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Coupling of Action-Perception Brain Networks during Musical Pulse Processing : Evidence from Region-of-Interest-Based Independent Component Analysis

机译:音乐脉搏处理过程中动作知觉大脑网络的耦合:来自基于兴趣区域的独立成分分析的证据

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摘要

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellarstructures. Although functional connectivity studies have advanced our understandingof rhythm perception, this phenomenon has not been sufficiently studied as a function ofmusical training and beyond the General Linear Model (GLM) approach. Here, we studiedpulse clarity processing during naturalistic music listening using a data-driven approach(independent component analysis; ICA). Participants’ (18 musicians and 18 controls)functional magnetic resonance imaging (fMRI) responses were acquired while listeningto music. A targeted region of interest (ROI) related to pulse clarity processing wasdefined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICAdecomposition was performed under different model orders, i.e., under a varying numberof assumed independent sources, to avoid relying on prior model order assumptions. Thecomponents best predicted by a measure of the pulse clarity of the music, extractedcomputationally from the musical stimulus, were identified. Their corresponding spatialmaps uncovered a network of auditory (perception) and motor (action) areas in anexcitatory-inhibitory relationship at lower model orders, while mainly constrained to theauditory areas at higher model orders. Results revealed (a) a strengthened functionalintegration of action-perception networks associated with pulse clarity perception hiddenfrom GLM analyses, and (b) group differences between musicians and non-musicians inpulse clarity processing, suggesting lifelong musical training as an important factor thatmay influence beat processing.
机译:我们的节奏感取决于几个大脑和小脑结构的协调活动。尽管功能连接性研究提高了我们对节奏感知的理解,但这种现象尚未作为音乐训练的功能得到充分研究,超出了通用线性模型(GLM)的方法。在这里,我们使用数据驱动的方法(独立分量分析; ICA)研究了自然音乐收听过程中的脉冲清晰度处理。在听音乐的同时,参与者(18位音乐家和18位控制者)获得了功能性磁共振成像(fMRI)反应。定义了与脉冲清晰度处理相关的目标感兴趣区域(ROI),包括听觉,躯体运动,基底神经节和小脑区域。 ICA分解是在不同的模型顺序下进行的,即在不同数量的假定独立来源下进行,以避免依赖先前的模型顺序假设。确定了通过从音乐刺激中算出的,通过测量音乐的脉冲清晰度来最好地预测的成分。他们对应的空间图揭示了模型较低阶的兴奋抑制关系中的听觉(感知)和运动(动作)区域的网络,而主要限于模型较高阶的听觉区域。结果表明(a)与GLM分析隐藏的与脉搏清晰度感知相关的动作感知网络的功能集成得到了增强,并且(b)音乐家与非音乐家之间的群体差异导致了脉搏清晰度处理,这表明终身音乐训练是可能影响节奏处理的重要因素。

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