ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
|Principal Investigator's Name:||Steven Potkin|
|Institution:||University of California, Irvine|
|Department:||Psychiatry and Human Behavior|
|Country:||United States of America|
|Proposed Analysis:||Baseline MPRAGE files (at time T=1) coupled with neuropsychological examination scores can predict immediate or longitudinal structural volume loss (at time T=2). Previously, neuropsychological examinations such as the Mini Mental State Examination (MMSE), Clinical Dementia Rating (CDR), as well as the MMSE’s item scores (such as “Recall,” “Time orientation,” “Place Orientation,” and “Attention” sub scores) have been used to predict brain global and structural volume. Employing multivariate regression to generate an optimal volumetric algorithm, structural volume (such as the Hippocampal volume) or structural volume loss (atrophy rate) will be predicted over a duration of Magnetic Resonance scans. Specifically, baseline MPRAGE files coupled with secondary and/or tertiary MPRAGE files in addition to neuropsychological examinations (MMSE, MMSE items, CDR sum of boxes, ADAS…) and a subject’s age will be used to predict this future structural volume based upon the calculated atrophy rate. The requested MPRAGE files are categorized as “best” having undergone gradwarping, intensity correction, and N3 scaling and are requested for subjects categorized by Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and Age-Matched Controls. To conduct our longitudinal project, the baseline, secondary and tertiary scans (if available) will be required of the aforementioned subjects. The MPRAGE files will be processed through FreeSurfer to reconstruct the cortical surface with particular interest in subcortical segmentation. Yielding segmentation and volumetric statistics, multivariate regression analysis will be employed to generate an optimal algorithm estimation model for group comparison assessment.|