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Rejoinder to discussions of “distributional independent component analysis for diverse neuroimaging modalities”

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  • Ben Wu
  • Subhadip Pal
  • Jian Kang
  • Ying Guo

Abstract

We thank the editors for organizing the discussions and the discussants for insightful comments. Our rejoinder provides results and comments to address the questions raised in the discussions. Specifically, we present results showing DICA largely demonstrates better or comparable stability as compared with standard ICA. We also validate the DICA in real fMRI application by showing DICA generally shows higher reliability in reproducibly recovering major brain functional networks as compared with the standard ICA. We provide details on the computational complexity of the method. The computational cost of DICA is very reasonable with the analysis of the fMRI and DTI data easily implementable on a PC or laptop. Finally, we include discussions on several directions for extending the DICA framework in the future.

Suggested Citation

  • Ben Wu & Subhadip Pal & Jian Kang & Ying Guo, 2022. "Rejoinder to discussions of “distributional independent component analysis for diverse neuroimaging modalities”," Biometrics, The International Biometric Society, vol. 78(3), pages 1122-1126, September.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:3:p:1122-1126
    DOI: 10.1111/biom.13588
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    References listed on IDEAS

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    1. Branislav Panić & Jernej Klemenc & Marko Nagode, 2020. "Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation," Mathematics, MDPI, vol. 8(3), pages 1-29, March.
    2. Ying Guo & Li Tang, 2013. "A Hierarchical Model for Probabilistic Independent Component Analysis of Multi-Subject fMRI Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 970-981, December.
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