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Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging

Institution:
1The Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of Sydney Sydney, NSW, Australia.
2Surgical Planning Laboratory, Harvard Medical School, Brigham and Women's Hospital Boston, MA, USA
Publisher:
Frontiers Media SA
Publication Date:
Feb-2016
Journal:
Front Aging Neurosci
Volume Number:
8
Pages:
23
Citation:
Front Aging Neurosci. 2016 Feb 23;8:23.
PubMed ID:
26941639
PMCID:
PMC4763344
Keywords:
pattern recognition, neuroimaging, multi-modal, Alzheimer's disease, mild cognitive impairment
Appears in Collections:
NAC, NA-MIC, SLICER, SPL
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U01 AG024904/AG/NIA NIH HHS/United States
Generated Citation:
Liu S., Cai W., Pujol S., Kikinis R., Feng D.D. Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci. 2016 Feb 23;8:23. PMID: 26941639. PMCID: PMC4763344.
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The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC) to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed nine types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI, and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging.

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