Fang Jun, Hu Xintao, Han Junwei, Jiang Xi, Zhu Dajiang, Guo Lei, Liu Tianming
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Brain Imaging Behav. 2015 Jun;9(2):162-77. doi: 10.1007/s11682-014-9293-0.
Natural stimulus functional magnetic resonance imaging (N-fMRI) such as fMRI acquired when participants were watching video streams or listening to audio streams has been increasingly used to investigate functional mechanisms of the human brain in recent years. One of the fundamental challenges in functional brain mapping based on N-fMRI is to model the brain's functional responses to continuous, naturalistic and dynamic natural stimuli. To address this challenge, in this paper we present a data-driven approach to exploring functional interactions in the human brain during free listening to music and speech streams. Specifically, we model the brain responses using N-fMRI by measuring the functional interactions on large-scale brain networks with intrinsically established structural correspondence, and perform music and speech classification tasks to guide the systematic identification of consistent and discriminative functional interactions when multiple subjects were listening music and speech in multiple categories. The underlying premise is that the functional interactions derived from N-fMRI data of multiple subjects should exhibit both consistency and discriminability. Our experimental results show that a variety of brain systems including attention, memory, auditory/language, emotion, and action networks are among the most relevant brain systems involved in classic music, pop music and speech differentiation. Our study provides an alternative approach to investigating the human brain's mechanism in comprehension of complex natural music and speech.
自然刺激功能磁共振成像（N-fMRI），例如参与者观看视频流或听音频流时采集的功能磁共振成像，近年来越来越多地用于研究人类大脑的功能机制。基于N-fMRI进行脑功能图谱绘制的一个基本挑战是对大脑对连续、自然主义和动态自然刺激的功能反应进行建模。为应对这一挑战，在本文中，我们提出了一种数据驱动的方法，用于探索人类大脑在自由聆听音乐和语音流时的功能交互。具体而言，我们通过使用N-fMRI测量具有内在建立的结构对应关系的大规模脑网络上的功能交互来对大脑反应进行建模，并执行音乐和语音分类任务，以指导在多个受试者聆听多种类别的音乐和语音时系统识别一致且有区别的功能交互。潜在的前提是，从多个受试者的N-fMRI数据中得出的功能交互应同时表现出一致性和可区分性。我们的实验结果表明，包括注意力、记忆、听觉/语言、情感和行动网络在内的多种脑系统是参与古典音乐、流行音乐和语音区分的最相关脑系统。我们的研究为研究人类大脑理解复杂自然音乐和语音的机制提供了一种替代方法。
