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Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

Author

Listed:
  • Nie Yunlong
  • Wang Jie
  • Wu Sidi
  • Wang Liangliang
  • Cao Jiguo

    (Department of Statistics and Actuarial Science, Simon Fraser University, Room SC K10545 8888 University Drive, Burnaby, BC V5A 1S6, Canada)

  • Opoku Eugene
  • Yasmin Laila
  • Song Yin
  • Nathoo Farouk S.

    (Department of Mathematics and Statistics, University of Victoria, Victoria, Canada)

  • Scarapicchia Vanessa
  • Gawryluk Jodie

    (Department of Psychology, University of Victoria, P. O. Box 1700 STN CSC, Victoria, British Columbia, V8W 2Y2Canada)

Abstract

We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

Suggested Citation

  • Nie Yunlong & Wang Jie & Wu Sidi & Wang Liangliang & Cao Jiguo & Opoku Eugene & Yasmin Laila & Song Yin & Nathoo Farouk S. & Scarapicchia Vanessa & Gawryluk Jodie, 2020. "Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(3), pages 1-18, June.
    • Handle: RePEc:bpj:sagmbi:v:19:y:2020:i:3:p:18:n:3
    DOI: 10.1515/sagmb-2019-0058
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    References listed on IDEAS

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    1. Hongtu Zhu & Zakaria Khondker & Zhaohua Lu & Joseph G. Ibrahim, 2014. "Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 977-990, September.
    2. Francesco C. Stingo & Michele Guindani & Marina Vannucci & Vince D. Calhoun, 2013. "An Integrative Bayesian Modeling Approach to Imaging Genetics," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 876-891, September.
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