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Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis

Author

Listed:
  • Dai, Ning
  • Jones, Galin L.
  • Fiecas, Mark

Abstract

The amplitude of the oscillatory patterns present in spontaneous fluctuations of brain signals obtained from resting-state functional magnetic resonance imaging (fMRI), measured using an index called the fractional amplitude of low-frequency fluctuation (fALFF), is a well-known measure of brain activity with potential to serve as a marker for brain dysfunction. With the rise of longitudinal neuroimaging studies, there is a great need for methodologies that take advantage of the longitudinal design in modeling the impact of aging or disease progression. Motivated by the longitudinal design of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a novel Bayesian longitudinal model is developed in order to estimate the spectra of resting-state fMRI time courses, from which one can extract estimates of fALFF that are potentially associated with aging. The model incorporates within-subject correlation to improve estimates of the spectra, in addition to the variability that naturally arises between subjects. The model is validated using simulated data to show the gains in performance for estimating fALFF by taking advantage of the longitudinal design. Finally, a longitudinal analysis on fALFF from the resting-state fMRI data from ADNI is conducted, where the impact of both Alzheimer’s disease and aging on the spontaneous fluctuations of brain activity is shown.

Suggested Citation

  • Dai, Ning & Jones, Galin L. & Fiecas, Mark, 2020. "Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis," Econometrics and Statistics, Elsevier, vol. 15(C), pages 104-116.
    • Handle: RePEc:eee:ecosta:v:15:y:2020:i:c:p:104-116
    DOI: 10.1016/j.ecosta.2019.01.002
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    References listed on IDEAS

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    1. Dai, Ning & Jones, Galin L., 2017. "Multivariate initial sequence estimators in Markov chain Monte Carlo," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 184-199.
    2. Pedro C. Alvarez‐Esteban & Carolina Euán & Joaquín Ortega, 2016. "Time series clustering using the total variation distance with applications in oceanography," Environmetrics, John Wiley & Sons, Ltd., vol. 27(6), pages 355-369, September.
    3. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    4. Fiecas, Mark & von Sachs, Rainer, 2014. "Data-driven shrinkage of the spectral density matrix of a high-dimensional time series," LIDAM Reprints ISBA 2014045, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Ori Rosen & Sally Wood & David S. Stoffer, 2012. "AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1575-1589, December.
    6. Jeff Goldsmith & Ciprian M. Crainiceanu & Brian Caffo & Daniel Reich, 2012. "Longitudinal penalized functional regression for cognitive outcomes on neuronal tract measurements," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(3), pages 453-469, May.
    7. Carolina Euán & Hernando Ombao & Joaquín Ortega, 2018. "The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 71-99, April.
    8. Martha Skup & Hongtu Zhu & Heping Zhang, 2012. "Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates," Biometrics, The International Biometric Society, vol. 68(4), pages 1083-1092, December.
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