Background

Independent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects’ clinical and demographic variables. Existing ICA methods and toolboxes don’t incorporate subjects’ covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects’ groups.

New method

We introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script.

Comparison to existing methods

HINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons.

Results

We demonstrate the steps and functionality of HINT with an fMRI example data to estimate treatment effects on brain networks while controlling for other covariates. Results demonstrate estimated brain networks and model-based comparisons between the treatment and control groups. In comparisons using synthetic fMRI data, HINT shows desirable statistical power in detecting group differences in networks especially in small sample sizes, while maintaining a low false positive rate. HINT also demonstrates similar or increased accuracy in reconstructing both population- and individual-level source signal maps as compared to some state-of-the-art group ICA methods.

Conclusion

HINT can provide a useful tool for both statistical and neuroscience researchers to evaluate and test differences in brain networks between subject groups.

中文翻译：

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提示：一个分层独立成分分析工具箱，用于使用神经影像数据研究大脑功能网络。

背景

独立成分分析 (ICA) 是神经科学研究中研究大脑组织的流行工具。在功能磁共振成像研究中，一个重要的目标是研究受试者的临床和人口统计变量如何调节大脑网络。现有的 ICA 方法和工具箱没有将受试者的协变量效应纳入大脑网络的 ICA 估计中，这可能会导致检测受试者组之间大脑网络差异的准确性和统计能力的损失。

新方法

我们引入了一个 Matlab 工具箱HINT（分层IN依赖成分分析工具箱），它提供了分层协变量调整 ICA (hc-ICA)，用于建模和测试协变量效应，并生成基于模型的大脑网络估计- 和个人层面。HINT 提供了一个用户友好的 Matlab GUI，允许用户轻松加载图像、指定协变量效应、通过 EM 算法监控模型估计、指定假设检验以及可视化结果。HINT 还有一个命令行界面，允许用户使用脚本方便地运行和重现分析。

与现有方法的比较

HINT 为组 ICA 实现了一种新的多级概率 ICA 模型。它提供了一个统计原理的 ICA 建模框架，用于研究协变量对大脑网络的影响。HINT还可以为用户指定的主题组生成和可视化基于模型的网络估计，这极大地方便了组间比较。

结果

我们使用功能磁共振成像示例数据演示了 HINT 的步骤和功能，以估计对大脑网络的治疗效果，同时控制其他协变量。结果显示了治疗组和对照组之间估计的大脑网络和基于模型的比较。在使用合成功能磁共振成像数据的比较中，HINT 在检测网络中的群体差异（尤其是小样本量）方面显示出理想的统计能力，同时保持较低的误报率。与一些最先进的群体 ICA 方法相比，HINT 还展示了在重建群体和个体水平源信号图方面相似或更高的准确性。

结论

HINT 可以为统计和神经科学研究人员提供有用的工具来评估和测试受试者组之间大脑网络的差异。