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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: Chen Yang
Institution: Harvard University
Department: Department of Biostatistics
Country:
Proposed Analysis: We are doing an AD prediction project for the course of Machine Learning for Healthcare, MIT. This is the paper we want to follow, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159475/. Construction of MRI-Based Alzheimer’s Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset. This project is totally for study purposes and de-identification private.
Additional Investigators  
Investigator's Name: Guangze Luo
Proposed Analysis: We are teammates for the project. So we have the same goal to do AD prediction following the paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159475/ to Construction of MRI-Based Alzheimer’s Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset
Investigator's Name: Tony Ding
Proposed Analysis: We are teammates for the project. So we have the same goal to do AD prediction following the paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159475/ to Construction of MRI-Based Alzheimer’s Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset
Investigator's Name: Mathilde Tans
Proposed Analysis: We are teammates for the project. So we have the same goal to do AD prediction following the paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159475/ to Construction of MRI-Based Alzheimer’s Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset