...
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:使用套索捕获音乐大脑:从fMRI数据动态解码音乐特征

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:我们使用解码方法研究了音乐特征处理的神经相关性。为此,我们使用了一种将音乐特征的计算提取与正则化多元回归(LASSO)相结合的方法。通过使用留一法交叉验证方案最大程度地提高解码精度来确定最佳模型参数。该方法适用于使用自然主义范式收集的功能性磁共振成像(fMRI)数据,其中参与者在不断聆听真实音乐的同时记录其大脑反应。因变量包括从刺激中计算提取的音乐特征时间序列。我们期望音色特征比节奏和音调特征具有更高的预测准确性。此外,我们希望对解码模型有重大贡献的区域与使用fMRI的自然范式进行的先前研究中观察到的明显激活的区域一致。在考虑的六种音乐特征中,对于大多数参与者而言,有五种可以被显着预测。对最佳解码模型有重大贡献的领域在很大程度上与以前的研究结果一致。特别是颞上回,赫氏回,罗兰land和小脑的区域有助于音色特征的解码。对于节奏特征的解码,我们发现双侧上颞回,右赫氏回和海马贡献最大。但是,音调特征无法得到明显预测,这表明其神经处理过程中参与者之间的变异性更高。随后的分类实验表明,可以从fMRI数据中以很高的准确性对刺激的各个部分进行分类。本发现为连续听音乐过程中听觉皮层,小脑和海马体参与音乐特征的处理提供了令人信服的证据。

著录项

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