利用跨脑节律的功能分离作为在音乐收听期间检测EEG振荡复用的手段
Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening
摘要：
音乐是一种在多个时间尺度上发展的多方面刺激，它以多种方式调节大脑功能，不仅包括听觉感知的不同阶段，还包括记忆和评价等更高的认知过程。网络理论显然是一种很有前途的方法来描述音乐听时脑振荡动力学的功能重组。然而，迄今为止，音乐引起的变化已经在孤立的大脑节律的功能边界内进行了研究。使用自然主义的音乐，我们检测到的功能分离模式与不同的皮层节奏，因为这些都反映在表面脑电图测量。出现的结构进行了比较，跨频带的节奏之间的相互作用进行量化。并将其与安静和噪声条件下的结构进行对比，以揭示音乐听觉产生的具体成分。我们的方法包括一个有效的图分区算法，这是进一步利用挖掘原型模块化模式，和一种新的算法程序，用于识别交换节点，始终改变模块在音乐听。我们的研究结果表明音乐诱导的功能重组具有多重特征，特别是δ和βH节律重建的网络之间的依赖性。这种依赖性在嵌套神经振荡的框架内得到进一步证明，并且在最近引入的音乐皮质夹带的背景下完全适合。考虑到其计算效率，并结合原位脑电描记术的灵活性，它可能会为现实生活中的应用带来新型辅助工具。
英文摘要：
Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms. Using naturalistic music, we detected the functional segregation patterns associated with different cortical rhythms, as these were reflected in the surface EEG measurements. The emerged structure was compared across frequency bands to quantify the interplay among rhythms. It was also contrasted against the structure from the rest and noise listening conditions to reveal the specific components stemming from music listening. Our methodology includes an efficient graph-partitioning algorithm, which is further utilized for mining prototypical modular patterns, and a novel algorithmic procedure for identifying switching nodes that consistently change module during music listening. Our results suggest the multiplex character of the music-induced functional reorganization and particularly indicate the dependence between the networks reconstructed from the δ and βH rhythms. This dependence is further justified within the framework of nested neural oscillations and fits perfectly within the context of recently introduced cortical entrainment to music. Considering its computational efficiency, and in conjunction with the flexibility of in situ electroencephalography, it may lead to novel assistive tools for real-life applications.
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