Alzheimer's disease (AD) affects a total of 5.3 million individuals in the U.S. alone, making it the 7th leading cause of death and also costing about 172 billion dollars annually. Currently, AD diagnosis is predominantly based on clinical and psychometric assessment. However, diagnosis can only be certain if an autopsy reports the presence of characteristic neuritic ?-amyloid plaques and neurofibrilatory tangles in specific brain regions in an individual with a history of progressive dementia. Thus, there is a significant unmet need for non-invasive objective diagnosis and quantification of pathologies, as well as general assessment of disease progression. The goal of this project is to develop a novel neuroimaging analysis framework that will harness the complementary information from different imaging modalities for effective quantification of disease -induced pathologies, so as to promote early detection for possible treatment and prophylaxis. Achieving this goal requires significant innovation in neuroimage analysis techniques to detect sophisticated yet subtle brain alteration patterns. Accordingly, the specific aims of this project are (Aim 1: Disease Diagnosis) to develop a multimodality multivariate diagnosis technique for accurate identification of individuals who are at risk for AD, (Aim 2: Progress Monitoring) to design a novel multi-task kernel learning framework for prediction and quantification of brain abnormality at various disease stages, and (Aim 3: Evaluation) to assess the developed methods using a large database of elderly subjects, for their diagnostic power in quantifying brain alteration patterns in AD/MCI patients, their predictive power of MCI patients who are at risk for AD, and also their capability in quantifying abnormalities as the disease progresses. We expect, upon successful completion of this project, that the resulting comprehensive, integrated, and effective diagnosis/monitoring framework will be conducive to improving the success of early detection of MCI/AD, as well as other neurological disorders including schizophrenia, autism, and multiple sclerosis. Public Health Relevance Statement: Prior to the appearance of clinical symptomatology, AD undergoes a prodromal phase, lasting from years to decades, with disease pathology or predisposition that is clinically undetectable or uncertain. Thus, identifying individuals who are t risk for AD is critical if disease-modifying treatments are to be effective. For this reason, the neuroimage analysis techniques developed in this project are significantly relevant to public health in that they will help improve accuracy in patient identification and disease monitoring for effective treatment.

Public Health Relevance

Description of Project This project aims to develop an individual-based diagnosis method for early detection and progression monitoring of brain disease by using multimodality imaging and non-imaging data. This is significantly different from the conventional methods that focus on group comparison of brain disease using a single imaging modality or simple combination of multimodality data. These group comparison methods are not able to diagnose and predict brain disease for an individual patient, although they may help identify the effect of disease on brain structures and functions at a group level.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG041721-03
Application #
8688869
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Hsiao, John
Project Start
2012-08-01
Project End
2015-05-31
Budget Start
2014-07-01
Budget End
2015-05-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Li, Guannan; Liu, Mingxia; Sun, Quansen et al. (2018) Early Diagnosis of Autism Disease by Multi-channel CNNs. Mach Learn Med Imaging 11046:303-309
Jie, Biao; Liu, Mingxia; Shen, Dinggang (2018) Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease. Med Image Anal 47:81-94
Liu, Mingxia; Gao, Yue; Yap, Pew-Thian et al. (2018) Multi-Hypergraph Learning for Incomplete Multimodality Data. IEEE J Biomed Health Inform 22:1197-1208
Zhang, Yongqin; Shi, Feng; Cheng, Jian et al. (2018) Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images. IEEE Trans Cybern :
Lian, Chunfeng; Liu, Mingxia; Zhang, Jun et al. (2018) Automatic Segmentation of 3D Perivascular Spaces in 7T MR Images Using Multi-Channel Fully Convolutional Network. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson M 2018:
Liu, Mingxia; Zhang, Jun; Adeli, Ehsan et al. (2018) Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43:157-168
Ding, Zhongxiang; Zhang, Han; Lv, Xiao-Fei et al. (2018) Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction. Hum Brain Mapp 39:407-427
Nie, Dong; Wang, Li; Adeli, Ehsan et al. (2018) 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation. IEEE Trans Cybern :
Zhang, Changqing; Adeli, Ehsan; Zhou, Tao et al. (2018) Multi-Layer Multi-View Classification for Alzheimer's Disease Diagnosis. Proc Conf AAAI Artif Intell 2018:4406-4413
Wang, Li; Li, Gang; Adeli, Ehsan et al. (2018) Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism. Hum Brain Mapp 39:2609-2623

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