Neural Processing of Noise-Vocoded Speech Under Divided Attention: An fMRI-Machine Learning Study.

IF 3.3 2区 医学 Q1 NEUROIMAGING
Han Wang, Rongru Chen, Josef Schlittenlacher, Carolyn McGettigan, Stuart Rosen, Patti Adank
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引用次数: 0

Abstract

In real-life interaction, we often need to communicate under challenging conditions, such as when speech is acoustically degraded. This issue is compounded by the fact that our attentional resources are often divided when we simultaneously need to engage in other tasks. The interaction between the perception of degraded speech and simultaneously performing additional cognitive tasks is poorly understood. Here, we combined a dual-task paradigm with functional magnetic resonance imaging (fMRI) and machine learning to establish the neural network supporting degraded speech perception under divided attention. We presented 25 human participants with noise-vocoded sentences while they engaged in a concurrent visuomotor recognition task, employing a factorial design that manipulated both speech degradation and task difficulty. Participants listened to eight-band (easier) and four-band (more difficult) noise-vocoded sentences, while the Gabor task featured two difficulty levels, determined by the angular discrepancy of the target. We employed a machine learning algorithm (Extreme Gradient Boosting, XGBoost) to evaluate the set of brain areas that showed activity predicting the difficulty of the speech and dual tasks. The results illustrated intelligibility-related responses in frontal and cingulate cortices and bilateral insulae induced by divided attention. Machine learning further revealed modality-general and specific responses to speech and visual inputs, in a set of frontotemporal regions reported for domain-general cognitive functions such as attentional control, motor function, and performance monitoring. These results suggest that the management of attentional resources during challenging speech perception recruits a bilateral operculo-frontal network also associated with processing acoustically degraded speech.

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分散注意力下噪声语音编码的神经处理:fmri机器学习研究。
在现实生活的互动中,我们经常需要在具有挑战性的条件下进行交流,例如当语音声音退化时。当我们同时需要从事其他任务时,我们的注意力资源经常被分散,这一事实使问题更加复杂。言语退化感知与同时执行额外认知任务之间的相互作用尚不清楚。在此,我们将双任务范式与功能磁共振成像(fMRI)和机器学习相结合,建立了支持分散注意力下退化语音感知的神经网络。我们向25名人类参与者展示了噪声编码的句子,同时他们参与了一个并发的视觉运动识别任务,采用了一个因子设计来控制语音退化和任务难度。参与者听了8波段(更容易)和4波段(更难)的噪声编码句子,而Gabor任务有两个难度等级,由目标的角度差异决定。我们使用了一种机器学习算法(Extreme Gradient Boosting, XGBoost)来评估显示预测语音和双重任务难度的活动的大脑区域集。结果表明,注意力分散引起额叶、扣带皮层和双侧脑岛的可理解性相关反应。机器学习进一步揭示了对语音和视觉输入的模态一般和特定反应,在一组额颞叶区域中报道了一般认知功能,如注意力控制、运动功能和表现监测。这些结果表明,在挑战语音感知过程中,注意力资源的管理需要一个与处理声音退化语音相关的双侧额叶神经网络。
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来源期刊
Human Brain Mapping
Human Brain Mapping 医学-核医学
CiteScore
8.30
自引率
6.20%
发文量
401
审稿时长
3-6 weeks
期刊介绍: Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged. Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.
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