Person:
Hu, Chenhui

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Hu

First Name

Chenhui

Name

Hu, Chenhui

Filters

2015 - 20162

Settings

Author's Publications: search.filters.isAuthorOfPublication.099d9cd4-2040-4d9d-a0bd-1a781fab6134

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease
    (Public Library of Science, 2015) Hu, Chenhui; Cheng, Lin; Sepulcre, Jorge; Johnson, Keith; Fakhri, Georges E.; Lu, Yue; Li, Quanzheng
    Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer's disease (AD) measured by amyloid-β deposits. The proposed GRM regards 11C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and ℓ1-regularized partial correlation estimation. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.
  • No Thumbnail Available
    Publication
    Graph-Based Data Mining in Neuroimaging of Neurological Diseases
    (2016-05-02) Hu, Chenhui; Tarokh, Vahid; Lu, Yue M.; Sepulcre, Jorge; Li, Quanzheng
    The human brain is one of the most complex network systems. Understanding the organization of the brain is crucial for the diagnosis and treatment of brain disorders. Recent advances in neuroimaging enable a non-invasive in vivo observation of the structural and functional behaviors of the human brain. However, it is challenging to find meaningful structures from the high-dimensional imaging data and develop robust frameworks for the detection of brain diseases, especially Alzheimer's disease (AD). To tackle these challenges, we investigate brain imaging data from a network perspective in this dissertation. We first analyze a high-resolution brain network targeting the hippocampal area based on amyloid depositions. We improve the quality of the positron emission tomography (PET) images with a deblurring algorithm and segment the hippocampal area into subregions. We find that the network features clearly indicate the stages of AD. Then, we propose a spectral graph regression model (GRM) to learn brain network structures. The proposed GRM regards the imaging data as smooth signals on an unknown graph. By solving inverse problems, a more physiologically meaningful brain network is estimated in comparison with the state-of-the-art method. We also study the sample complexity of learning graphical models with block structures. Next, we develop a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. To model different real data, we present hypothesis tests by considering low-pass, variation bounded, and random signals on graphs. Real data evaluations demonstrate the effectiveness of the method. Moreover, we introduce an AD detection framework by exploiting deep learning and brain network structures. The classification accuracy of a targeted autoencoder network combing with network information is much higher than that of traditional approaches. Finally, we present a network diffusion model with sources to localize the origins of AD. By imposing a sparsity constraint on the number of sources, we solve the inverse problem efficiently. In addition, we precisely predict the changes of brain atrophy patterns through this model.