Multi-modal brain connectivity study using deep collaborative learning

Wenxing Hu, Biao Cai, Vince Daniel Calhoun, Yu Ping Wang

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

Abstract

Functional connectivities in the brain explain how different brain regions interact with each other when conducting a specific activity. Canonical correlation analysis (CCA) based models, have been used to detect correlations and to analyze brain connectivities which further help explore how the brain works. However, the data representation of CCA lacks label related information and may be limited when applied to functional connectivity study. Collaborative regression was proposed to address the limitation of CCA by combining correlation analysis and regression. However, both prediction and correlation are sacrificed as linear collaborative regression use the same set of projections on both correlation and regression. We propose a novel method, deep collaborative learning (DCL), to address the limitations of CCA and collaborative regression. DCL improves collaborative regression by combining correlation analysis and label information using deep networks, which may lead to better performance both for classification/prediction and for correlation detection. Results demonstrated the out-performance of DCL over other conventional models in terms of classification accuracy. Experiments showed the difference of brain connectivities between different age groups may be more significant than that between different cognition groups.

Original languageEnglish (US)
Title of host publicationGraphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities - 2nd International Workshop, GRAIL 2018 and 1st International Workshop, Beyond MIC 2018 Held in Conjunction with MICCAI 2018, Proceedings
EditorsDanail Stoyanov, Aristeidis Sotiras, Bartlomiej Papiez, Adrian V. Dalca, Anne Martel, Sarah Parisot, Enzo Ferrante, Lena Maier-Hein, Mert R. Sabuncu, Li Shen, Zeike Taylor
PublisherSpringer Verlag
Pages66-73
Number of pages8
ISBN (Print)9783030006884
DOIs
StatePublished - Jan 1 2018
Externally publishedYes
Event2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 20 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11044 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/20/189/20/18

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Keywords

  • Canonical correlation
  • Deep network
  • fMRI
  • Functional connectivity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hu, W., Cai, B., Calhoun, V. D., & Wang, Y. P. (2018). Multi-modal brain connectivity study using deep collaborative learning. In D. Stoyanov, A. Sotiras, B. Papiez, A. V. Dalca, A. Martel, S. Parisot, E. Ferrante, L. Maier-Hein, M. R. Sabuncu, L. Shen, & Z. Taylor (Eds.), Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities - 2nd International Workshop, GRAIL 2018 and 1st International Workshop, Beyond MIC 2018 Held in Conjunction with MICCAI 2018, Proceedings (pp. 66-73). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11044 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00689-1_7