Jiang Jessica, Benhamou Elia, Waters Sheena, Johnson Jeremy C S, Volkmer Anna, Weil Rimona S, Marshall Charles R, Warren Jason D, Hardy Chris J D
Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK.
Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London EC1M 6BQ, UK.
Brain Sci. 2021 Mar 20;11(3):394. doi: 10.3390/brainsci11030394.
The speech we hear every day is typically "degraded" by competing sounds and the idiosyncratic vocal characteristics of individual speakers. While the comprehension of "degraded" speech is normally automatic, it depends on dynamic and adaptive processing across distributed neural networks. This presents the brain with an immense computational challenge, making degraded speech processing vulnerable to a range of brain disorders. Therefore, it is likely to be a sensitive marker of neural circuit dysfunction and an index of retained neural plasticity. Considering experimental methods for studying degraded speech and factors that affect its processing in healthy individuals, we review the evidence for altered degraded speech processing in major neurodegenerative diseases, traumatic brain injury and stroke. We develop a predictive coding framework for understanding deficits of degraded speech processing in these disorders, focussing on the "language-led dementias"-the primary progressive aphasias. We conclude by considering prospects for using degraded speech as a probe of language network pathophysiology, a diagnostic tool and a target for therapeutic intervention.
我们每天听到的语音通常会因竞争声音和各个说话者独特的嗓音特征而“退化”。虽然对“退化”语音的理解通常是自动的,但它依赖于分布式神经网络的动态和自适应处理。这给大脑带来了巨大的计算挑战,使得退化语音处理容易受到一系列脑部疾病的影响。因此,它很可能是神经回路功能障碍的一个敏感指标,也是神经可塑性保留的一个指标。考虑到研究退化语音的实验方法以及影响健康个体对其处理的因素,我们回顾了在主要神经退行性疾病、创伤性脑损伤和中风中退化语音处理改变的证据。我们开发了一个预测编码框架,以理解这些疾病中退化语音处理的缺陷,重点关注“语言主导的痴呆症”——原发性进行性失语症。我们通过考虑将退化语音用作语言网络病理生理学的探针、诊断工具和治疗干预靶点的前景来得出结论。