Correlation-weighted sparse group representation for brain network construction in MCI classification

Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen

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

8 Citations (Scopus)

Abstract

Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders,such as Alzheimer’s disease and its early stage,mild cognitive impairment (MCI). In all these applications,the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network,sparse learning has been widely used for complex BFCN construction. However,the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network,which ignores the link strength and could remove strong links in the brain network. Besides,the conventional sparse regularization often overlooks group structure in the brain network,i.e.,a set of links (or connections) sharing similar attribute. To address these issues,we propose to construct BFCN by integrating both link strength and group structure information. Specifically,a novel correlation-weighted sparse group constraint is devised to account for and balance among (1) sparsity,(2) link strength,and (3) group structure,in a unified framework. The proposed method is applied to MCI classification using the resting-state fMRI from ADNI-2 dataset. Experimental results show that our method is effective in modeling human brain connectomics,as demonstrated by superior MCI classification accuracy of 81.8%. Moreover,our method is promising for its capability in modeling more biologically meaningful sparse brain networks,which will benefit both basic and clinical neuroscience studies.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Mert R. Sabuncu, William Wells, Sebastian Ourselin, Leo Joskowicz
PublisherSpringer Verlag
Pages37-45
Number of pages9
ISBN (Print)9783319467191
DOIs
Publication statusPublished - 2016
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., & Shen, D. (2016). Correlation-weighted sparse group representation for brain network construction in MCI classification. In G. Unal, M. R. Sabuncu, W. Wells, S. Ourselin, & L. Joskowicz (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 37-45). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_5