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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.

A manually denoised audio-visual movie watching fMRI dataset for the studyforrest project