Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Multi-Channel Neurodegenerative Pattern Analysis and its Application in Alzheimer's Disease Characterization

Institution:
1Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Surgical Planning Laboratory (SPL), Brigham and Women's Hospital, Harvard Medical School, United States. Electronic address: sliu7418@uni.sydney.edu.au.
2Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Publisher:
Elsevier Science
Publication Date:
Sep-2014
Journal:
Comput Med Imaging Graph
Volume Number:
38
Issue Number:
6
Pages:
436-44
Citation:
Comput Med Imaging Graph. 2014 Sep;38(6):436-44.
PubMed ID:
24933011
PMCID:
PMC4135007
Keywords:
Alzheimer's disease, Mild cognitive impairment, Pattern analysis
Appears in Collections:
NAC, NA-MIC, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Liu S., Cai W., Wen L., Feng D.D., Pujol S., Kikinis R., Fulham M.J., Eberl S. Multi-Channel Neurodegenerative Pattern Analysis and its Application in Alzheimer's Disease Characterization. Comput Med Imaging Graph. 2014 Sep;38(6):436-44. PMID: 24933011. PMCID: PMC4135007.
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Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, as disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could effectively distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to global methods and other pattern analysis methods based on clinical expertise or statistics tests. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.

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