Brain and Mind Laboratory, Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, Espoo, Finland.
PLoS One. 2012;7(4):e35215. doi: 10.1371/journal.pone.0035215. Epub 2012 Apr 5.
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.
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一个基本目标。在这里，我们在功能性磁共振成像（fMRI）的血流动力学脑活动中向 10 名健康志愿者放映了一部故事片。然后，我们将电影的听觉和视觉特征注释为血流动力学数据分析提供信息。这些注释既适合体素级数据，也适合独立成分分析（ICA）提取的脑网络时间过程。听觉注释与两个独立成分（IC）相关，揭示了两个功能网络，一个对各种听觉刺激有反应，另一个对言语有反应，但网络的一部分也对非言语交流有反应。视觉特征注释与四个 IC 相关，根据其对不同视觉刺激特征的敏感性来描绘视觉区域。相比之下，基于单独体素的广义线性模型分析揭示了大脑区域优先对声音能量、言语、音乐、视觉对比度边缘、身体运动和手部运动做出反应，这些结果与 ICA 揭示的结果大部分重叠。IC 和基于体素的分析结果之间的差异表明，对体素时间过程的彻底分析对于理解功能网络特定子区域的活动很重要，而 ICA 是揭示关于功能连接的新信息的有用工具，这些信息不一定需要用预定义的模型来解释。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。
