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: | GORDON CHELUNE |
| Institution: | University of Utah |
| Department: | Neurology |
| Country: | |
| Proposed Analysis: | Our purpose is to establish enhanced demographically-adjusted cutoff-scores and Test Operating Characteristics (TOC: e.g., Sensitivity, Specificity, Likelihood Ratios) for the Montreal Cognitive Assessment (MoCA) based on recently published robust, demographic norms from the SPRINT-MIND trial (N=5338) derived using beta-binomial (BB) regression modeling. These norms will be applied to the observed MoCA scores of participants in the ADNI database and converted to standardized z-scores reflecting the degree to which an individual’s observed MoCA is above or below demographic expectations based on their Age, Education, Sex, and Race/Ethnicity. Logistic regression will be used to compare the standardized MoCA scores of ADNI participants classified as cognitively normal and those with Mild Cognitive Impairment (MCI) and Dementia to determine optimal clinical cutoff-scores. ROC curves will be computed to compare the diagnostic effectiveness of the demographically-adjusted versus unadjusted MoCA scores. Finally, the cutoff-scores will then be used in classification analyses to establish the clinical sensitivity of the demographically-adjusted MoCA scores and other TOC characteristics. |
| Additional Investigators | |
| Investigator's Name: | Dustin Hammers |
| Proposed Analysis: | Our purpose is to establish enhanced demographically-adjusted cutoff-scores and Test Operating Characteristics (TOC: e.g., Sensitivity, Specificity, Likelihood Ratios) for the Montreal Cognitive Assessment (MoCA) based on recently published robust, demographic norms from the SPRINT-MIND trial (N=5338) derived using beta-binomial (BB) regression modeling. These norms will be applied to the observed MoCA scores of participants in the ADNI database and converted to standardized z-scores reflecting the degree to which an individual’s observed MoCA is above or below demographic expectations based on their Age, Education, Sex, and Race/Ethnicity. Logistic regression will be used to compare the standardized MoCA scores of ADNI participants classified as cognitively normal and those with Mild Cognitive Impairment (MCI) and Dementia to determine optimal clinical cutoff-scores. ROC curves will be computed to compare the diagnostic effectiveness of the demographically-adjusted versus unadjusted MoCA scores. Finally, the cutoff-scores will then be used in classification analyses to establish the clinical sensitivity of the demographically-adjusted MoCA scores and other TOC characteristics. |
