Exploring latent structures of Alzheimer's disease via structure learning | IEEE Conference Publication | IEEE Xplore

Exploring latent structures of Alzheimer's disease via structure learning


Abstract:

As a non-invasive brain imaging approach, functional magnetic resonance imaging (fMRI) acts an irreplaceable role when studying whole brain functional activities and dyna...Show More

Abstract:

As a non-invasive brain imaging approach, functional magnetic resonance imaging (fMRI) acts an irreplaceable role when studying whole brain functional activities and dynamics. Large-scale and collaborative effort in collecting fMRI data, especially in neurology and psychiatry, offers a new source of statistical power to deepen the understanding of brain disease. However, due to the heterogeneous and dynamic nature of fMRI BOLD signals, we still lack a fundamental model to effectively identify and integrate the intrinsic functional characteristics at population level. Here we introduced a novel supervised structure learning method to explore latent structures of resting state fMRI (rs-fMRI) data that belongs to multiple groups. Using the dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI) as a testbed, we successfully identified a "TREE" structure in both whole brain and ROI based analysis, which reflects a virtual "path" of Alzheimer's Disease (AD) progression as multiple stages.
Date of Conference: 04-07 April 2018
Date Added to IEEE Xplore: 24 May 2018
ISBN Information:
Electronic ISSN: 1945-8452
Conference Location: Washington, DC, USA

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