Brain Imaging and Behavior

, Volume 9, Issue 4, pp 663–677 | Cite as

Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment

  • Jinli Ou
  • Li Xie
  • Xiang Li
  • Dajiang Zhu
  • Douglas P. Terry
  • A. Nicholas Puente
  • Rongxin Jiang
  • Yaowu Chen
  • Lihong Wang
  • Dinggang Shen
  • Jing Zhang
  • L. Stephen Miller
  • Tianming LiuEmail author
Original Research


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.


Resting state fMRI Brain networks Functional connectome MCI NMF DICCCOL 



T Liu was supported by NIH R01 DA-033393, NIH R01 AG-042599, NSF CAREER Award IIS-1149260, NSF CBET-1302089 and NSF BCS-1439051. J Zhang was supported by start-up funding and Sesseel Award from Yale University. The authors would like to thank the anonymous reviewers for their constructive comments.

Conflict of Interest

Jinli Ou, Li Xie, Xiang Li, Dajiang Zhu, Douglas P. Terry, A. Nicholas Puente, Rongxin Jiang, Yaowu Chen, Lihong Wang, Dinggang Shen, Jing Zhang, L. Stephen Miller, and Tianming Liu declare that they have no conflicts of interest.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all patients for being included in the study.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Jinli Ou
    • 1
  • Li Xie
    • 1
  • Xiang Li
    • 2
  • Dajiang Zhu
    • 2
    • 4
  • Douglas P. Terry
    • 3
  • A. Nicholas Puente
    • 3
  • Rongxin Jiang
    • 1
  • Yaowu Chen
    • 1
  • Lihong Wang
    • 7
  • Dinggang Shen
    • 6
  • Jing Zhang
    • 5
  • L. Stephen Miller
    • 3
    • 4
  • Tianming Liu
    • 2
    Email author
  1. 1.School of Biomedical Engineering & Instrument ScienceZhejiang UniversityHangzhouChina
  2. 2.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensUSA
  3. 3.Department of PsychologyThe University of GeorgiaAthensUSA
  4. 4.Bioimaging Research CenterThe University of GeorgiaAthensUSA
  5. 5.Department of StatisticsYale UniversityNew HavenUSA
  6. 6.Department of RadiologyUNCChapel HillUSA
  7. 7.Department of Biomedical EngineeringTsinghua UniversityBeijingChina

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