title: Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization.
abstract: 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.
authors.affiliation: Biomedical 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.
authors.forename: Sidong
authors.initials: S
authors.lastname: Liu
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia.
authors.forename: Lingfeng
authors.initials: L
authors.lastname: Wen
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Med-X Research Institute, Shanghai Jiao Tong University, China.
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
authors.affiliation: Surgical Planning Laboratory (SPL), Brigham and Women's Hospital, Harvard Medical School, United States.
authors.forename: Sonia
authors.initials: S
authors.lastname: Pujol
authors.affiliation: Surgical Planning Laboratory (SPL), Brigham and Women's Hospital, Harvard Medical School, United States.
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Australia.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fulham
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia.
authors.forename: Stefan
authors.initials: S
authors.lastname: Eberl
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000378:metabolism / Q000473:pathology*; D001921:Brain / Q000000981:diagnostic imaging* / Q000378:metabolism; D019992:Databases as Topic; D018450:Disease Progression; D006801:Humans; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging; D010363:Pattern Recognition, Automated; D049268:Positron-Emission Tomography
keywords: Alzheimer's disease; Mild cognitive impairment; Pattern analysis