Zeidman Peter, Kazan Samira M, Todd Nick, Weiskopf Nikolaus, Friston Karl J, Callaghan Martina F
Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Front Neurosci. 2019 Jan 10;12:986. doi: 10.3389/fnins.2018.00986. eCollection 2018.
In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol-and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.
在本技术说明中，我们探讨了神经影像统计学中一个尚未解决的挑战：如何确定几个数据集中哪一个最适合推断神经元反应。对于实验者在选择成像方案时以及新采集方法的开发者而言，此类比较至关重要。然而，一个数据集比另一个更好这一假设无法使用传统统计学（基于似然比）进行检验，因为这些方法要求在每个假设下数据是相同的。在此，我们提出贝叶斯数据比较（BDC），这是一个从神经元连接参数估计的精度以及竞争模型区分能力的角度评估功能成像数据质量的原则性框架。对于几个候选数据集中的每一个，使用贝叶斯（概率）正向模型（如一般线性模型（GLMs）或动态因果模型（DCMs））对神经元反应进行建模。接下来，使用贝叶斯GLM在组水平上汇总来自个体特异性模型的参数。然后，我们在此介绍的一系列度量用于根据（组水平）参数估计的精度和数据区分相似模型的能力来评估每个数据集。为举例说明该方法，我们比较了在一项评估多频段功能磁共振成像采集方案的研究中获取的四个数据集，并使用模拟来确定比较度量的表面效度。为使人们能够使用自己的数据和实验范式重现这些分析，我们通过SPM软件提供通用的Matlab代码。
