychological assessments, MRI and PET images. Data analysis: 1. Using genomic data describes the endophenotypes of AD, and relating the endophenotypes with image data. 2. Find the correlations among genes, image data, neuropsychological assessments and biomarkers. 3. Building more accurate prediction models using deep learning algorithms based on multi-dimension data.