Speech neuroimaging research targeting individual speakers could help elucidate differences that may be crucial to understanding speech disorders. However, this research necessitates reliable brain activation across multiple speech production sessions. In the present study, we evaluated the reliability of speech-related brain activity measured by functional magnetic resonance imaging data from twenty neuro-typical subjects who participated in two experiments involving reading aloud simple speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability. We also found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.

中文翻译：

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语音生成任务中单主体神经激活模式的可靠性

针对个体说话者的言语神经影像研究可以帮助阐明可能对理解言语障碍至关重要的差异。然而，这项研究需要在多个语音生成会话中可靠地激活大脑。在本研究中，我们评估了通过功能磁共振成像数据测量的与语音相关的大脑活动的可靠性，这些数据来自 20 名神经典型受试者，这些受试者参与了两个涉及大声朗读简单语音刺激的实验。使用 Dice 和类内相关系数等传统方法，我们发现大多数人表现出中等至高度的可靠性。我们还发现，一种新的机器学习主题分类器可以通过他们的语音激活模式从 75 个主题的数据集中以 97% 的准确率识别这些人。