首页> 外文期刊>NeuroImage >Capturing the musical brain with Lasso: Dynamic decoding of musical features from fMRI data
【24h】

Capturing the musical brain with Lasso: Dynamic decoding of musical features from fMRI data

机译:用套索捕获音乐大脑:FMRI数据的音乐特征动态解码

获取原文
获取原文并翻译 | 示例
       

摘要

We investigated neural correlates of musical feature processing with a decoding approach. To this end, we used a method that combines computational extraction of musical features with regularized multiple regression (LASSO). Optimal model parameters were determined by maximizing the decoding accuracy using a leave-one-out cross-validation scheme. The method was applied to functional magnetic resonance imaging (fMRI) data that were collected using a naturalistic paradigm, in which participants' brain responses were recorded while they were continuously listening to pieces of real music. The dependent variables comprised musical feature time series that were computationally extracted from the stimulus. We expected timbral features to obtain a higher prediction accuracy than rhythmic and tonal ones. Moreover, we expected the areas significantly contributing to the decoding models to be consistent with areas of significant activation observed in previous research using a naturalistic paradigm with fMRI. Of the six musical features considered, five could be significantly predicted for the majority of participants. The areas significantly contributing to the optimal decoding models agreed to a great extent with results obtained in previous studies. In particular, areas in the superior temporal gyrus, Heschl's gyrus, Rolandic operculum, and cerebellum contributed to the decoding of timbral features. For the decoding of the rhythmic feature, we found the bilateral superior temporal gyrus, right Heschl's gyrus, and hippocampus to contribute most. The tonal feature, however, could not be significantly predicted, suggesting a higher inter-participant variability in its neural processing. A subsequent classification experiment revealed that segments of the stimulus could be classified from the fMRI data with significant accuracy. The present findings provide compelling evidence for the involvement of the auditory cortex, the cerebellum and the hippocampus in the processing of musical features during continuous listening to music.
机译:我们通过解码方法调查了音乐特征处理的神经相关性。为此,我们使用了一种方法,该方法将音乐特征的计算提取与正则化多元回归(套索)结合起来。通过使用休假次交叉验证方案最大化解码精度来确定最佳模型参数。该方法应用于使用自然主义范式收集的功能磁共振成像(FMRI)数据,其中记录了参与者的脑反应,同时他们不断收听真实音乐。从属变量包括从刺激计算上提取的音乐特征时间序列。我们预期的时间特征,以获得比节奏和色调的更高的预测精度。此外,我们预计将与解码模型的解码模型显着贡献的区域与使用与FMRI的自然主义范例一起在以前的研究中观察到的显着激活的领域。在考虑的六个音乐特征中,对于大多数参与者来说,可以大大预测五个。在很大程度上,这些区域对最佳解码模型进行了很大程度上同意,在以前的研究中获得的结果。特别是,优越的颞克鲁斯,Heschl的回肠,Rolandic和Cerebellum的区域有助于解码织物特征。为了解读节奏特征,我们发现双侧优越的颞克鲁鲁斯,右半的Heschl的回肠和海马最贡献。然而,Tenal特征不能显着预测,表明其神经处理中的较高的参与者间变异性。随后的分类实验表明,刺激的区段可以以显着的准确度从FMRI数据分类。目前的研究结果为听觉皮质,小脑和海马在持续倾听音乐期间的音乐特征加工中提供了令人信服的证据。

著录项

  • 来源
    《NeuroImage》 |2014年第null期|共11页
  • 作者单位

    Finnish Centre of Excellence in Interdisciplinary Music Research Department of Music University;

    Finnish Centre of Excellence in Interdisciplinary Music Research Department of Music University;

    Finnish Centre of Excellence in Interdisciplinary Music Research Department of Music University;

    Center of Functionally Integrative Neuroscience Aarhus University Hospital Denmark;

    Center of Functionally Integrative Neuroscience Aarhus University Hospital Denmark Royal Academy;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 诊断学;
  • 关键词

    Decoding; FMRI; Music; Music Information Retrieval; Naturalistic paradigm; Time series;

    机译:解码;FMRI;音乐;音乐信息检索;自然主义范式;时间序列;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号