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首页> 外文期刊>Neuroinformatics >Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts
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Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts

机译：在自由听取自然音频摘录中解码脑活动模式的听觉显着性

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摘要

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.
机译：近年来，自然刺激如音频摘录或视频流已经受到神经影像研究的增加。与传统的简单，理想化和反复的人工刺激相比，自然刺激含有更加接近现实生活的更加不重复的，动态和复杂的信息。然而，刺激与大脑的任何感官或认知功能之间没有直接对应，这使得难以应用传统的假设驱动的分析方法（例如，通用线性模型（GLM））。此外，传统的数据驱动方法（例如，独立分量分析（ICA））缺乏刺激的定量建模，这可能限制了分析模型的力量。在本文中，我们提出了一种基于稀疏表示的解码框架，以探讨在自由收听条件下计算音频特征和功能性大脑活动之间的神经相关性。首先，我们采用生物合理的听觉显着特征来定量模型音频摘录，同时开发稀疏表示/字典学习方法学习一个完整的大脑活动模式的字典基础。然后，我们从学习的FMRI衍生词典重建了听觉显着特征。之后，进行组织明智分析程序以识别相关的大脑区域和网络。实验表明，通过我们的方法可以从脑活动模式中解码听觉显着特征，并且所识别的大脑区域和网络是一致的和有意义的。最后，我们的方法被评估并与ICA方法进行了比较，实验结果表明了我们的方法的优越性。

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  • 来源
    《Neuroinformatics》 |2018年第4期|共16页
  • 作者

    Shijie Zhao; Junwei Han; Xi Jiang; Heng Huang; Huan Liu; Jinglei Lv; Lei Guo; Tianming Liu;

  • 作者单位

    School of Automation Northwestern Polytechnical University;

    School of Automation Northwestern Polytechnical University;

    Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia;

    School of Automation Northwestern Polytechnical University;

    School of Automation Northwestern Polytechnical University;

    School of Automation Northwestern Polytechnical University;

    School of Automation Northwestern Polytechnical University;

    Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工神经网络与计算;
  • 关键词

    Natural stimuli; Sparse representation; Brain networks; Decoding; Auditory saliency;

    机译：自然刺激;稀疏表示;脑网络;解码;听觉效力;

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