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

    15 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
    EditorsSebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
    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
    Country/TerritoryGreece
    CityAthens
    Period16/10/2116/10/21

    Bibliographical note

    Publisher Copyright:
    © Springer International Publishing AG 2016.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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