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Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

机译:自由观看故事片期间与刺激相关的独立成分和人脑活动的体素明智分析

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摘要

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.
机译:了解大脑如何在丰富的自然环境中处理刺激是神经科学的基本目标。在这里,我们向血液动力学脑活动的功能磁共振成像(fMRI)过程中的10名健康志愿者展示了一部功能片。然后,我们注释了电影的听觉和视觉特征,以告知对血液动力学数据的分析。这些注解适用于通过独立成分分析(ICA)提取的体素数据和脑网络时程。听觉注释与揭示两个功能网络的两个独立组件(IC)相关,一个响应多种听觉刺激,另一个响应优先于语音,但网络的某些部分也响应非语言交流。视觉特征注释与四个IC相关联,根据它们对不同视觉刺激特征的敏感度来划定视觉区域。相比之下,基于体素的一般线性模型的单独分析显示,大脑区域优先响应声能,语音,音乐,视觉对比边缘,身体运动和手部运动,这与ICA揭示的结果大为重叠。基于IC和基于体素的分析结果之间的差异表明,对体素时间过程进行彻底的分析对于理解功能网络特定子区域的活动非常重要,而ICA是揭示有关功能连接性的新颖信息的有价值的工具,不需要通过预定义的模型进行解释。我们的研究结果鼓励在认知神经影像学中使用自然刺激和任务来研究大脑如何在丰富的自然环境中处理刺激。

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