Abstract

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a $60 million, multi-site, longitudinal, prospective, naturalistic study of individuals with normal cognitive aging, mild cognitive impairment (MCI), and early Alzheimer's disease (AD) to facilitate the scientific evaluation of neuroimaging and other markers for the onset and progression of MCI and AD. Tests of cognitive functioning provide the benchmark for comparison in ADNI. A major limitation of the ADNI project is that modern psychometric analyses are not applied. Our group of investigators from the University of Washington, the University of California Davis, Washington University in St. Louis, and the Hebrew Rehabilitation Center for the Aged in Boston intend to address this limitation to determine those methods best able to detect and measure important changes over time. We will combine analyses of ADNI data with analyses of data from the Subcortical Ischemic Vascular Disease Program Project Grant (Helena Chui, PI) to pursue the following specific aims:
Aim 1. Develop strategies for measuring change over time in subjects with normal aging, MCI, and AD using modern psychometric methods applied to neuropsychological data from ADNI and SIVD PPG.
Aim 2. Evaluate modern psychometric methods for measuring change over time, and compare with conventional analyses using simulated and real data.
Aim 3. Develop and evaluate strategies for combining neuropsychological, neuroimaging, and biomarker data into composite outcomes for randomized controlled trials (RCTs) of interventions to prevent and treat MCI and AD.
Aim 4. Develop clinically useful score reports for the Uniform Data Set (UDS) to summarize general cognition, memory, and executive functioning, accounting for DIP and measurement error. We will test several hypotheses and hope to illustrate statistical techniques for optimizing the robustness of the psychometric data that will be collected in future clinical trials, whether alone or in conjunction with imaging.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG029672-04
Application #
7907812
Study Section
Adult Psychopathology and Disorders of Aging Study Section (APDA)
Program Officer
Hsiao, John
Project Start
2007-09-15
Project End
2012-08-31
Budget Start
2010-09-01
Budget End
2011-08-31
Support Year
4
Fiscal Year
2010
Total Cost
$557,232
Indirect Cost

  Institution

Name
University of Washington
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195

  Publications

Crane, Paul K; Trittschuh, Emily; Mukherjee, Shubhabrata et al. (2017) Incidence of cognitively defined late-onset Alzheimer's dementia subgroups from a prospective cohort study. Alzheimers Dement 13:1307-1316
Erosheva, Elena A; Curtis, S McKay (2017) Dealing with Reflection Invariance in Bayesian Factor Analysis. Psychometrika 82:295-307
Dadar, Mahsa; Maranzano, Josefina; Misquitta, Karen et al. (2017) Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging. Neuroimage 157:233-249
Deters, Kacie D; Nho, Kwangsik; Risacher, Shannon L et al. (2017) Genome-wide association study of language performance in Alzheimer's disease. Brain Lang 172:22-29
Dmitrieva, Natalia O; Fyffe, Denise; Mukherjee, Shubhabrata et al. (2015) Demographic characteristics do not decrease the utility of depressive symptoms assessments: examining the practical impact of item bias in four heterogeneous samples of older adults. Int J Geriatr Psychiatry 30:88-96
Luo, Jingqin; D'Angela, Gina; Gao, Feng et al. (2015) Bivariate correlation coefficients in family-type clustered studies. Biom J 57:1084-109
Fieo, Robert; Mukherjee, Shubhabrata; Dmitrieva, Natalia O et al. (2015) Differential item functioning due to cognitive status does not impact depressive symptom measures in four heterogeneous samples of older adults. Int J Geriatr Psychiatry 30:911-8
Mukherjee, Shubhabrata; Kim, Sungeun; Ramanan, Vijay K et al. (2014) Gene-based GWAS and biological pathway analysis of the resilience of executive functioning. Brain Imaging Behav 8:110-8
Xiong, Chengjie; Luo, Jingqin; Gao, Feng et al. (2014) Optimizing parameters in clinical trials with a randomized start or withdrawal design. Comput Stat Data Anal 69:101-113
Dong, Tuochuan; Kang, Le; Hutson, Alan et al. (2014) Confidence interval estimation of the difference between two sensitivities to the early disease stage. Biom J 56:270-86

Showing the most recent 10 out of 44 publications