Speech Rate Mediated Vowel and Stop Voicing Distinctiveness in Parkinson's Disease

Author(s):  
Thea Knowles ◽  
Scott G. Adams ◽  
Mandar Jog

Purpose The purpose of this study was to quantify changes in acoustic distinctiveness in two groups of talkers with Parkinson's disease as they modify across a wide range of speaking rates. Method People with Parkinson's disease with and without deep brain stimulation and older healthy controls read 24 carrier phrases at different speech rates. Target nonsense words in the carrier phrases were designed to elicit stop consonants and corner vowels. Participants spoke at seven self-selected speech rates from very slow to very fast, elicited via magnitude production. Speech rate was measured in absolute words per minute and as a proportion of each talker's habitual rate. Measures of segmental distinctiveness included a temporal consonant measure, namely, voice onset time, and a spectral vowel measure, namely, vowel articulation index. Results All talkers successfully modified their rate of speech from slow to fast. Talkers with Parkinson's disease and deep brain stimulation demonstrated greater baseline speech impairment and produced smaller proportional changes at the fast end of the continuum. Increasingly slower speaking rates were associated with increased temporal contrasts (voice onset time) but not spectral contrasts (vowel articulation). Faster speech was associated with decreased contrasts in both domains. Talkers with deep brain stimulation demonstrated more aberrant productions across all speaking rates. Conclusions Findings suggest that temporal and spectral segmental distinctiveness are asymmetrically affected by speaking rate modifications in Parkinson's disease. Talkers with deep brain stimulation warrant further investigation with regard to speech changes they make as they adjust their speaking rate.

Author(s):  
Tipu Aziz ◽  
Holly Roy

Deep brain stimulation (DBS) is a neurosurgical technology that allows the manipulation of activity within specific brain regions through delivery of electrical stimulation via implanted electrodes. The growth of DBS has led to research around the development of novel interventions for a wide range of neurological and neuropsychiatric conditions, including Parkinson’s disease, dystonia, chronic pain, Tourette’s syndrome, treatment-resistant depression, anorexia nervosa, and Alzheimer’s disease. Some of these treatment approaches have a high level of efficacy as well as an established place in the clinical armamentarium for the diseases in question, such as DBS for movement disorders, including Parkinson’s disease. Other interventions are at a more developmental stage, such as DBS for depression and Alzheimer’s disease. Success both in clinical aspects of DBS and new innovations depends on a close-knit multidisciplinary team incorporating experts in the underlying condition (often neurologists and psychiatrists); neurosurgeons; nurse specialists, who may be involved in device programming and other aspects of patient care; and researchers including neuroscientists, imaging specialists, engineers, and signal analysts. Directly linked to the growth of DBS as a specialty is allied research around neural signals analysis and device development, which feed directly back into further clinical progress. The close links between clinical DBS and basic and translational research make it an exciting and fast-moving area of neuroscience.


2020 ◽  
Vol 10 (11) ◽  
pp. 809
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Oleg Shylo ◽  
Ritesh A. Ramdhani

Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson’s disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information’s Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers’ (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.


2009 ◽  
Vol 36 (S 02) ◽  
Author(s):  
J Gierthmühlen ◽  
P Arning ◽  
G Wasner ◽  
A Binder ◽  
J Herzog ◽  
...  

2019 ◽  
pp. 158-173

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by a dopamine deficiency that presents with motor symptoms. Visual disorders can occur concomitantly but are frequently overlooked. Deep brain stimulation (DBS) has been an effective treatment to improve tremors, stiffness and overall mobility, but little is known about its effects on the visual system. Case Report: A 75-year-old Caucasian male with PD presented with longstanding binocular diplopia. On baseline examination, the best-corrected visual acuity was 20/25 in each eye. On observation, he had noticeable tremors with an unsteady gait. Distance alternating cover test showed exophoria with a right hyperphoria. Near alternating cover test revealed a significantly larger exophoria accompanied by a reduced near point of convergence. Additional testing with a 24-2 Humphrey visual field and optical coherence tomography (OCT) of the nerve and macula were unremarkable. The patient underwent DBS implantation five weeks after initial examination, and the device was activated four weeks thereafter. At follow up, the patient still complained of intermittent diplopia. There was no significant change in the manifest refraction or prism correction. On observation, the patient had remarkably improved tremors with a steady gait. All parameters measured were unchanged. The patient was evaluated again seven months after device activation. Although vergence ranges at all distances were improved, the patient was still symptomatic for intermittent diplopia. OCT scans of the optic nerve showed borderline but symmetric thinning in each eye. All other parameters measured were unchanged. Conclusion: The case found no significant changes on ophthalmic examination after DBS implantation and activation in a patient with PD. To the best of the authors’ knowledge, there are no other cases in the literature that investigated the effects of DBS on the visual system pathway in a patient with PD before and after DBS implantation and activation.


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