Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Ou, J; Xie, L; Li, X; Zhu, D; Terry, DP; Puente, AN; Jiang, R; Chen, Y; Wang, L; Shen, D; Zhang, J; Miller, LS; Liu, T

Published Date

  • December 2015

Published In

Volume / Issue

  • 9 / 4

Start / End Page

  • 663 - 677

PubMed ID

  • 25355371

Electronic International Standard Serial Number (EISSN)

  • 1931-7565

Digital Object Identifier (DOI)

  • 10.1007/s11682-014-9320-1


  • eng

Conference Location

  • United States