Identification of MCI using optimal sparse MAR modeled effective connectivity networks

Chong Yaw Wee, Yang Li, Biao Jie, Zi Wen Peng, Dinggang Shen

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

5 Citations (Scopus)

Abstract

Capability of detecting causal or effective connectivity from resting-state functional magnetic resonance imaging (R-fMRI) is highly desirable for better understanding the cooperative nature of the brain. Effective connectivity provides specific dynamic temporal information of R-fMRI time series and reflects the directional causal influence of one brain region over another. These causal influences among brain regions are normally extracted based on the concept of Granger causality. Conventionally, the effective connectivity is inferred using multivariate autoregressive (MAR) modeling with default model order q = 1, considering low frequency fluctuation of R-fMRI time series. This assumption, although reduces the modeling complexity, does not guarantee the best fitting of R-fMRI time series at different brain regions. Instead of using the default model order, we propose to estimate the optimal model order based upon MAR order distribution to better characterize these causal influences at each brain region. Due to sparse nature of brain connectivity networks, an orthogonal least square (OLS) regression algorithm is incorporated to MAR modeling to minimize spurious effective connectivity. Effective connectivity networks inferred using the proposed optimal sparse MAR modeling are applied to Mild Cognitive Impairment (MCI) identification and obtained promising results, demonstrating the importance of using optimal causal relationships between brain regions for neurodegeneration disorder identification.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages319-327
Number of pages9
EditionPART 2
DOIs
Publication statusPublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8150 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13/9/2213/9/26

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wee, C. Y., Li, Y., Jie, B., Peng, Z. W., & Shen, D. (2013). Identification of MCI using optimal sparse MAR modeled effective connectivity networks. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 2 ed., pp. 319-327). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-40763-5_40