Neural structures supporting spontaneous and assisted (entrained) speech fluency

Abstract

Non-fluent speech is one of the most common impairments in post-stroke aphasia. The rehabilitation of non-fluent speech in aphasia is particularly challenging as patients are rarely able to produce and practice fluent speech production. Speech entrainment is a behavioural technique that enables patients with non-fluent aphasia to speak fluently. However, its mechanisms are not well understood and the level of improved fluency with speech entrainment varies among individuals with non-fluent aphasia. In this study, we evaluated the behavioural and neuroanatomical factors associated with better speech fluency with the aid of speech entrainment during the training phase of speech entrainment. We used a lesion-symptom mapping approach to define the relationship between chronic stroke location on MRI and the number of different words per second produced during speech entrainment versus picture description spontaneous speech. The behavioural variable of interest was the speech entrainment/picture description ratio, which, if ≥1, indicated an increase in speech output during speech entrainment compared to picture description. We used machine learning (shallow neural network) to assess the statistical significance and out-of-sample predictive accuracy of the neuroanatomical model, and its regional contributors. We observed that better assisted speech (higher speech entrainment/picture description ratio) was achieved by individuals who had preservation of the posterior middle temporal gyrus, inferior fronto-occipital fasciculus and uncinate fasciculus, while exhibiting lesions in areas typically associated with non-fluent aphasia, such as the superior longitudinal fasciculus, precentral, inferior frontal, supramarginal and insular cortices. Our findings suggest that individuals with dorsal stream damage but preservation of ventral stream structures are more likely to achieve more fluent speech with the aid of speech entrainment compared to spontaneous speech. This observation provides insight into the mechanisms of non-fluent speech in aphasia and has potential implications for future research using speech entrainment for rehabilitation of non-fluent aphasia.

Keywords: aphasia; machine learning; speech fluency; speech therapy; stroke.