The successful memory process produces specific activity in the brain network. As the brain activity of the prestimulus and encoding phases has a crucial effect on subsequent memory outcomes (e.g., remembered or forgotten), previous studies have tried to predict the memory performance in this period. Conventional studies have used the spectral power or event-related potential of specific regions as the classification feature. However, as multiple brain regions work collaboratively to process memory, it could be a better option to use functional connectivity within the memory-related brain network to predict subsequent memory performance. In this study, we acquired the EEG signals while performing an associative memory task that remembers scene–word pairs. For the connectivity analysis, we estimated the cross–mutual information within the default mode network with the time–frequency spectra at the prestimulus and encoding phases. Then, we predicted the success or failure of subsequent memory outcome with the connectivity features. We found that the classifier with support vector machine achieved the highest classification accuracy of 80.83% ± 12.65% (mean ± standard deviation) using the beta (13–30 Hz) connectivity at encoding phase among the multiple frequency bands and task phases. Using the prestimulus beta connectivity, the classification accuracy of 72.45% ± 12.52% is also achieved. Among the features, the connectivity related to the dorsomedial prefrontal cortex was found to contribute to successful memory encoding. The connectivity related to the posterior cingulate cortex was found to contribute to the failure of memory encoding. The present study showed for the first time the successful prediction with high accuracy of subsequent memory outcome using single-trial functional connectivity.

中文翻译：

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编码前和编码期间默认模式网络的单次试验脑电图连接预测后续记忆结果

成功的记忆过程会在大脑网络中产生特定的活动。由于刺激前和编码阶段的大脑活动对随后的记忆结果（例如，记住或忘记）具有至关重要的影响，以前的研究试图预测这一时期的记忆表现。常规研究使用特定区域的光谱功率或事件相关电位作为分类特征。然而，由于多个大脑区域协同工作来处理记忆，因此使用与记忆相关的大脑网络内的功能连接来预测随后的记忆表现可能是更好的选择。在这项研究中，我们在执行记住场景-单词对的联想记忆任务时获取了 EEG 信号。对于连通性分析，我们用预刺激和编码阶段的时间-频谱估计了默认模式网络中的互互信息。然后，我们用连通性特征预测了后续记忆结果的成功或失败。我们发现带有支持向量机的分类器在多个频段和任务阶段之间的编码阶段使用 beta (13-30 Hz) 连通性实现了最高的分类准确率 80.83% ± 12.65%（平均值 ± 标准偏差）。使用预刺激 beta 连接，也实现了 72.45% ± 12.52% 的分类精度。在这些特征中，发现与背内侧前额叶皮层相关的连通性有助于成功的记忆编码。发现与后扣带回皮层相关的连接导致记忆编码失败。本研究首次显示了使用单次试验功能连接对后续记忆结果进行高精度预测的成功。