Brain and Mind Institute, Western University, Canada; Department of Statistical and Actuarial Sciences, Western University, Canada; Department of Computer Science, Western University, Canada.
Brain and Mind Institute, Western University, Canada; Graduate School of Frontier Biosciences, Osaka University, Japan.
Neuroimage. 2018 Oct 15;180(Pt A):119-133. doi: 10.1016/j.neuroimage.2017.08.051. Epub 2017 Aug 24.
Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) are not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of the encoded feature spaces can be estimated from the data. We present here a number of different ways in which such flexible representational models can be specified, and how models of different complexity can be compared. We then provide a number of practical examples from our recent work in motor control, ranging from fixed models to more complex non-linear models of brain representations. The code for the fitting and cross-validation of representational models is provided in an open-source software toolbox.
表象模型指定了复杂的神经活动模式如何与视觉刺激、运动动作或抽象思维相关联。在这里，我们回顾了模式成分建模（PCM），这是一种用于评估此类模型的实用贝叶斯方法。与编码模型类似，PCM 评估模型预测新的大脑活动模式的能力。然而，与编码模型不同，PCM 并没有直接拟合各个条件下的单个体素的活动（活动分布）。相反，PCM 对代表模型指定的活动分布中的所有可能的活动分布进行积分，并计算数据在活动分布下的边际似然。通过使用边际似然的解析表达式，PCM 允许拟合灵活的表象模型，从中可以从数据中估计编码特征空间的相对强度和形式。在这里，我们提出了几种指定这种灵活表象模型的方法，以及如何比较不同复杂程度的模型。然后，我们提供了一些来自我们最近在运动控制方面的工作的实际示例，范围从固定模型到更复杂的大脑表象非线性模型。代表模型的拟合和交叉验证的代码在一个开源软件工具包中提供。
