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Spatio-Spectral Mixed-Effects Model for Functional Magnetic Resonance Imaging Data

Author

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  • Hakmook Kang
  • Hernando Ombao
  • Crystal Linkletter
  • Nicole Long
  • David Badre

Abstract

The goal of this article is to model cognitive control related activation among predefined regions of interest (ROIs) of the human brain while properly adjusting for the underlying spatio-temporal correlations. Standard approaches to fMRI analysis do not simultaneously take into account both the spatial and temporal correlations that are prevalent in fMRI data. This is primarily due to the computational complexity of estimating the spatio-temporal covariance matrix. More specifically, they do not take into account multiscale spatial correlation (between-ROIs and within-ROI). To address these limitations, we propose a spatio-spectral mixed-effects model. Working in the spectral domain simplifies the temporal covariance structure because the Fourier coefficients are approximately uncorrelated across frequencies. Additionally, by incorporating voxel-specific and ROI-specific random effects, the model is able to capture the multiscale spatial covariance structure: distance-dependent local correlation (within an ROI), and distance-independent global correlation (between-ROIs). Building on existing theory on linear mixed-effects models to conduct estimation and inference, we applied our model to fMRI data to study activation in prespecified ROIs in the prefontal cortex and estimate the correlation structure in the network. Simulation studies demonstrate that ignoring the multiscale correlation leads to higher false positive error rates.

Suggested Citation

  • Hakmook Kang & Hernando Ombao & Crystal Linkletter & Nicole Long & David Badre, 2012. "Spatio-Spectral Mixed-Effects Model for Functional Magnetic Resonance Imaging Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 568-577, June.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:498:p:568-577
    DOI: 10.1080/01621459.2012.664503
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    Cited by:

    1. Zheng, Xueying & Xue, Lan & Qu, Annie, 2018. "Time-varying correlation structure estimation and local-feature detection for spatio-temporal data," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 221-239.
    2. Stefano Castruccio & Hernando Ombao & Marc G. Genton, 2018. "A scalable multi‐resolution spatio‐temporal model for brain activation and connectivity in fMRI data," Biometrics, The International Biometric Society, vol. 74(3), pages 823-833, September.
    3. Brian J. Reich & Joseph Guinness & Simon N. Vandekar & Russell T. Shinohara & Ana†Maria Staicu, 2018. "Fully Bayesian spectral methods for imaging data," Biometrics, The International Biometric Society, vol. 74(2), pages 645-652, June.
    4. Zhe Yu & Raquel Prado & Erin Burke Quinlan & Steven C. Cramer & Hernando Ombao, 2016. "Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 549-563, April.

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