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.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG037376-05
Application #
8667386
Study Section
Neurotechnology Study Section (NT)
Program Officer
Hsiao, John
Project Start
2010-05-01
Project End
2015-04-30
Budget Start
2014-06-15
Budget End
2015-04-30
Support Year
5
Fiscal Year
2014
Total Cost
$424,371
Indirect Cost
$159,139
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Xie, Long; Das, Sandhitsu R; Wisse, Laura E M et al. (2018) Early Tau Burden Correlates with Higher Rate of Atrophy in Transentorhinal Cortex. J Alzheimers Dis 62:85-92
Adler, Daniel H; Wisse, Laura E M; Ittyerah, Ranjit et al. (2018) Characterizing the human hippocampus in aging and Alzheimer's disease using a computational atlas derived from ex vivo MRI and histology. Proc Natl Acad Sci U S A 115:4252-4257
Hadar, Peter N; Kini, Lohith G; Coto, Carlos et al. (2018) Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy. Neuroimage Clin 20:1139-1147
Das, Sandhitsu R; Xie, Long; Wisse, Laura E M et al. (2018) Longitudinal and cross-sectional structural magnetic resonance imaging correlates of AV-1451 uptake. Neurobiol Aging 66:49-58
Wisse, Laura E M; Daugherty, Ana M; Olsen, Rosanna K et al. (2017) A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals? Hippocampus 27:3-11
Xie, Long; Pluta, John B; Das, Sandhitsu R et al. (2017) Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability. Neuroimage 144:183-202
Wisse, L E M; Adler, D H; Ittyerah, R et al. (2017) Comparison of In Vivo and Ex Vivo MRI of the Human Hippocampal Formation in the Same Subjects. Cereb Cortex 27:5185-5196
Wolk, David A; Das, Sandhitsu R; Mueller, Susanne G et al. (2017) Medial temporal lobe subregional morphometry using high resolution MRI in Alzheimer's disease. Neurobiol Aging 49:204-213
Berron, D; Vieweg, P; Hochkeppler, A et al. (2017) A protocol for manual segmentation of medial temporal lobe subregions in 7 Tesla MRI. Neuroimage Clin 15:466-482
Yushkevich, Paul A; Yang Gao; Gerig, Guido (2016) ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. Conf Proc IEEE Eng Med Biol Soc 2016:3342-3345

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