We show that fMRI analysis using machine learning tools are sufficient to distinguish valence (i.e., positive or negative) of freely retrieved autobiographical memories in a cross-participant setting. Our methodology uses feature selection (ReliefF) in combination with boosting methods, both applied directly to data represented in voxel space. In previous work using the same data set, Nawa and Ando showed that whole-brain based classification could achieve above-chance classification accuracy only when both training and testing data came from the same individual. In a cross-participant setting, classification results were not statistically significant. Additionally, on average the classification accuracy obtained when using ReliefF is substantially higher than previous results - 81% for the within-participant classification, and 62% for the cross-participant classification. Furthermore, since features are defined in voxel space, it is possible to show brain maps indicating the regions of that are most relevant in determining the results of the classification. Interestingly, the voxels that were selected using the proposed computational pipeline seem to be consistent with current neurophysiological theories regarding the brain regions actively involved in autobiographical memory processes.

中文翻译：

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从 fMRI 数据中对自传体记忆的效价进行分类

我们表明，使用机器学习工具进行的 fMRI 分析足以区分在跨参与者设置中自由检索的自传体记忆的效价（即正面或负面）。我们的方法将特征选择 (ReliefF) 与增强方法结合使用，两者都直接应用于体素空间中表示的数据。在之前使用相同数据集的工作中，Nawa 和 Ando 表明，只有当训练和测试数据来自同一个人时，基于全脑的分类才能实现高于机会的分类准确率。在跨参与者设置中，分类结果没有统计学意义。此外，平均而言，使用 ReliefF 时获得的分类准确度大大高于之前的结果——参与者内分类为 81%，62% 用于交叉参与者分类。此外，由于特征是在体素空间中定义的，因此可以显示大脑图，指示与确定分类结果最相关的区域。有趣的是，使用提议的计算管道选择的体素似乎与当前关于积极参与自传体记忆过程的大脑区域的神经生理学理论一致。