Ensemble hierarchical high-order functional connectivity networks for MCI classification

Xiaobo Chen, Han Zhang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


Conventional functional connectivity (FC) and corresponding networks focus on characterizing the pairwise correlation between two brain regions,while the high-order FC (HOFC) and networks can model more complex relationship between two brain region “pairs” (i.e.,four regions). It is eyecatching and promising for clinical applications by its irreplaceable function of providing unique and novel information for brain disease classification. Since the number of brain region pairs is very large,clustering is often used to reduce the scale of HOFC network. However,a single HOFC network,generated by a specific clustering parameter setting,may lose multifaceted,highly complementary information contained in other HOFC networks. To accurately and comprehensively characterize such complex HOFC towards better discriminability of brain diseases,in this paper,we propose a novel HOFC based disease diagnosis framework,which can hierarchically generate multiple HOFC networks and further ensemble them with a selective feature fusion method. Specifically,we create a multi-layer HOFC network construction strategy,where the networks in upper layers are formed by hierarchically clustering the nodes of the networks in lower layers. In such a way,information is passed from lower layers to upper layers by effectively removing the most redundant part of information and,at the same time,retaining the most unique part. Then,the retained information/features from all HOFC networks are fed into a selective feature fusion method,which combines sequential forward selection and sparse regression,to further select the most discriminative feature subset for classification. Experimental results confirm that our novel method outperforms all the single HOFC networks corresponding to any single parameter setting in diagnosis of mild cognitive impairment (MCI) subjects.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319467221
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Brain network
  • Functional connectivity
  • Functional magnetic resonance imaging
  • Hierarchical clustering
  • High-order network
  • Resting state

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Ensemble hierarchical high-order functional connectivity networks for MCI classification'. Together they form a unique fingerprint.

Cite this