Dystonia Diagnosis: Clinical Neurophysiology and Genetics

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review

BACKGROUND

The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance".

RESULTS

From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours.

DISCUSSION

Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.

Pallidal and Thalamic Deep Brain Stimulation in the Treatment of Unilateral Dystonia: A Prospective Assessment

BACKGROUND

The complexities of unilateral dystonia have led to exploring simultaneous (dual) globus pallidus internus (GPi) and motor ventral thalamus (Vim/Vop) deep brain stimulation (DBS), yet detailed assessments are lacking.

OBJECTIVES

To assess the efficacy of GPi, Vim/Vop, and dual DBS in unilateral dystonia.

METHODS

Three patients with unilateral dystonia (two idiopathic, one acquired), implanted with two DBS electrodes targeting ipsilateral Vim/Vop and GPi, were included. Three stimulation modalities were assessed. First, one electrode was activated, then the other, and finally, both electrodes were activated simultaneously.

RESULTS

DBS yielded substantial symptomatic reductions in all three evaluated stimulation modalities. Patients exhibited varying responses regarding quality-of-life and depressive symptoms. Treatment satisfaction didn't align with clinical improvements, potentially affected by unrealistic expectations.

CONCLUSIONS

This study contributes critical insights into GPi, Vim/Vop and simultaneous stimulation for unilateral dystonia. The safety of the procedure underscores the promise of this approach.

Pisa Syndrome Secondary to Drugs: A Scope Review

BACKGROUND

Pisa syndrome, also known as pleurothotonus, is a neurological condition characterized by more than ten degrees of constant lateral curvature of the spine when upright. In this way, the present manuscript aims to systematically review Pisa syndrome secondary to drugs.

METHODS

Two reviewers identified and assessed relevant reports in six databases without language restriction between January 1990 and June 2024.

RESULTS

The prevalence of Pisa syndrome varied from 0.037 to 9.3%. We found 109 articles containing 191 cases of drug-induced Pisa syndrome reported in the literature. The mean and median ages were 59.70 (SD = 19.02) and 67 (range = 12-98 years). The most prevalent sex was female, 56.91% (107/188). The most frequent medications associated with Pisa syndrome were acetylcholinesterase inhibitors in 87 individuals. Of 112 individuals in which the onset time from the medication to the movement disorder occurrence was reported, 59 took place within a month. In this way, a return to baseline was observed in 45.50% of the cases, and partial recovery was observed in 14.28%.

CONCLUSION

We proposed new diagnostic criteria for Pisa syndrome based on previous findings in the literature. Moreover, multiple mechanisms are probably involved in balance control and the development of lateral trunk flexions.

Possible Role of Mitochondrial Transfer RNA Gene 5816 A > G Genetic Polymorphism (m.5816A > G) in a 3-Year-Old Child with Dystonia: Report of a Case

Background  Mutations in the mitochondrial transfer RNA (mt-tRNA) gene are a hotspot for mitochondrial DNA (mtDNA) mutations and are most common in mitochondrial diseases. Methods  We identified the mt-tRNA gene 5816 A > G (m.5816 A > G) mutation in a 3-year-old child with dystonia who died. We performed clinical evaluation, genetic analysis, and biochemical investigation with mitochondrial function testing. Results  Our patient was found to have dystonia with hyperlactatemia. Electroencephalogram findings were abnormal in children with numerous multifocal spikes, multispike, spikes and slow waves, slow waves and low amplitude fast waves, more pronounced in the occipital region bilaterally, and occurring continuously during sleep. One year later, the preexisting patient had seizures lasting 1 to 2 hours and subsequently died. mtDNA sequencing revealed that the proband, her mother, and her grandmother all carried the m.5816A > G mutation. Oxygen consumption rate (OCR) assays revealed that the proband's basal resting OCR, adenosine triphosphate production, proton leak, maximal respiration, and spare capacity OCR were all significantly lower compared with healthy children of the same age. Conclusion  The present case demonstrates a childhood dystonia caused by a mt-tRNA gene 5816 A > G mutation, which has never been reported before. Our findings provide valuable new insights into the pathogenic mechanism and function of the m.5816A > G mutation.

Levodopa-Induced Dyskinesias in Parkinson's Disease: An Overview on Pathophysiology, Clinical Manifestations, Therapy Management Strategies and Future Directions

Since its first introduction, levodopa has become the cornerstone for the treatment of Parkinson's disease and remains the leading therapeutic choice for motor control therapy so far. Unfortunately, the subsequent appearance of abnormal involuntary movements, known as dyskinesias, is a frequent drawback. Despite the deep knowledge of this complication, in terms of clinical phenomenology and the temporal relationship during a levodopa regimen, less is clear about the pathophysiological mechanisms underpinning it. As the disease progresses, specific oscillatory activities of both motor cortical and basal ganglia neurons and variation in levodopa metabolism, in terms of the dopamine receptor stimulation pattern and turnover rate, underlie dyskinesia onset. This review aims to provide a global overview on levodopa-induced dyskinesias, focusing on pathophysiology, clinical manifestations, therapy management strategies and future directions.

Clinical features of dystonia and the science of classification

This chapter describes advances in understanding the clinical features of dystonia since initial clinical recognition and its organization into a coherent and systematic clinical set. The clinical features of dystonia were at first considered an odd neurological movement disorder. Etymology of the word misleadingly underlined muscle tone. The main clinical features of dystonia were recognized gradually. They encompass dystonic movements, dystonic postures, alleviating maneuvers, overflow and mirroring. These features are observed in patients who present a variety of syndromes where dystonia occurs in isolation or combined with other movement disorders, or with other neurologic or systemic features. A large number of syndromic combinations is observed in the clinic and some of the syndomes are highlighted here. Practitioners are required to exert dedicated skills to recognize dystonia and correctly diagnose and classify their patients.

Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring

Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.