Paper Info
Title
Connectomics signature for characterizaton of mild cognitive impairment and schizophrenia.
Abstract
Human connectomes constructed via neuroimaging data offer a comprehensive description of the macro-scale structural connectivity within the brain. Thus quantitative assessment of connectome-scale structural and functional connectivities will not only fundamentally advance our understanding of normal brain organization and function, but also have significant importance to systematically and comprehensively characterize many devastating brain conditions. In recognition of the importance of connectome and connectomics, in this paper, we develop and evaluate a novel computational framework to construct structural connectomes from diffusion tensor imaging (DTI) data and assess connectome-scale functional connectivity alterations in mild cognitive impairment (MCI) and schizophrenia (SZ) from concurrent resting state fMRI (R-fMRI) data, in comparison with their healthy controls. By applying effective feature selection approaches, we discovered informative and robust functional connectomics signatures that can distinctively characterize and successfully differentiate the two brain conditions of MCI and SZ from their healthy controls (classification accuracies are 96% and 100%, respectively). Our results suggest that connectomics signatures could be a general, powerful methodology for characterization and classification of many brain conditions in the future.
Year
DOI
Venue
2014
10.1109/ISBI.2014.6867874
ISBI
Keywords
Field
DocType
bioinformatics,biomedical research
Neuroscience,Diffusion MRI,Connectomics,Feature selection,Pattern recognition,Connectome,Computer science,Resting state fMRI,Artificial intelligence,Neuroimaging,Schizophrenia,Cognitive impairment
Conference
Volume
ISSN
Citations 
2014
1945-7928
1
PageRank 
References 
Authors
0.40
2
4
Name
Order
Citations
PageRank
Dajiang Zhu132036.72
Dinggang Shen27837611.27
Xi Jiang331137.88
Tianming Liu41033112.95