The general linear model (GLM) is a popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. The recently proposed cortical surface-based spatial Bayesian GLM leverages spatial dependencies among neighboring cortical vertices to produce smoother and more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-session analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to use with the BayesfMRI R package.
翻译:普通线性模型(GLM)是一个广受欢迎的方便工具,用于估计大脑功能反应和确定任务或刺激期间显著激活的领域。然而,古典GLM是基于大规模单向法方法,不能利用相邻大脑位置的类似激活模式。结果,它往往产生噪音估计数,不足以探测重大激活,特别是在个别对象和小群体中。最近提议的表面空间贝叶斯基GLM利用邻近的螺旋性脑反应和确定在任务或刺激期间显著激活的领域之间的空间依赖性空间脊椎脊椎,以产生更顺畅和更准确的估计数和功能激活领域。空间巴伊西亚GLMMM方法可以应用于个人和群体层面的类似活动模式分析。我们评估个体和群体平均任务激活措施的可靠性和力量,特别是在个别人际连接项目(HCP)45个主题和HCPResticality Speality Speciality Speciality messional),我们还将模型推广到多会期分析,并运用特定的表面的小型表面,而不是地面的精确度分析。在高空基质的地面上,我们能够向一个高端测测测测测测测测测地的地面的地的地面上。