Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
J Neurosci Methods. 2020 Jul 15;341:108726. doi: 10.1016/j.jneumeth.2020.108726. Epub 2020 Apr 30.
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.
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.
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.
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.
HINT can provide a useful tool for both statistical and neuroscience researchers to evaluate and test differences in brain networks between subject groups.
独立成分分析（ICA）是神经科学研究中用于研究大脑组织的一种流行工具。在 fMRI 研究中，一个重要目标是研究大脑网络如何受到被试的临床和人口统计学变量的调节。现有的 ICA 方法和工具箱没有将被试的协变量效应纳入到大脑网络的 ICA 估计中，这可能导致在检测被试组之间的大脑网络差异时准确性和统计功效的损失。
我们引入了一个 Matlab 工具箱，HINT（分层独立成分分析工具箱），它提供了一种分层协变量调整的 ICA（hc-ICA），用于对协变量效应进行建模和检验，并在群体和个体水平上生成基于模型的大脑网络估计。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 算法监测模型估计，指定假设检验，并可视化结果。HINT 还具有命令行界面，允许用户使用脚本方便地运行和重现分析。
HINT 为组 ICA 实现了一个新的多层次概率 ICA 模型。它为研究协变量对大脑网络的影响提供了一个具有统计学原理的 ICA 建模框架。HINT 还可以为用户指定的被试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。
我们用 fMRI 示例数据演示了 HINT 的步骤和功能，以在控制其他协变量的情况下估计治疗对大脑网络的影响。结果展示了估计的大脑网络和治疗组与对照组之间的基于模型的比较。在使用合成 fMRI 数据的比较中，HINT 在检测网络中的组差异方面显示出令人满意的统计功效，特别是在小样本量的情况下，同时保持低的假阳性率。与一些最先进的组 ICA 方法相比，HINT 还在重建群体和个体水平的源信号图方面具有相似或更高的准确性。
HINT 可以为统计和神经科学研究人员提供一个有用的工具，用于评估和测试被试组之间的大脑网络差异。
