Listening to real-world sounds: fMRI data for analyzing connectivity networks

Kuo, Po-Chih; Suen, Summit; Tseng, Yi-Li; Lee, Juin-Der; Zilles, Karl; Eickhoff, Simon B.; Cheng, Philip E.; Liou, Michelle

doi:10.1016/j.dib.2019.104411

Journal Article

FZJ-2019-05266

Listening to real-world sounds: fMRI data for analyzing connectivity networks

Kuo, P.-C. ; Tseng, Y.-L. ; Zilles, K.FZJ* ; Suen, S. ; Eickhoff, S. B.FZJ* ; Lee, J.-D. ; Cheng, P. E. ; Liou, M. (Corresponding author)

2019
Elsevier Amsterdam [u.a.]

Data in Brief 26, 104411 (2019) [10.1016/j.dib.2019.104411]

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Please use a persistent id in citations: http://hdl.handle.net/2128/23455 doi:10.1016/j.dib.2019.104411

Abstract: There is a growing interest in functional magnetic resonance imaging (fMRI) studies on connectivity networks in the brain when subjects are under exposure to natural sensory stimulation. Because of a complicated coupling between spontaneous and evoked brain activity under real-world stimulation, there is no critical mapping between the experimental inputs and corresponding brain responses. The dataset contains auditory fMRI scans and T1-weighted anatomical scans acquired under eyes-closed and eyes-open conditions. Within each scanning condition, the subject was presented 12 different sound clips, including human voices followed by animal vocalizations. The dataset is meant to be used to assess brain dynamics and connectivity networks under natural sound stimulation; it also allows for empirical investigation of changes in fMRI responses between eyes-closed and eyes-open conditions, between animal vocalizations and human voices, as well as between the 12 different sound clips during auditory stimulation. The dataset is a supplement to the research findings in the paper “Brain dynamics and connectivity networks under natural auditory stimulation” published in NeuroImage.

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ddc:570

Note: We are indebted to the Research Center for Mind, Brain & Learning at the National Chengchi University for the great assistance during fMRI data acquisition. This research was supported by grants MOST-105-2410-H-001-036 and MOST-106-2410-H-001-026 from the Ministry of Science and Technology, Taiwan, and by funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 (HBP SGA2).

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Appears in the scientific report 2019

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