MICA: A toolkit for multimodal image coupling analysis,Journal of Neuroscience Methods

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MICA: A toolkit for multimodal image coupling analysis
Journal of Neuroscience Methods ( IF 2.7 ) Pub Date : 2020-10-02 , DOI: 10.1016/j.jneumeth.2020.108962
Bo Hu ₁ , Ying Yu ₁ , Wen Wang ₁ , Guang-Bin Cui ₁

Affiliation

Background

Analytical methods of brain research involving across-voxel correlation between multimodal images are currently tedious and slow due to the amount of manual interaction required. We have developed a new software package to automate and simplify many of these tasks.

New method and results

Our software performs four primary functions to aid in research. First, it helps with consistent renaming of files preprocessed with other software, enabling more accurate analysis. Second, it automates ROI extraction using data from existing and custom brain atlases. Third, it performs coupling analysis to obtain across-voxel Pearson correlation coefficients between images of different modalities based on these brain atlases or custom ROIs. Fourth, it automatically performs multiple comparison correction to correct the P-value using two false discovery rate (FDR) methods and a Bonferroni method to reduce the false-positive rate.

Comparison with existing methods

Previous researchers have investigated the couplings between blood supply and brain functional topology in healthy brains and those from patients with type 2 diabetes, chronic migraine, and schizophrenia. These studies conducted analyses of both the whole and parts of the brain in terms of neuronal activity and blood perfusion, but the procedures were laborious and time-consuming.

Conclusion

We have developed a convenient and time-saving software package using MATLAB 2014a to automate the data preparation and analysis of across-voxel coupling between multimodal images.

中文翻译：

MICA：用于多模式图像耦合分析的工具包

背景

由于需要人工交互，目前涉及多模态图像之间跨体素相关的大脑研究分析方法繁琐且缓慢。我们已经开发了一个新的软件包来自动化和简化许多任务。

新方法和结果

我们的软件执行四个主要功能以辅助研究。首先，它有助于一致地重命名使用其他软件预处理的文件，从而可以进行更准确的分析。其次，它使用来自现有和自定义脑图集的数据自动进行ROI提取。第三，它基于这些脑图集或自定义ROI，执行耦合分析以获得不同模态图像之间的跨体素Pearson相关系数。第四，它使用两种错误发现率（FDR）方法和Bonferroni方法自动执行多次比较校正以校正P值，以降低错误肯定率。