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 proceedingChapter

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 ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages319-327
Number of pages9
Volume16
EditionPt 2
Publication statusPublished - 2013 Jan 1
Externally publishedYes

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Brain
Magnetic Resonance Imaging
Cognitive Dysfunction
Least-Squares Analysis
Causality

ASJC Scopus subject areas

  • Medicine(all)

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 ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 16, pp. 319-327)

Identification of MCI using optimal sparse MAR modeled effective connectivity networks. / Wee, Chong Yaw; Li, Yang; Jie, Biao; Peng, Zi Wen; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 2. ed. 2013. p. 319-327.

Research output: Chapter in Book/Report/Conference proceedingChapter

Wee, CY, Li, Y, Jie, B, Peng, ZW & Shen, D 2013, Identification of MCI using optimal sparse MAR modeled effective connectivity networks. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 16, pp. 319-327.
Wee CY, Li Y, Jie B, Peng ZW, Shen D. Identification of MCI using optimal sparse MAR modeled effective connectivity networks. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 16. 2013. p. 319-327
Wee, Chong Yaw ; Li, Yang ; Jie, Biao ; Peng, Zi Wen ; Shen, Dinggang. / Identification of MCI using optimal sparse MAR modeled effective connectivity networks. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 2. ed. 2013. pp. 319-327
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