title: Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment.
abstract: In recent years, functional connectomics signatures have been shown to be a very valuable tool in characterizing and differentiating brain disorders from normal controls. However, if the functional connectivity alterations in a brain disease are localized within sub-networks of a connectome, then accurate identification of such disease-specific sub-networks is critical and this capability entails both fine-granularity definition of connectome nodes and effective clustering of connectome nodes into disease-specific and non-disease-specific sub-networks. In this work, we adopted the recently developed DICCCOL (dense individualized and common connectivity-based cortical landmarks) system as a fine-granularity high-resolution connectome construction method to deal with the first issue, and employed an effective variant of non-negative matrix factorization (NMF) method to pinpoint disease-specific sub-networks, which we called atomic connectomics signatures in this work. We have implemented and applied this novel framework to two mild cognitive impairment (MCI) datasets from two different research centers, and our experimental results demonstrated that the derived atomic connectomics signatures can effectively characterize and differentiate MCI patients from their normal controls. In general, our work contributed a novel computational framework for deriving descriptive and distinctive atomic connectomics signatures in brain disorders.
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Jinli
authors.initials: J
authors.lastname: Ou
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Li
authors.initials: L
authors.lastname: Xie
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.|Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.affiliation: Department of Psychology, The University of Georgia, Athens, GA, USA.
authors.forename: Douglas P
authors.initials: DP
authors.lastname: Terry
authors.affiliation: Department of Psychology, The University of Georgia, Athens, GA, USA.
authors.forename: A Nicholas
authors.initials: AN
authors.lastname: Puente
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Rongxin
authors.initials: R
authors.lastname: Jiang
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Yaowu
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing, China.
authors.forename: Lihong
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Radiology, UNC, Chapel Hill, NC, USA.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Department of Statistics, Yale University, New Haven, CT, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Psychology, The University of Georgia, Athens, GA, USA.|Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: L Stephen
authors.initials: LS
authors.lastname: Miller
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA. tliu@cs.uga.edu.
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000368:Aged; D001921:Brain / Q000503:physiopathology*; D060825:Cognitive Dysfunction / Q000145:classification / Q000503:physiopathology*; D063132:Connectome / Q000379:methods*; D066264:Datasets as Topic; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009434:Neural Pathways / Q000503:physiopathology; D012146:Rest
keywords: Brain networks; DICCCOL; Functional connectome; MCI; NMF; Resting state fMRI