Recently major efforts in Alzheimer's disease (AD) research have concentrated on the search for disease-associated biomarkers that can identify people at risk for developing AD, help with early diagnosis, prognosis or therapeutic decision-making, and allow for an expedited evaluation of novel disease-modifying therapies. The current project will use an advanced automated hippocampal segmentation approach based on an adaptive machine-learning algorithm paired with the hippocampal radial atrophy mapping technique to establish the dynamic pattern of hippocampal changes that occur in Alzheimer's disease in 3D. AdaBoost is a widely recognized breakthrough computer vision approach that shows great promise as an objective, reproducible and reliable automated hippocampal segmentation technique. While implementing cutting edge mathematical and statistical concepts, the combined technique provides high throughput and sensitivity to disease-associated hippocampal changes. The main goal of this project is to test the performance, variability, robustness and reproducibility of the approach in very large 1.5T and 3T epidemiological and 1.5T clinical trial datasets and to ultimately establish the AdaBoost/radial atrophy methodology as a potential secondary/surrogate outcome measure for clinical trials in AD and MCI that will overcome many of the limitations imposed by the widely used cognitive batteries such as ceiling and floor effects, fluctuations and learning and practice effects. In addition we will identify the hippocampal morphological changes associated with imminent conversion from MCI to AD and investigate potential cognitive and laboratory biomarker correlations with hippocampal atrophy thus investigating potential disease biosignatures.

Public Health Relevance

If no cure is found, AD will affect as many as 10-16 million Americans by year 2050. Recently major efforts in AD research have concentrated on the search for biomarkers that can identify people at risk as early as possible and assess promising disease-modifying therapies presently being evaluated. The proposed research will further validate and establish a state-of-the-art high throughput automated imaging biomarker for use in epidemiological and clinical trial research.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Specialized Center (P50)
Project #
5P50AG016570-14
Application #
8450829
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4)
Project Start
Project End
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
14
Fiscal Year
2013
Total Cost
$153,207
Indirect Cost
$31,613
Name
University of California Los Angeles
Department
Type
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Ting, Simon Kang Seng; Foo, Heidi; Chia, Pei Shi et al. (2018) Dyslexic Characteristics of Chinese-Speaking Semantic Variant of Primary Progressive Aphasia. J Neuropsychiatry Clin Neurosci 30:31-37
Petyuk, Vladislav A; Chang, Rui; Ramirez-Restrepo, Manuel et al. (2018) The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target. Brain 141:2721-2739
Burke, Shanna L; Cadet, Tamara; Maddux, Marlaina (2018) Chronic Health Illnesses as Predictors of Mild Cognitive Impairment Among African American Older Adults. J Natl Med Assoc 110:314-325
Cruchaga, Carlos; Del-Aguila, Jorge L; Saef, Benjamin et al. (2018) Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms. Alzheimers Dement 14:205-214
Joe, Elizabeth; Medina, Luis D; Ringman, John M et al. (2018) 1H MRS spectroscopy in preclinical autosomal dominant Alzheimer disease. Brain Imaging Behav :
Burke, Shanna L; Maramaldi, Peter; Cadet, Tamara et al. (2018) Decreasing hazards of Alzheimer's disease with the use of antidepressants: mitigating the risk of depression and apolipoprotein E. Int J Geriatr Psychiatry 33:200-211
Qian, Winnie; Fischer, Corinne E; Schweizer, Tom A et al. (2018) Association Between Psychosis Phenotype and APOE Genotype on the Clinical Profiles of Alzheimer's Disease. Curr Alzheimer Res 15:187-194
Burke, Shanna L; Hu, Tianyan; Fava, Nicole M et al. (2018) Sex differences in the development of mild cognitive impairment and probable Alzheimer's disease as predicted by hippocampal volume or white matter hyperintensities. J Women Aging :1-25
Wang, Qi; Guo, Lei; Thompson, Paul M et al. (2018) The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 64:149-169
Wang, Tingyan; Qiu, Robin G; Yu, Ming (2018) Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks. Sci Rep 8:9161

Showing the most recent 10 out of 727 publications