Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by different cognitive states. This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality — such as whole-brain images. Unlike the previous methods, DRSL is not limited by a linear transformation or a restricted fixed nonlinear kernel function — such as Gaussian kernel. DRSL utilizes a multi-layer neural network for mapping neural responses to linear space, where this network can implement a customized nonlinear transformation for each subject separately. Furthermore, utilizing a gradient-based optimization in DRSL can significantly reduce runtime of analysis on large datasets because it uses a batch of samples in each iteration rather than all neural responses to find an optimal solution. Empirical studies on multi-subject fMRI datasets with various tasks — including visual stimuli, decision making, flavor, and working memory — confirm that the proposed method achieves superior performance to other state-of-the-art RSA algorithms.

相似性分析是大多数 fMRI 研究中的关键步骤之一。表征相似性分析 (RSA) 可以测量由不同认知状态生成的神经特征的相似性。本文开发了深度表征相似性学习 (DRSL)，这是 RSA 的深度扩展，适用于分析具有大量主题和高维（例如全脑图像）的 fMRI 数据集中各种认知任务之间的相似性。与以前的方法不同，DRSL 不受线性变换或受限制的固定非线性核函数（例如高斯核）的限制。DRSL 利用多层神经网络将神经反应映射到线性空间，该网络可以为每个主题分别实现定制的非线性变换。此外，在 DRSL 中利用基于梯度的优化可以显着减少对大型数据集的分析运行时间，因为它在每次迭代中使用一批样本而不是所有神经响应来寻找最佳解决方案。对具有各种任务（包括视觉刺激、决策、风味和工作记忆）的多主题 fMRI 数据集的实证研究证实，所提出的方法比其他最先进的 RSA 算法具有更优越的性能。