Decoding power-spectral profiles from FMRI brain activities during naturalistic auditory experience
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Recent studies have demonstrated a close relationship between computational acoustic features and neural brain activities, and have largely advanced our understanding of auditory information processing in the human brain. Along this line, we proposed a multidisciplinary study to examine whether power spectral density (PSD) profiles can be decoded from brain activities during naturalistic auditory experience. The study was performed on a high resolution functional magnetic resonance imaging (fMRI) dataset acquired when participants freely listened to the audio-description of the movie “Forrest Gump”. Representative PSD profiles existing in the audio-movie were identified by clustering the audio samples according to their PSD descriptors. Support vector machine (SVM) classifiers were trained to differentiate the representative PSD profiles using corresponding fMRI brain activities. Based on PSD profile decoding, we explored how the neural decodability correlated to power intensity and frequency deviants. Our experimental results demonstrated that PSD profiles can be reliably decoded from brain activities. We also suggested a sigmoidal relationship between the neural decodability and power intensity deviants of PSD profiles. Our study in addition substantiates the feasibility and advantage of naturalistic paradigm for studying neural encoding of complex auditory information.
KeywordsPower-spectral profile fMRI brain decoding Auditory intensity-encoding Frequency-encoding Naturalistic paradigm
Compliance with ethical standards
This study was funded by National Natural Science Foundation of China (NSFC) 61103061, 61333017, 61473234 and 61522207, and the Fundamental Research Funds for the Central Universities 3102014JCQ01065.
Conflict of Interest
All co-authors have seen and agreed with the contents of the manuscript. We have no relevant conflicts of interest.
This article does not contain any studies with human participants performed by any of the authors.
- Bilecen D, Seifritz E, Scheffler K, Henning J, AC S (2002) Amplitopicity of the human auditory cortex: an fMRI study. NeuroImage 17 (2):710–718.Google Scholar
- Cong, F., Alluri, V., Nandi, A. K., Toiviainen, P., Rui, F., Abu-Jamous, B., Gong, L., Craenen, B. G. W., Poikonen, H., & Huotilainen, M. (2013). Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features. Multimedia IEEE Transactions on, 15(5), 1060–1069.CrossRefGoogle Scholar
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.Google Scholar
- Hu, X., Lv, C., Cheng, G., Lv, J., Guo, L., Han, J., & Liu, T. (2015). Sparsity-constrained fMRI decoding of visual saliency in naturalistic video streams. Autonomous Mental Development, IEEE Transactions on 7, 2, 65–75.Google Scholar
- Proakis, J. G., & Manolakis, D. G. (1992). Digital signal processing: Principles, algorithms, and applications. Maxwell Macmillan Canada, Maxwell Macmillan International: Macmillan.Google Scholar
- Toiviainen, P., Alluri, V., Brattico, E., Wallentin, M., & Vuust, P. (2013). Capturing the musical brain with lasso: dynamic decoding of musical features from fMRI data. NeuroImage, 88C, 170–180.Google Scholar
- Zhao S, Jiang X, Han J, Hu X, Zhu D, Lv J, Zhang T, Guo L, Liu T (2014) Decoding auditory saliency from FMRI brain imaging. Paper presented at the proceedings of the ACM international conference on multimedia, Orlando, Florida, USA.Google Scholar