School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology 116024, Dalian, P. R. China.
Faculty of Information Technology, University of Jyväskylä 40014, Jyväskylä, Finland.
Int J Neural Syst. 2021 Mar;31(3):2150001. doi: 10.1142/S0129065721500015. Epub 2020 Dec 22.
To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.
为了研究音乐聆听中涉及的功能网络的电生理基础，最近基于空间独立成分分析（ICA）的先前方法已被用于持续的脑电图（EEG）和脑磁图（MEG）。然而，这些研究仅针对健康受试者，未能在音乐聆听期间进行组水平比较。在这里，我们将组水平空间傅里叶 ICA 与声学特征提取相结合，以实现音乐特征处理的特定频率脑网络的组比较。然后将其应用于健康受试者和重度抑郁症（MDD）受试者。通过个体傅里叶 ICA 成分时间历程与音乐特征之间的置换相关分析确定音乐诱导的振荡脑模式。我们发现：（1）在大多数健康受试者中，三个成分（包括β感觉运动网络、β听觉网络和α内侧视觉网络）参与音乐处理；并且（2）位于左角回的一个α外侧成分参与了大多数 MDD 个体的音乐感知。所提出的方法允许进行统计组比较，我们发现：（1）与 MDD 个体相比，健康受试者的α外侧成分激活更强；并且（2）与健康受试者相比，MDD 参与者的音乐特征处理的衍生频率相关网络似乎发生了改变。该流水线似乎可用于研究自然范式中精神障碍期间的脑振荡中断。
