ADNI | Active Investigations

Select the area you would like to search.
ADNI SITE
DATA DICTIONARY This search queries the ADNI data dictionary. Enter terms or codes used in the dictionary for a definition, or enter search terms to locate data within the archive.
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.

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:	Jianwei Zheng
Institution:	Chapman University
Department:	Math
Country:
Proposed Analysis:	Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease ; cluster-0 and cluster-3 represented mid-severity sub-populations; cluster-2 represented most severe disease.
Additional Investigators
Investigator's Name:	Cyril Rakovski
Proposed Analysis:	Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease ; cluster-0 and cluster-3 represented mid-severity sub-populations; cluster-2 represented most severe disease.