https://doi.org/10.1016/j.cnp.2022.06.002

Clinical neurophysiology of Parkinson’s disease and parkinsonism

Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and naïve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.

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Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification

The preliminary diagnosis and evaluation of the presence and/or severity of Parkinson’s disease is crucial in controlling the progress of the disease. Real-time, non-invasive methodologies based on machine learning-enhanced voice analysis are gathering more interest as the potential of this field unveils. Specifically, acoustic features are employed in many machine learning techniques, and could also function as indicators of the overall state of the subjects’ voice: this review aims at identifying the most widely employed and promising feature-based machine learning methodologies, evidencing baselines and state-of-the-art solutions. A total of 102 works plus 5 review articles were selected from the IEEE Xplore, PubMed, Elsevier, and Web of Science electronic databases. A statistical assessment is performed identifying the most frequently used features as well as those deemed as most effective; an overview of algorithms, public datasets, toolboxes, and general metadata is also performed. According to our results, Jitter, Shimmer, Harmonic-to-Noise Ratio, Fundamental Frequency, and Mel Frequency Cepstral Coefficients are the mostly adopted features. In addition, it is worth noting a fair prevalence of glottal-like models and additional filtering options, such as Detrended Fluctuation Analysis. © 2017 Elsevier Inc. All rights reserved. 

Machine learning- and statistical-based voice analysis of Parkinson's disease patients: A survey

Parkinson’s Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient’s daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson’s Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting.

/pdf/an-analysis-of-vocal-features-for-parkinsons-disease-3a8gp856.pdf

An Analysis of Vocal Features for Parkinson’s Disease Classification Using Evolutionary Algorithms

Introduction Parkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy. Methods We investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations. Results Voice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning. Conclusion Voice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD.

/pdf/voice-in-parkinson-s-disease-a-machine-learning-study-1yfvy3d5.pdf

Voice in Parkinson's Disease: A Machine Learning Study

Background: Lipedema is a chronic and progressive adipose tissue disorder that causes significant morbidity and negatively influences mental health and quality of life, and increases the risk of depression, anxiety, and eating disorders. One construct of relevance to better understanding psychological disorders is emotion regulation (ER). Therefore, the aim of this study is to investigate the difficulties in ER among lipedema patients compared to healthy people without lipedema. Methods: This cross-sectional study assessed differences in ER and anxiety between two groups: 26 female patients with lipedema and 26 sex- and age-matched healthy controls. The Difficulties in Emotion Regulation Scale (DERS) assessed emotional regulation across six dimensions: Impulse control, goal-directed behavior, awareness, clarity, non-acceptance, and strategies. Anxiety was assessed by the Hamilton Anxiety Scale (HAM-A). ANOVA assessed differences in measures between lipedema and healthy control groups. Results: Lipedema patients presented with significantly more difficulties in ER and a higher level of anxiety than those without lipedema. Specifically, the lipedema group showed higher and significant differences in total DERS and anxiety scores and all DERS subscales scores compared to those without lipedema. Conclusions: Lipedema patients showed significant difficulties with ER, and were associated with anxiety symptoms, indicating that ER difficulties may play a role in developing emotional disorders, such as anxiety, for patients with lipedema. The health care provider should pay more attention to ER difficulties and psychological status among lipedema patients.

https://www.mdpi.com/1660-4601/19/20/13679/pdf?version=1666347595

The Difficulties in Emotional Regulation among a Cohort of Females with Lipedema

BACKGROUND
Multiple sclerosis (MS) is a progressive inflammatory and autoimmune neurological disease caused by inflammation and demyelination of the central nervous system. Pain is a typical symptom of central nervous system demyelination, affecting 63% of adults with MS. Recently, the role of non-pharmacological pain management in patients is growing because the non-pharmacological interventions are considered safe, affordable, easy, and accessible. However, to date, no systematic reviews or meta-analyses have comprehensively examined the therapeutic effects of the variety of non-pharmacological therapeutic interventions in the management of pain in patients with MS.


OBJECTIVE
The study aimed to conduct a systematic review with meta-analysis to assess the effectiveness of the non-pharmacological rehabilitation interventions in pain management in patients with MS.


METHODS
A comprehensive search using PubMed, Cochrane, and Science Direct databases was performed and included all randomized controlled trials, randomized cross-over trials, and quasi-experimental trials assessing the effect of non-pharmacological interventions for managing pain in patients with MS. This study was conducted according to PRISMA guidelines of a systematic review and pair-wise meta-analysis. Meta-analyses were performed by calculating the standardized mean difference at a 95% confidence interval using Review Manager software.


RESULTS
Twenty-nine papers were included in the systematic review, and only 22 of them were included in the meta-analysis. The pooled analysis showed a significant effect of neuromodulation and transcranial direct current stimulation on pain intensity reduction in patients with MS (SMD -0.51, 95% CI -0.51 to -0.09, P = 0.02), (SMD -0.67, 95% CI -1.18 to -0.16 P = 0.01), respectively. The analysis showed significant improvement in pain intensity in patient with MS after mind-body therapies (SMD -0.45, 95% CI -0.82 to -0.7, P = 0.02), mindfulness (SMD -0.55, 95% CI -0.96 to -0.14, P = 0.009), hypnosis (SMD -0.88, 95% CI -1.30 to -0.46, P = 0.0001), trigger point therapies (SMD -0.83, 95% CI -1.65 to -0.01, P = 0.05) and cognitive behavioral therapy (SMD -0.64, 95% CI -1.18 to -0.11, P = 0.02). However, there is no significant effect of relaxation therapy on pain reduction in patients with MS (SMD -0.82, 95% CI -1.94 to 0.31, P = 0.15).


CONCLUSIONS
The results indicated that the majority of the non-pharmacological rehabilitation interventions showed potential therapeutic effects in reducing pain intensity in patients with MS.

Effectiveness of non-pharmacological rehabilitation interventions in pain management in patients with multiple sclerosis: Systematic review and meta-analysis.

Christian Geroin, PhD,* Carlo Alberto Artusi, MD, PhD, Jorik Nonnekes, MD, PhD, Camila Aquino, MD, PhD, Divyani Garg, MD, DM, DNB, MNAMS, Marian L. Dale, MD, MRC, Darbe Schlosser, Yijie Lai, MD, Mohammad Al-Wardat, PT, PhD, Mehri Salari, MD, PhD, Robin Wolke, MD, Valery Tsinda Labou, PhD, Gabriele Imbalzano, MD, Serena Camozzi, PT, Marcelo Merello, MD, PhD, Bastiaan R. Bloem, MD, PhD, Tamine Capato, PhD, Ruth Djaldetti, MD, Karen Doherty, MD, FRCP, Alfonso Fasano, MD, PhD, Houyam Tibar, MD, Leonardo Lopiano, MD, PhD, Nils G. Margraf, MD, PhD, Caroline Moreau, MD, PhD, Yoshikazu Ugawa, MD, PhD, Roongroj Bhidayasiri, MD, FRCP, and Michele Tinazzi, MD, PhD,* on behalf of the International Parkinson and Movement Disorders Society Task Force on Postural Abnormalities

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mds.29377

Axial Postural Abnormalities in Parkinsonism: Gaps in Predictors, Pathophysiology, and Management

Axial postural abnormalities and pain are two main determinants of poor quality of life in patients with Parkinson’s disease (PD). Indeed, a detailed characterization of pain and other non-motor symptoms in patients with PAs has not been provided yet. The aim of this study is to assess the phenomenology of pain and other non-motor symptoms in PD patients with Pisa syndrome and camptocormia compared to PD patients without axial postural abnormality. Forty-five PD participants were equally distributed in three groups: patients with Pisa syndrome (PS), patients with Camptocormia (CC), and patients without postural abnormalities (PD). Pain characteristics were assessed by Kings Parkinson’s Pain Scale (KPPS), brief pain inventory (BPI), and numeric pain rating scale (NRS). All participants completed clinical assessments by non-motor symptom scale (NMSS), and movement disorder society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) parts II–III. Patients with and without axial postural abnormalities showed one or more types of pain, being fluctuation, nocturnal, chronic, and musculoskeletal the most frequently reported in Pisa Syndrome and camptocormia. PD group compared with PS and CC groups showed differences in the KPPS, NMSS, BPI pain severity and interference, and NRS total scores. No significant differences were found between PS group compared with CC group with exception of the NMSS total scores. PD patients with Pisa syndrome or camptocormia have a higher burden of musculoskeletal, chronic and fluctuation pain than PD patients without axial postural abnormalities, suggesting different etiologies of pain and possible different treatments. 

/pdf/axial-postural-abnormalities-and-pain-in-parkinsons-disease-1fiqn2v2.pdf

Mohammad Al-Wardat

Papers

Voice in Parkinson's Disease: A Machine Learning Study

The Difficulties in Emotional Regulation among a Cohort of Females with Lipedema

Effectiveness of non-pharmacological rehabilitation interventions in pain management in patients with multiple sclerosis: Systematic review and meta-analysis.

Axial Postural Abnormalities in Parkinsonism: Gaps in Predictors, Pathophysiology, and Management

Axial postural abnormalities and pain in Parkinson’s disease