Structural connectivity guided sparse effective connectivity for MCI identification

Yang Li, Jingyu Liu, Meilin Luo, Ke Li, Pew Thian Yap, Minjeong Kim, Chong Yaw Wee, Dinggang Shen

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

1 Citation (Scopus)

Abstract

Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages299-306
Number of pages8
Volume10541 LNCS
ISBN (Print)9783319673882
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 102017 Sep 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1017/9/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Li, Y., Liu, J., Luo, M., Li, K., Yap, P. T., Kim, M., Wee, C. Y., & Shen, D. (2017). Structural connectivity guided sparse effective connectivity for MCI identification. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10541 LNCS, pp. 299-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_35