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.
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.
|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|
|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|
|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|
|Wisse, L E M; Kuijf, H J; Honingh, A M et al. (2016) Automated Hippocampal Subfield Segmentation at 7T MRI. AJNR Am J Neuroradiol 37:1050-7|
|Yushkevich, Paul A; Wisse, Laura; Adler, Daniel et al. (2016) A framework for informing segmentation of in vivo MRI with information derived from ex vivo imaging: Application in the medial temporal lobe. Conf Proc IEEE Eng Med Biol Soc 2016:6014-6017|
|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|
|Xie, Long; Wisse, Laura E M; Das, Sandhitsu R et al. (2016) Accounting for the Confound of Meninges in Segmenting Entorhinal and Perirhinal Cortices in T1-Weighted MRI. Med Image Comput Comput Assist Interv 9901:564-571|
|Gertje, Eske Christiane; Pluta, John; Das, Sandhitsu et al. (2016) Clinical Application of Automatic Segmentation of Medial Temporal Lobe Subregions in Prodromal and Dementia-Level Alzheimer's Disease. J Alzheimers Dis 54:1027-1037|
|Yushkevich, Paul A; Amaral, Robert S C; Augustinack, Jean C et al. (2015) Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol. Neuroimage 111:526-41|
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