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Spatial-Temporal Analysis of Multi-Subject Functional Magnetic Resonance Imaging Data

Author

Listed:
  • Zhang, Tingting
  • Pham, Minh
  • Yan, Guofen
  • Wang, Yaotian
  • Medina-DeVilliers, Sara
  • Coan, James A.

Abstract

Functional magnetic resonance imaging (fMRI) is one of the most popular neuroimaging technologies used in human brain studies. However, fMRI data analysis faces several challenges, including intensive computation due to the massive data size and large estimation errors due to a low signal-to-noise ratio of the data. A new statistical model and a computational algorithm are proposed to address these challenges. Specifically, a new multi-subject general linear model is built for stimulus-evoked fMRI data. The new model assumes that brain responses to stimuli at different brain regions of various subjects fall into a low-rank structure and can be represented by a few principal functions. Therefore, the new model enables combining data information across subjects and regions to evaluate subject-specific and region-specific brain activity. Two optimization functions and a new fast-to-compute algorithm are developed to analyze multi-subject stimulus-evoked fMRI data and address two research questions of a broad interest in psychology: evaluating every subject’s brain responses to different stimuli and identifying brain regions responsive to the stimuli. Both simulation and real data analysis are conducted to show that the new method can outperform existing methods by providing more efficient estimates of brain activity.

Suggested Citation

  • Zhang, Tingting & Pham, Minh & Yan, Guofen & Wang, Yaotian & Medina-DeVilliers, Sara & Coan, James A., 2024. "Spatial-Temporal Analysis of Multi-Subject Functional Magnetic Resonance Imaging Data," Econometrics and Statistics, Elsevier, vol. 31(C), pages 117-129.
    • Handle: RePEc:eee:ecosta:v:31:y:2024:i:c:p:117-129
    DOI: 10.1016/j.ecosta.2021.02.006
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, September.
    2. NESTEROV, Yurii, 2013. "Gradient methods for minimizing composite functions," LIDAM Reprints CORE 2510, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, September.
    4. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    5. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
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