论文标题
模块化允许在音乐和语音感知期间对人脑网络进行分类
Modularity allows classification of human brain networks during music and speech perception
论文作者
论文摘要
我们研究了模块化作为全脑功能网络的量词的使用。大脑网络是由功能性磁共振成像构建的,而受试者听取了情绪和文化熟悉程度各不相同的听觉作品。我们的分析结果揭示了基于受试者喜欢的歌曲期间网络配置的高模块化组,并且此分类可以预测其他听觉作品期间的网络重新配置。特别是,低模块化组中的受试者在熟悉和不熟悉的作品中都显示出明显的大脑网络重新配置。相比之下,高模块化大脑网络看起来更强大,并且在陌生的音乐和言语中仅显示出重大变化。在每个听觉作品中,我们还发现两组模块组成的稳定性差异。我们的结果表明,整个脑网络的模块化在不同听觉处理需求期间的网络重新配置的方式中起着重要作用,因此它可能有助于在治疗音乐参与过程中神经可塑性能力的个体差异。
We investigate the use of modularity as a quantifier of whole-brain functional networks. Brain networks are constructed from functional magnetic resonance imaging while subjects listened to auditory pieces that varied in emotivity and cultural familiarity. The results of our analysis reveal high and low modularity groups based on the network configuration during a subject's favorite song, and this classification can predict network reconfiguration during the other auditory pieces. In particular, subjects in the low modularity group show significant brain network reconfiguration during both familiar and unfamiliar pieces. In contrast, the high modularity brain networks appear more robust and only exhibit significant changes during the unfamiliar music and speech. We also find differences in the stability of module composition for the two groups during each auditory piece. Our results suggest that the modularity of the whole-brain network plays a significant role in the way the network reconfigures during varying auditory processing demands, and it may therefore contribute to individual differences in neuroplasticity capability during therapeutic music engagement.