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 Liu

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)663-677
Number of pages15
JournalBrain Imaging and Behavior
Volume9
Issue number4
DOIs
Publication statusPublished - 2015 Dec 1
Externally publishedYes

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Keywords

  • Brain networks
  • DICCCOL
  • Functional connectome
  • MCI
  • NMF
  • Resting state fMRI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Behavioral Neuroscience
  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience
  • Neurology
  • Psychiatry and Mental health
  • Clinical Neurology

Cite this

Ou, J., Xie, L., Li, X., Zhu, D., Terry, D. P., Puente, A. N., Jiang, R., Chen, Y., Wang, L., Shen, D., Zhang, J., Miller, L. S., & Liu, T. (2015). Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment. Brain Imaging and Behavior, 9(4), 663-677. https://doi.org/10.1007/s11682-014-9320-1