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Analysis of Group ICA-Based Connectivity Measures from fMRI: Application to Alzheimer's Disease

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  • Shanshan Li
  • Ani Eloyan
  • Suresh Joel
  • Stewart Mostofsky
  • James Pekar
  • Susan Spear Bassett
  • Brian Caffo

Abstract

Functional magnetic resonance imaging (fMRI) is a powerful tool for the in vivo study of the pathophysiology of brain disorders and disease. In this manuscript, we propose an analysis stream for fMRI functional connectivity data and apply it to a novel study of Alzheimer's disease. In the first stage, spatial independent component analysis is applied to group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population-level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact logistic regression for matched pairs data. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity when comparing subjects evidencing mild cognitive impairment relative to carefully matched controls.

Suggested Citation

  • Shanshan Li & Ani Eloyan & Suresh Joel & Stewart Mostofsky & James Pekar & Susan Spear Bassett & Brian Caffo, 2012. "Analysis of Group ICA-Based Connectivity Measures from fMRI: Application to Alzheimer's Disease," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
    • Handle: RePEc:plo:pone00:0049340
    DOI: 10.1371/journal.pone.0049340
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    References listed on IDEAS

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    1. Crainiceanu, Ciprian M. & Staicu, Ana-Maria & Di, Chong-Zhi, 2009. "Generalized Multilevel Functional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1550-1561.
    2. Crainiceanu, Ciprian M. & Caffo, Brian S. & Di, Chong-Zhi & Punjabi, Naresh M., 2009. "Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 541-555.
    3. Peter Hall & Mohammad Hosseini‐Nasab, 2006. "On properties of functional principal components analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 109-126, February.
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    1. Yasser Al Zaim & Mohammad Reza Faridrohani, 2021. "Bayesian random projection-based signal detection for Gaussian scale space random fields," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 503-532, September.

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