In this paper, a novel dictionary learning (DL) method is proposed to estimate sparse neural activations from multi-subject fMRI data sets. By exploiting the label information such as the patient and the normal healthy groups, the activation maps that are commonly shared across the groups as well as those that can explain the group differences are both captured. The proposed method was tested using real fMRI data sets consisting of schizophrenic subjects and healthy controls. The DL approach not only reproduced most of the maps obtained from the conventional independent component analysis (ICA), but also identified more maps that are significantly group-different, including a number of novel ones that were not revealed by ICA. The stability analysis of the DL method and the correlation analysis with separate neuropsychological test scores further strengthen the validity of our analysis.

中文翻译：

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基于字典学习的 fMRI 数据分析，用于捕获常见和个体神经激活图

在本文中，提出了一种新的字典学习 (DL) 方法来估计多主题 fMRI 数据集中的稀疏神经激活。通过利用患者和正常健康组等标签信息，可以捕获组间共享的激活图以及可以解释组差异的激活图。使用由精神分裂症受试者和健康对照组成的真实 fMRI 数据集对所提出的方法进行了测试。DL 方法不仅复制了从传统独立成分分析 (ICA) 获得的大部分地图，而且还识别了更多具有显着组差异的地图，包括许多 ICA 未揭示的新地图。