标题： 多目标认知模型：多主题fMRI分析的监督方法 显示英文标题

标题： Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis

摘要： 为了破译人脑，多变量模式（MVP）分类通过使用功能磁共振成像（fMRI）数据集生成认知模型。作为MVP分析的标准流程，多受试者fMRI数据集中的大脑模式必须映射到共享空间，然后通过使用映射后的模式生成分类模型。然而，由于标准流程使用独立的步骤来生成这些模型，MVP模型可能无法在一个新的fMRI数据集上提供稳定性能。事实上，管道中的每个步骤都包括一个具有独立优化方法的目标函数，其中每个步骤的最佳解可能不是下一阶段的最优解。为了解决上述问题，本文介绍了多目标认知模型（MOCM），它利用集成的目标函数来进行MVP分析，而不是仅仅使用那些独立的步骤。为了解决集成问题，我们提出了一种定制的多目标优化方法，首先生成所有可能的解，然后我们的方法对解进行排序并选择鲁棒解作为最终结果。实证研究表明，所提出的方法与其他技术相比可以产生更优的性能。 显示英文摘要

摘要： In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces the Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.