Paper Info
Title
Inference of brain pathway activities for Alzheimer's disease classification
Abstract
Alzheimer's disease (AD) is a neurodegenerative and progressive disorder that results in brain malfunctions. Resting-state (RS) functional magnetic resonance imaging (fMRI) techniques have been successfully applied for quantifying brain activities of both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients. Region-based approaches are widely utilized to classify patients from cognitively normal subjects (CN). Nevertheless, region-based approaches have a few limitations, reproducibility owing to selection of disease-specific brain regions, and heterogeneity of brain activities during disease progression. For coping with these issues, network-based approaches have been suggested in the field of molecular bioinformatics. In comparison with individual gene-based approaches, they acquired more accurate results in diverse disease classification, and reproducibility was confirmed by replication studies. In our work, we applied a similar methodology integrating brain pathway information into pathway activity inference, and permitting classification of both aMCI and AD patients based on pathway activities rather than single region activities.
Year
DOI
Venue
2015
10.1186/1472-6947-15-S1-S1
BMC Med. Inf. & Decision Making
Keywords
Field
DocType
Functional Connectivity, Default Mode Network, Support Vector Machine Model, Cognitively Normal, aMCI Patient
Data mining,Neuroscience,Disease,Default mode network,Alzheimer's disease,Functional magnetic resonance imaging,Inference,Functional neuroimaging,Nerve net,Bioinformatics,Medicine,Magnetic resonance imaging
Journal
Volume
Issue
ISSN
15
S-1
1472-6947
Citations 
PageRank 
References 
0
0.34
8
Authors
7
Name
Order
Citations
PageRank
Jongan Lee121.73
Younghoon Kim2144.26
Yong Jeong3575.68
Duk L Na4769.88
Jongwon Kim51042153.38
Kwang H. Lee649030.05
Doheon Lee71144113.05