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: | Andreana Haley |
| Institution: | The University of Texas at Autin |
| Department: | Psychology |
| Country: | |
| Proposed Analysis: | Individual differences in within-person change in cognitive function over time (executive function, memory, and language composite z scores provided by ADNI) and effects of baseline predictors (MCI diagnosis & Body Mass Index, BMI) on within-person trajectories of cognitive decline will be modeled using Latent Growth Curve Modeling (LGM) implemented in MPlus version 7.4. In addition, we will test if the effects of a baseline predictor (BMI) on cognitive decline are global (using a common pathway) or specific (impacting memory, executive function and language differentially). Model fit will be examined utilizing three fit indexes: Chi^2 test of model fit, root mean square error of approximation (RMSEA), and comparative fit index (CFI). Good fit will be defined as Chi^2 not significantly different than the fully saturated model, RMSEA < 0.05, and CFI > 0.9. RMSEA values between 0.05 and 0.08 will be considered adequate fit. Missing data will be accounted for using robust full-information maximum likelihood method. |
| Additional Investigators | |
| Investigator's Name: | Alexandra Clark |
| Proposed Analysis: | INDIVIDUAL DIFFERENCES IN WITHIN-PERSON CHANGE IN COGNITIVE FUNCTION OVER TIME (EXECUTIVE FUNCTION, MEMORY, AND LANGUAGE COMPOSITE Z SCORES PROVIDED BY ADNI) AND EFFECTS OF BASELINE PREDICTORS (MCI DIAGNOSIS & BODY MASS INDEX, BMI) ON WITHIN-PERSON TRAJECTORIES OF COGNITIVE DECLINE WILL BE MODELED USING LATEnT GROWTH CURVE MODELINg (LGM) IMPLEMENTED IN MPLUS VERSION 7.4. IN ADDITION, WE WILL TEST IF THE EFFECTS OF A BASELINE PREDICTOR (BMI) ON COGNITIVE DECLINE ARE GLOBAL (USING A COMMON PATHWAY) OR SPECIFIC (IMPACTING MEMORY, EXECUTIVE FUNCTION AND LANGUAGE DIFFERENTIALLY). MODEL FIT WILL BE EXAMINED UTILIZING THREE FIT INDEXES: CHI^2 TEST OF MODEL FIT, ROOT MEAN SQUARE ERROR OF APPROXIMATION (RMSEA), AND COMPARATIVE FIT INDEX (CF|). GOOD FIT WILL BE DEFINED AS CHI^2 NOT SIGNIFICANTLY DIFFERENT THAN THE FULLY SATURATED MODEL, RMSEA < 0.05, AND CFI > 0.9. RMSEA VALUES BETWEEN 0.05 AND 0.08 WILL BE CONSIDERED ADEQUATE FIT. MISSING DATA WILL BE ACCOUNTED FOR USING ROBUST FULL-INFORMATION MAXIMUM LIKELIHOOD METHOD. |
| Investigator's Name: | Audrey Duarte |
| Proposed Analysis: | INDIVIDUAL DIFFERENCES IN WITHIN-PERSON CHANGE IN COGNITIVE FUNCTION OVER TIME (EXECUTIVE FUNCTION, MEMORY, AND LANGUAGE COMPOSITE Z SCORES PROVIDED BY ADNI) AND EFFECTS OF BASELINE PREDICTORS (MCI DIAGNOSIS & BODY MASS INDEX, BMI) ON WITHIN-PERSON TRAJECTORIES OF COGNITIVE DECLINE WILL BE MODELED USING LATEnT GROWTH CURVE MODELINg (LGM) IMPLEMENTED IN MPLUS VERSION 7.4. IN ADDITION, WE WILL TEST IF THE EFFECTS OF A BASELINE PREDICTOR (BMI) ON COGNITIVE DECLINE ARE GLOBAL (USING A COMMON PATHWAY) OR SPECIFIC (IMPACTING MEMORY, EXECUTIVE FUNCTION AND LANGUAGE DIFFERENTIALLY). MODEL FIT WILL BE EXAMINED UTILIZING THREE FIT INDEXES: CHI^2 TEST OF MODEL FIT, ROOT MEAN SQUARE ERROR OF APPROXIMATION (RMSEA), AND COMPARATIVE FIT INDEX (CF|). GOOD FIT WILL BE DEFINED AS CHI^2 NOT SIGNIFICANTLY DIFFERENT THAN THE FULLY SATURATED MODEL, RMSEA < 0.05, AND CFI > 0.9. RMSEA VALUES BETWEEN 0.05 AND 0.08 WILL BE CONSIDERED ADEQUATE FIT. MISSING DATA WILL BE ACCOUNTED FOR USING ROBUST FULL-INFORMATION MAXIMUM LIKELIHOOD METHOD. |
