Jenson David, Bowers Andrew L, Hudock Daniel, Saltuklaroglu Tim
Department of Speech and Hearing Sciences, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, United States.
Epley Center for Health Professions, Communication Sciences and Disorders, University of Arkansas, Fayetteville, AR, United States.
Front Hum Neurosci. 2020 Jan 10;13:458. doi: 10.3389/fnhum.2019.00458. eCollection 2019.
Deficits in basal ganglia-based inhibitory and timing circuits along with sensorimotor internal modeling mechanisms are thought to underlie stuttering. However, much remains to be learned regarding the precise manner how these deficits contribute to disrupting both speech and cognitive functions in those who stutter. Herein, we examine the suitability of electroencephalographic (EEG) mu rhythms for addressing these deficits. We review some previous findings of mu rhythm activity differentiating stuttering from non-stuttering individuals and present some new preliminary findings capturing stuttering-related deficits in working memory. Mu rhythms are characterized by spectral peaks in alpha (8-13 Hz) and beta (14-25 Hz) frequency bands (mu-alpha and mu-beta). They emanate from premotor/motor regions and are influenced by basal ganglia and sensorimotor function. More specifically, alpha peaks (mu-alpha) are sensitive to basal ganglia-based inhibitory signals and sensory-to-motor feedback. Beta peaks (mu-beta) are sensitive to changes in timing and capture motor-to-sensory (i.e., forward model) projections. Observing simultaneous changes in mu-alpha and mu-beta across the time-course of specific events provides a rich window for observing neurophysiological deficits associated with stuttering in both speech and cognitive tasks and can provide a better understanding of the functional relationship between these stuttering symptoms. We review how independent component analysis (ICA) can extract mu rhythms from raw EEG signals in speech production tasks, such that changes in alpha and beta power are mapped to myogenic activity from articulators. We review findings from speech production and auditory discrimination tasks demonstrating that mu-alpha and mu-beta are highly sensitive to capturing sensorimotor and basal ganglia deficits associated with stuttering with high temporal precision. Novel findings from a non-word repetition (working memory) task are also included. They show reduced mu-alpha suppression in a stuttering group compared to a typically fluent group. Finally, we review current limitations and directions for future research.
基于基底神经节的抑制和定时回路以及感觉运动内部建模机制的缺陷被认为是口吃的基础。然而,关于这些缺陷如何导致口吃者的言语和认知功能中断的精确方式,仍有许多有待了解。在此,我们研究脑电图(EEG)μ节律用于解决这些缺陷的适用性。我们回顾了一些先前关于μ节律活动区分口吃者与非口吃者的研究结果,并展示了一些新的初步研究结果,这些结果捕捉到了口吃相关的工作记忆缺陷。μ节律的特征是在α(8 - 13Hz)和β(14 - 25Hz)频段出现频谱峰值(μ - α和μ - β)。它们起源于运动前区/运动区,并受基底神经节和感觉运动功能的影响。更具体地说,α峰值(μ - α)对基于基底神经节的抑制信号和感觉 - 运动反馈敏感。β峰值(μ - β)对时间变化敏感,并捕捉运动 - 感觉(即前向模型)投射。观察特定事件时间进程中μ - α和μ - β的同时变化,为观察与口吃相关的言语和认知任务中的神经生理缺陷提供了一个丰富的窗口,并且可以更好地理解这些口吃症状之间的功能关系。我们回顾了独立成分分析(ICA)如何从言语产生任务中的原始EEG信号中提取μ节律,从而将α和β功率的变化映射到发音器官的肌源性活动。我们回顾了言语产生和听觉辨别任务的研究结果,这些结果表明μ - α和μ - β对以高时间精度捕捉与口吃相关的感觉运动和基底神经节缺陷高度敏感。还包括了非词重复(工作记忆)任务中的新发现。这些发现表明,与典型流利组相比,口吃组的μ - α抑制减少。最后,我们回顾了当前的局限性和未来研究的方向。