Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.
Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.
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DOI：
10.1371/journal.pone.0117303
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Wong PC
中科院分区：
文献类型：
--
作者：
Zhang F;Wang JP;Kim J;Parrish T;Wong PC
声音类别的感知是听觉感知的一个重要方面。大脑对声音类别的表征在多大程度上被编码在专门的子区域或分布在听觉皮层中的程度仍不清楚。最近使用大脑激活的多元模式分析（MVPA）进行的研究为大脑如何解码感知信息提供了重要的见解。在现有大量关于使用 MVPA 方法进行大脑解码的文献中，对听觉领域的多类分类进行的研究相对较少。在这里，我们使用高分辨率功能磁共振成像和 MVPA 方法研究了人类颞皮层内听觉类别的表示和处理。更重要的是，我们考虑通过多类支持向量机递归特征消除（MSVM-RFE）作为我们的 MVPA 工具同时解码多个声音类别。结果表明，对于所有分类，MSVM-RFE 模型都能够学习多个声音类别与相应诱发空间模式之间的函数关系，并对未标记的声音诱发模式进行分类，其概率显着高于随机概率。这表明不仅在受试者内部而且跨受试者解码多个声音类别的可行性。然而，跨受试者变异比受试者内变异对分类性能的影响更大，因为跨受试者分析的分类精度明显较低。基于多类分类识别声音类别选择性脑图，并揭示颞上回和颞中回的大脑活动分布模式。这与之前的研究一致，表明空间分布模式中的信息可能反映声音类别表示的更抽象的感知水平。此外，我们表明，通过对项目上的功能磁共振成像图像进行平均，可以显着提高跨主题分类性能，因为同一声音类别的不同项目之间的不相关变化减少了，反过来与声音分类相关的信号比例增加了。
Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.
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