Kansas State University, Manhattan, Kansas, USA.
Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, California, USA.
Hum Brain Mapp. 2024 Feb 1;45(2):e26572. doi: 10.1002/hbm.26572.
Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.
tau 节律主要由声音响应的 alpha 频段（~8-13Hz）振荡定义，这些振荡主要在颞上回的听觉区域产生。tau 的研究主要采用脑磁图或颅内记录，因为 tau 在脑电图中难以捉摸。在这里，我们证明独立成分分析（ICA）分解可以是一种有效识别 tau 源并研究脑电图记录中 tau 源活动的方法。研究对象（N=18）在被动暴露于复杂声音刺激的同时，从头皮上的 68 个电极记录脑电图。研究对象的数据被分为 60 个并行处理管道，涉及使用 5 个高通滤波器级别（通带为 0.1、0.5、1、2 和 4Hz）、3 个低通滤波器级别（25、50 和 100Hz）和 4 种不同的 ICA 算法（FastICA、Infomax、自适应混合 ICA[AMICA]和多模型 AMICA[mAMICA]）。从这些数据中识别出与 tau 相关的独立成分（IC）过程，这些过程定位于颞上回附近，频谱峰值在 8-13Hz 的 alpha 频段。这些“tau ICs”在声音呈现期间表现出 alpha 抑制，而在其他常见的 alpha 频段频谱峰值观察到的 IC 簇（例如与躯体运动 mu 和顶叶或枕叶 alpha 相关的那些）中则没有观察到。分析参数的选择会影响从 ICA 分解中获得 tau IC 的可能性。高通滤波器的较低截止频率导致显示 tau IC 的受试者明显少于更激进的高通滤波。在这方面，FastICA 算法的分解表现最差，而 mAMICA 表现最好。滤波器和 ICA 模型选择的最佳组合能够在大约 94%的样本数据中识别至少一个 tau IC。总的来说，数据显示 tau EEG IC 动力学与 MEG 和颅内数据中观察到的 tau 动力学非常相似。使用相对激进的高通滤波器和 mAMICA 分解应该可以让研究人员在大多数研究对象中识别和描述 tau 节律。我们相信，采用 ICA 分解方法进行脑电图分析可以提高与听觉响应 tau 节律相关的发现的速度和范围。
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