Department of Speech, Language, & Hearing Sciences, Boston University, Boston, MA 02215, USA.
Department of Speech, Language, & Hearing Sciences, Boston University, Boston, MA 02215, USA.
Brain Lang. 2021 Jan;212:104881. doi: 10.1016/j.bandl.2020.104881. Epub 2020 Dec 2.
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.
针对个体说话者的言语神经影像学研究有助于阐明对于理解言语障碍至关重要的差异。然而，这项研究需要在多个言语产生过程中获得可靠的大脑激活。在本研究中，我们评估了 20 名神经典型受试者在两次朗读简单言语刺激实验中通过功能磁共振成像数据测量的言语相关脑活动的可靠性。使用传统方法（如 Dice 和组内相关系数），我们发现大多数个体表现出中等到高度的可靠性。我们还发现，一种新的机器学习个体分类器可以通过 75 名受试者数据集的言语激活模式以 97%的准确率识别这些个体。这些结果表明，个体言语研究将产生有效的结果，并且对言语障碍患者言语激活的可靠性进行研究是有必要的。
