Alzheimer's disease (AD) is one of the greatest public health challenges in the United States. There is no cure for AD, but pharmaceutical companies and academia are investigating several disease-modifying medicines that target early stages of AD neuropathology, before the damage to the brain is irreparable. However, this re- search is impeded by the enormous costs of conducting AD clinical trials. These costs are high because it is difficult to identify individuals who have early symptomatic or presymptomatic AD, as well as because AD develops slowly and it takes a very long time to discover whether a treatment is effective. The goal of this project is to develop novel neuroimaging biomarkers that can serve as surrogate measures of brain degeneration in AD. This study will build on the success of the NIH/NIA Alzheimer The specific aims of this project are (1) to build a detailed three-dimensional computational atlas of the human hippocampus and entorhinal cortex using a combination of ultra high-resolution 9.4 Tesla MRI of autopsy tissue samples and histology;(2) to develop algorithms and software that would leverage this atlas for automatic detection of the subfields of the hippocampus and entorhinal cortex in in vivo MRI acquired with a T2-weighted protocol that targets this region;(3) to compare the effectiveness of 3 Tesla and 7 Tesla MRI scanners for imaging the medial temporal lobe and deriving biomarkers;and (4) to assess the sensitivity and specificity of the novel biomarkers for progression detection and cohort stratification in AD using imaging data from healthy elderly, individuals with mild cognitive impairment, AD patients, and patients with frontotemporal dementia.

Public Health Relevance

Alzheimer's disease is one of the greatest challenges public health challenges in the United States. This project aims to reduce the cost of conducting a clinical trial in Alzheimer's disease by making it easier to detect the effects of treatment in a relatively short time window and with fewer participants. If successful, this project may result in more clinical trials being launched and a greater likelihood of finding a cure for Alzheimer's disease.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project (R01)
Project #
Application #
Study Section
Neurotechnology Study Section (NT)
Program Officer
Hsiao, John
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pennsylvania
Schools of Medicine
United States
Zip Code
Adler, Daniel H; Pluta, John; Kadivar, Salmon et al. (2014) Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI. Neuroimage 84:505-23
Pouch, A M; Wang, H; Takabe, M et al. (2014) Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling. Med Image Anal 18:118-29
Das, Sandhitsu R; Pluta, John; Mancuso, Lauren et al. (2013) Increased functional connectivity within medial temporal lobe in mild cognitive impairment. Hippocampus 23:1-6
Das, Sandhitsu R; Avants, Brian B; Pluta, John et al. (2012) Measuring longitudinal change in the hippocampal formation from in vivo high-resolution T2-weighted MRI. Neuroimage 60:1266-79
Pluta, John; Yushkevich, Paul; Das, Sandhitsu et al. (2012) In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI. J Alzheimers Dis 31:85-99
Wang, Hongzhi; Suh, Jung Wook; Pluta, John et al. (2011) Optimal weights for multi-atlas label fusion. Inf Process Med Imaging 22:73-84
Wang, Hongzhi; Das, Sandhitsu R; Suh, Jung Wook et al. (2011) A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation. Neuroimage 55:968-85
Yushkevich, Paul A; Wang, Hongzhi; Pluta, John et al. (2010) Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI. Neuroimage 53:1208-24