After experiencing the same episode, some people can recall certain details about it, whereas others cannot. We investigate how common (intersubject) neural patterns during memory encoding influence whether an episode will be subsequently remembered, and how divergence from a common organization is associated with encoding failure. Using functional magnetic resonance imaging with intersubject multivariate analyses, we measured brain activity as people viewed episodes within wildlife videos and then assessed their memory for these episodes. During encoding, greater neural similarity was observed between the people who later remembered an episode (compared with those who did not) within the regions of the declarative memory network (hippocampus, posterior medial cortex [PMC], and dorsal Default Mode Network [dDMN]). The intersubject similarity of the PMC and dDMN was episode-specific. Hippocampal encoding patterns were also more similar between subjects for memory success that was defined after one day, compared with immediately after retrieval. The neural encoding patterns were sufficiently robust and generalizable to train machine learning classifiers to predict future recall success in held-out subjects, and a subset of decodable regions formed a network of shared classifier predictions of subsequent memory success. This work suggests that common neural patterns reflect successful, rather than unsuccessful, encoding across individuals.

中文翻译：

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在成功的记忆编码期间，个体之间的神经模式比在失败的记忆编码期间更相似。

经历过同样的情节后，有些人可以回忆起它的某些细节，而有些人则不能。我们研究了记忆编码过程中常见的（受试者间）神经模式如何影响一个情节是否会被记住，以及与一个共同组织的分歧如何与编码失败相关联。使用功能性磁共振成像和受试者间多变量分析，我们测量了人们在观看野生动物视频中的情节时的大脑活动，然后评估他们对这些情节的记忆。在编码过程中，在陈述性记忆网络区域（海马、后内侧皮层 [PMC] 和背侧默认模式网络 [dDMN]）内，后来记得某个情节的人（与那些没有记住的人相比）之间观察到了更大的神经相似性）。PMC 和 dDMN 的受试者间相似性是特定于情节的。与检索后立即相比，一天后定义的记忆成功受试者之间的海马编码模式也更加相似。神经编码模式足够健壮和泛化，可以训练机器学习分类器来预测留存对象未来的回忆成功，并且可解码区域的子集形成了一个共享分类器预测后续记忆成功的网络。这项工作表明，常见的神经模式反映了个人之间成功的编码，而不是不成功的编码。神经编码模式足够健壮和泛化，可以训练机器学习分类器来预测留存对象未来的回忆成功，并且可解码区域的子集形成了一个共享分类器预测后续记忆成功的网络。这项工作表明，常见的神经模式反映了个人之间成功的编码，而不是不成功的编码。神经编码模式足够健壮和泛化，可以训练机器学习分类器来预测留存对象未来的回忆成功，并且可解码区域的子集形成了一个共享分类器预测后续记忆成功的网络。这项工作表明，常见的神经模式反映了个人之间成功的编码，而不是不成功的编码。