Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Neuroimage. 2010 Apr 15;50(3):1085-98. doi: 10.1016/j.neuroimage.2009.12.106. Epub 2010 Jan 4.
We present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems with distinct types of selectivity. The method characterizes these systems by their selectivity patterns and spatial maps, both estimated simultaneously via the EM algorithm. We demonstrate a corresponding method for group analysis that avoids the need for spatial correspondence among subjects. Consistency of the selectivity profiles across subjects provides a way to assess the validity of the discovered systems. We validate this model in the context of category selectivity in visual cortex, demonstrating good agreement with the findings based on prior hypothesis-driven methods.
我们提出了一种在 fMRI 数据中发现多刺激/任务实验选择性模式的方法。我们使用线性回归估计将数据表示为选择性的剖面，并采用混合模型密度估计来识别具有不同选择性类型的功能系统。该方法通过 EM 算法同时估计选择性模式和空间图来对这些系统进行特征描述。我们还展示了一种对应的组分析方法，该方法避免了对受试者间空间对应性的要求。选择性剖面在受试者间的一致性为评估所发现系统的有效性提供了一种方法。我们在视觉皮层的类别选择性背景下验证了该模型，结果与基于先前假设驱动方法的发现有很好的一致性。
