Beijing Key Laboratory of Applied Experimental Psychology, Beijing Normal University, Beijing, 100875, China.
Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
Sci Data. 2019 Nov 29;6(1):295. doi: 10.1038/s41597-019-0303-3.
The data presented here are related to the studyforrest project that uses the movie 'Forrest Gump' to map brain functions in a real-life context using functional magnetic resonance imaging (fMRI). However, neural-related fMRI signals are often small and confounded by various noise sources (i.e., artifacts) that makes searching for the signals induced by specific cognitive processes significantly challenging. To make neural-related signals stand out from the noise, the audio-visual movie watching fMRI dataset from the project was denoised by a combination of spatial independent component analysis and manual identification of signals or noise. Here, both the denoised data and the labeled decomposed components are shared to facilitate further study. Compared with the original data, the denoised data showed a substantial improvement in the temporal signal-to-noise ratio and provided a higher sensitivity in subsequent analyses such as in an inter-subject correlation analysis.
这里呈现的数据与研究 forest 项目有关，该项目使用电影《阿甘正传》（Forrest Gump），通过功能磁共振成像（fMRI）在真实环境中对大脑功能进行映射。然而，神经相关的 fMRI 信号通常较小，并且受到各种噪声源（即伪影）的干扰，这使得寻找特定认知过程所诱导的信号变得极具挑战性。为了使神经相关的信号从噪声中凸显出来，该项目的视听电影观看 fMRI 数据集通过空间独立成分分析和信号或噪声的手动识别相结合进行了去噪。在此，去噪后的数据和标记的分解成分都被共享，以方便进一步研究。与原始数据相比，去噪后的数据在时间信号噪声比方面有了显著提高，并在后续分析中（如在受试者间相关性分析中）提供了更高的灵敏度。
Zotero 插件
