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一种包容性的多变量方法,用于语言组件的神经局部化

William W Graves¹, Hillary J Levinson², Ryan Staples³

¹Department of Psychology, Rutgers University, Smith Hall, Room 301, 101 Warren Street, Newark, NJ, 07102, USA. william.graves@rutgers.edu.

Brain structure & function

|May 1, 2024

概括

与传统方法相比,一种新的多变量脑成像方法更准确地绘制语言处理区域,特别是单词含义 (语义),与传统方法相比. 这种技术增强了对大脑语言网络的理解.

科学领域:

神经科学是一个神经科学.
认知科学认知科学
心理语言学心理语言学

背景情况:

在大脑中定位语言对于理解它的神经执行至关重要.
当前的功能磁共振成像 (fMRI) 方法经常使用单变量分析,这可能会错过涉及复杂语言功能 (如语义) 的大脑区域.
现有的句子与伪词对比可靠地识别核心语言区域,但对语义处理区域不那么敏感.

研究的目的:

开发和验证一种多变量,基于模式的方法来识别涉及语言处理的大脑区域.
为了比较多变量感兴趣区域 (mROI) 与传统单变量感兴趣区域 (uROI) 的灵敏度,用于绘制语言的语义组件.
评估跨不同数据集和语言任务的多变量方法的可复制性和有效性.

主要方法:

定义的多变量感兴趣区域 (mROI) 基于可重复的,基于模式的参与者和测量的激活.
在fMRI数据上使用代表性相似性分析 (RSA),参与者对书面文字进行熟悉性判断.
将mROI的RSA结果与从句子与伪词对比得出的单变性感兴趣区域 (uROI) 的结果进行比较.

主要成果:

当分析语义词刺激时,表示性相似性分析 (RSA) 显示了与mROI中的神经模式的更强的对应,而不是uROI.
这一发现被复制到两个独立的fMRI数据集中,一个集中在单词识别上,另一个集中在名词-名词短语含义上.
对语义处理的最大神经关联是在mROI和uROI空间重叠的区域观察到的.

结论:

通过可重现模式分析识别的多变量感兴趣区域 (mROI) 提供了语言组件的改进本地化,特别是语义.
多变量方法补充了传统的单变量方法,可以扩展到研究其他语言方面,如语音学.
这种综合方法为绘制整个语言皮层提供了更具包容性的方法.

关键词:

功能性磁共振成像技术功能性磁共振成像技术语言语言语言语言多变量模式分析多变量模式分析阅读阅读阅读阅读代表性的相似性分析.

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