Marquand Andre F, Brammer Michael, Williams Steven C R, Doyle Orla M
Department of Neuroimaging, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, United Kingdom.
Department of Neuroimaging, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, United Kingdom.
Neuroimage. 2014 May 15;92(100):298-311. doi: 10.1016/j.neuroimage.2014.02.008. Epub 2014 Feb 13.
Decoding models based on pattern recognition (PR) are becoming increasingly important tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding approaches that use hierarchical models to capture inter-subject variability, inter-subject differences are not typically handled efficiently in PR. In this work, we propose to overcome this problem by recasting the decoding problem in a multi-task learning (MTL) framework. In MTL, a single PR model is used to learn different but related "tasks" simultaneously. The primary advantage of MTL is that it makes more efficient use of the data available and leads to more accurate models by making use of the relationships between tasks. In this work, we construct MTL models where each subject is modelled by a separate task. We use a flexible covariance structure to model the relationships between tasks and induce coupling between them using Gaussian process priors. We present an MTL method for classification problems and demonstrate a novel mapping method suitable for PR models. We apply these MTL approaches to classifying many different contrasts in a publicly available fMRI dataset and show that the proposed MTL methods produce higher decoding accuracy and more consistent discriminative activity patterns than currently used techniques. Our results demonstrate that MTL provides a promising method for multi-subject decoding studies by focusing on the commonalities between a group of subjects rather than the idiosyncratic properties of different subjects.
基于模式识别（PR）的解码模型正日益成为神经影像数据分析的重要工具。与使用分层模型来捕捉个体间变异性的替代（大规模单变量）编码方法不同，在PR中，个体间差异通常无法得到有效处理。在这项工作中，我们建议通过在多任务学习（MTL）框架中重新构建解码问题来克服这一问题。在MTL中，单个PR模型用于同时学习不同但相关的“任务”。MTL的主要优点在于它能更有效地利用可用数据，并通过利用任务之间的关系得出更准确的模型。在这项工作中，我们构建MTL模型，其中每个个体由一个单独的任务来建模。我们使用灵活的协方差结构对任务之间的关系进行建模，并使用高斯过程先验来诱导它们之间的耦合。我们提出一种用于分类问题的MTL方法，并展示一种适用于PR模型的新颖映射方法。我们将这些MTL方法应用于对一个公开可用的功能磁共振成像（fMRI）数据集中的许多不同对比进行分类，并表明所提出的MTL方法比目前使用的技术产生更高的解码准确率和更一致的判别活动模式。我们的结果表明，MTL通过关注一组个体之间的共性而非不同个体的特质，为多主体解码研究提供了一种很有前景的方法。
