Department of Biomedical Engineering, Shahed University, Tehran, Iran.
Department of Pharmacology and Physiology, University of Montreal, Quebec, Canada.
Med Biol Eng Comput. 2021 Mar;59(3):575-588. doi: 10.1007/s11517-020-02304-8. Epub 2021 Feb 9.
Human memory retrieval is one of the brain's most important, and least understood cognitive mechanisms. Traditionally, research on this aspect of memory has focused on the contributions of particular brain regions to recognition responses, but the interaction between regions may be of even greater importance to a full understanding. In this study, we examined patterns of network connectivity during retrieval in a recognition memory task. We estimated connectivity between brain regions from electroencephalographic signals recorded from twenty healthy subjects. A multivariate autoregressive model (MVAR) was used to determine the Granger causality to estimate the effective connectivity in the time-frequency domain. We used GPDC and dDTF methods because they have almost resolved the previous volume conduction and bivariate problems faced by previous estimation methods. Results show enhanced global connectivity in the theta and gamma bands on target trials relative to lure trials. Connectivity within and between the brain's hemispheres may be related to correct rejection. The left frontal signature appears to have a crucial role in recollection. Theta- and gamma-specific connectivity patterns between temporal, parietal, and frontal cortex may disclose the retrieval mechanism. Old/new comparison resulted in different patterns of network connection. These results and other evidence emphasize the role of frequency-specific causal network interactions in the memory retrieval process. Graphical abstract a Schematic of processing workflow which is consists of pre-processing, sliding-window AMVAR modeling, connectivity estimation, and validation and group network analysis. b Co-registration between Geodesic Sensor Net. and 10-20 system, the arrows mention eight regions of interest (Left, Anterior, Inferior (LAI) and Right, Anterior, Inferior (RAI) and Left, Anterior, Superior (LAS) and Right, Anterior, Superior (RAS) and Left, Posterior, Inferior (LPI) and Right, Posterior, Inferior (RPI) and Left, Posterior, Superior (LPS) and Right, Posterior, Superior (RPS)).
人类记忆检索是大脑最重要但认知机制中理解最少的机制之一。传统上,对记忆这一方面的研究侧重于特定脑区对识别反应的贡献,但区域之间的相互作用可能对全面理解更为重要。在这项研究中,我们在识别记忆任务中检查了检索过程中的网络连接模式。我们从二十名健康受试者记录的脑电图信号中估计了脑区之间的连接。使用多元自回归模型 (MVAR) 确定格兰杰因果关系以估计时频域中的有效连接。我们使用 GPDC 和 dDTF 方法,因为它们几乎解决了之前估计方法面临的体积传导和双变量问题。结果表明,与诱饵试验相比,目标试验中θ和γ频段的全局连接增强。大脑半球内和半球间的连接可能与正确拒绝有关。左额叶特征似乎在回忆中起着关键作用。颞叶、顶叶和额叶皮质之间的θ和γ特定连接模式可能揭示了检索机制。新旧比较导致网络连接的不同模式。这些结果和其他证据强调了频率特定因果网络相互作用在记忆检索过程中的作用。