Hypokinetic dysarthria (HD) occurs in 90% of Parkinson's disease (PD) patients. It manifests specifically in the areas of articulation, phonation, prosody, speech fluency, and faciokinesis. We aimed to systematically review papers on HD in PD with a special focus on (1) early PD diagnosis and monitoring of the disease progression using acoustic voice and speech analysis, and (2) functional imaging studies exploring neural correlates of HD in PD, and (3) clinical studies using acoustic analysis to evaluate effects of dopaminergic medication and brain stimulation. A systematic literature search of articles written in English before March 2016 was conducted in the Web of Science, PubMed, SpringerLink, and IEEE Xplore databases using and combining specific relevant keywords. Articles were categorized into three groups: (1) articles focused on neural correlates of HD in PD using functional imaging (n = 13); (2) articles dealing with the acoustic analysis of HD in PD (n = 52); and (3) articles concerning specifically dopaminergic and brain stimulation-related effects as assessed by acoustic analysis (n = 31); the groups were then reviewed. We identified 14 combinations of speech tasks and acoustic features that can be recommended for use in describing the main features of HD in PD. While only a few acoustic parameters correlate with limb motor symptoms and can be partially relieved by dopaminergic medication, HD in PD seems to be mainly related to non-dopaminergic deficits and associated particularly with non-motor symptoms. Future studies should combine non-invasive brain stimulation with voice behavior approaches to achieve the best treatment effects by enhancing auditory-motor integration.

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Introduction: Hypokinetic dysarthria (HD) is a common symptom of Parkinson's disease (PD) which does not respond well to PD treatments. We investigated acute effects of repetitive transcranial magnetic stimulation (rTMS) of the motor and auditory feedback area on HD in PD using acoustic analysis of speech. Methods: We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the left orofacial primary motor area, the right superior temporal gyrus (STG), and over the vertex (a control stimulation site) in 16 PD patients with HD. A cross-over design was used. Stimulation sites and protocols were randomised across subjects and sessions. Acoustic analysis of a sentence reading task performed inside the MR scanner was used to evaluate rTMS-induced effects on motor speech. Acute fMRI changes due to rTMS were also analysed. Results: The 1 Hz STG stimulation produced significant increases of the relative standard deviation of the 2nd formant (p = 0.019), i.e. an acoustic parameter describing the tongue and jaw movements. The effects were superior to the control site stimulation and were accompanied by increased resting state functional connectivity between the stimulated region and the right parahippocampal gyrus. The rTMS-induced acoustic changes were correlated with the reading task-related BOLD signal increases of the stimulated area (R = 0.654, p = 0.029). Conclusion: Our results demonstrate for the first time that low-frequency stimulation of the temporal auditory feedback area may improve articulation in PD and enhance functional connectivity between the STG and the cortical region involved in an overt speech control.

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.