Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China.
Department of Biostatistics and Bioinformatics, University of Louisville, Louisville, Kentucky, USA.
Biometrics. 2022 Sep;78(3):1122-1126. doi: 10.1111/biom.13588. Epub 2021 Nov 15.
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.
我们感谢编辑组织了这些讨论，感谢讨论者提出了富有洞察力的意见。我们的回应提供了结果和评论，以解决讨论中提出的问题。具体来说，我们展示了 DICA 在很大程度上表现出比标准 ICA 更好或相当的稳定性的结果。我们还通过显示 DICA 在可重复性地恢复主要脑功能网络方面通常比标准 ICA 具有更高的可靠性，在真实的 fMRI 应用中验证了 DICA。我们提供了有关该方法计算复杂性的详细信息。DICA 的计算成本非常合理，对 fMRI 和 DTI 数据的分析可以轻松在 PC 或笔记本电脑上实现。最后，我们包括了关于未来扩展 DICA 框架的几个方向的讨论。
