Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts
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ORIGINAL ARTICLE
Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts Shijie Zhao 1 & Junwei Han 1 & Xi Jiang 2 & Heng Huang 1 & Huan Liu 1 & Jinglei Lv 1 & Lei Guo 1 & Tianming Liu 2
# Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract In recent years, natural stimuli such as audio excerpts or video streams have received increasing attention in neuroimaging studies. Compared with conventional simple, idealized and repeated artificial stimuli, natural stimuli contain more unrepeated, dynamic and complex information that are more close to real-life. However, there is no direct correspondence between the stimuli and any sensory or cognitive functions of the brain, which makes it difficult to apply traditional hypothesis-driven analysis methods (e.g., the general linear model (GLM)). Moreover, traditional data-driven methods (e.g., independent component analysis (ICA)) lack quantitative modeling of stimuli, which may limit the power of analysis models. In this paper, we propose a sparse representation based decoding framework to explore the neural correlates between the computational audio features and functional brain activities under free listening conditions. First, we adopt a biologically-plausible auditory saliency feature to quantitatively model the audio excerpts and meanwhile develop sparse representation/dictionary learning method to learn an over-complete dictionary basis of brain activity patterns. Then, we reconstruct the auditory saliency features from the learned fMRI-derived dictionaries. After that, a group-wise analysis procedure is conducted to identify the associated brain regions and networks. Experiments showed that the auditory saliency feature can be well decoded from brain activity patterns by our methods, and the identified brain regions and networks are consistent and meaningful. At last, our method is evaluated and compared with ICA method and experimental results demonstrated the superiority of our methods. Keywords Natural stimuli . Sparse representation . Brain networks . Decoding . Auditory saliency
Introduction Traditionally, most of previous functional imaging studies have employed simple, idealized and repeated stimuli to study the brain functions in controlled laboratory environments (Hasson and Honey 2012; Bordier et al. 2013; Liu et al. 2014). Although this strategy works well, there are still some limitations. First, it is reported that naturalistic stimuli will evoke stronger neuronal responses than conventional * Junwei Han [email protected] * Tianming Liu [email protected] 1
School of Automation, Northwestern Polytechnical University, Xi’an, China
2
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
laboratory stimuli, like artificial stimuli (Mechler et al. 1998; Yao et al. 2007). Moreover, as pointed out in (Hasson and Honey 2012), these findings discovered under controlled laboratory cond
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