Rigidity in Parkinson's disease: evidence from biomechanical and neurophysiological measures.

Although rigidity is a cardinal motor sign in patients with Parkinson's disease (PD), the instrumental measurement of this clinical phenomenon is largely lacking, and its pathophysiological underpinning remains still unclear. Further advances in the field would require innovative methodological approaches able to measure parkinsonian rigidity objectively, discriminate the different biomechanical sources of muscle tone (neural or visco-elastic components), and finally clarify the contribution to 'objective rigidity' exerted by neurophysiological responses, which have previously been associated with this clinical sign (i.e. the long-latency stretch-induced reflex). Twenty patients with PD (67.3 ± 6.9 years) and 25 age- and sex-matched controls (66.9 ± 7.4 years) were recruited. Rigidity was measured clinically and through a robotic device. Participants underwent robot-assisted wrist extensions at seven different angular velocities randomly applied, when ON therapy. For each value of angular velocity, several biomechanical (i.e. elastic, viscous and neural components) and neurophysiological measures (i.e. short and long-latency reflex and shortening reaction) were synchronously assessed and correlated with the clinical score of rigidity (i.e. Unified Parkinson's Disease Rating Scale-part III, subitems for the upper limb). The biomechanical investigation allowed us to measure 'objective rigidity' in PD and estimate the neuronal source of this phenomenon. In patients, 'objective rigidity' progressively increased along with the rise of angular velocities during robot-assisted wrist extensions. The neurophysiological examination disclosed increased long-latency reflexes, but not short-latency reflexes nor shortening reaction, in PD compared with control subjects. Long-latency reflexes progressively increased according to angular velocities only in patients with PD. Lastly, specific biomechanical and neurophysiological abnormalities correlated with the clinical score of rigidity. 'Objective rigidity' in PD correlates with velocity-dependent abnormal neuronal activity. The observations overall (i.e. the velocity-dependent feature of biomechanical and neurophysiological measures of objective rigidity) would point to a putative subcortical network responsible for 'objective rigidity' in PD, which requires further investigation.

Assessing the interaction between L-dopa and γ-transcranial alternating current stimulation effects on primary motor cortex plasticity in Parkinson's disease.

L-dopa variably influences transcranial magnetic stimulation (TMS) parameters of motor cortex (M1) excitability and plasticity in Parkinson's disease (PD). In patients OFF dopaminergic medication, impaired M1 plasticity and defective GABA-A-ergic inhibition can be restored by boosting gamma (γ) oscillations via transcranial alternating current stimulation (tACS) during intermittent theta-burst stimulation (iTBS). However, it is unknown whether L-dopa modifies the beneficial effects of iTBS-γ-tACS on M1 in PD. In this study, a PD patients group underwent combined iTBS-γ-tACS and iTBS-sham-tACS, each performed both OFF and ON dopaminergic therapy (four sessions in total). Motor evoked potentials (MEPs) elicited by single TMS pulses and short-interval intracortical inhibition (SICI) were assessed before and after iTBS-tACS. We also evaluated possible SICI changes during γ-tACS delivered alone in OFF and ON conditions. The amplitude of MEP elicited by single TMS pulses and the degree of SICI inhibition significantly increased after iTBS-γ-tACS. The amount of change produced by iTBS-γ-tACS was similar in patients OFF and ON therapy. Finally, γ-tACS (delivered alone) modulated SICI during stimulation and this effect did not depend on the dopaminergic condition of patients. In conclusion, boosting cortical γ oscillatory activity via tACS during iTBS improved M1 plasticity and enhanced GABA-A-ergic transmission in PD patients to the same extent regardless of dopaminergic state. These results suggest a lack of interaction between L-dopa and γ-tACS effects at the M1 level. The possible neural substrate underlying iTBS-γ tACS effects, that is, γ-resonant GABA-A-ergic interneurons activity, may explain our findings.

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

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.

Decipher non-canonical SPAST splicing mutations with the help of functional assays in patients affected by spastic paraplegia 4 (SPG4).

Flowchart showing the molecular approach used to decipher the non-canonical splicing mutations.

Wearable Electrochemical Sensors in Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder associated with widespread aggregation of α-synuclein and dopaminergic neuronal loss in the substantia nigra pars compacta. As a result, striatal dopaminergic denervation leads to functional changes in the cortico-basal-ganglia-thalamo-cortical loop, which in turn cause most of the parkinsonian signs and symptoms. Despite tremendous advances in the field in the last two decades, the overall management (i.e., diagnosis and follow-up) of patients with PD remains largely based on clinical procedures. Accordingly, a relevant advance in the field would require the development of innovative biomarkers for PD. Recently, the development of miniaturized electrochemical sensors has opened new opportunities in the clinical management of PD thanks to wearable devices able to detect specific biological molecules from various body fluids. We here first summarize the main wearable electrochemical technologies currently available and their possible use as medical devices. Then, we critically discuss the possible strengths and weaknesses of wearable electrochemical devices in the management of chronic diseases including PD. Finally, we speculate about possible future applications of wearable electrochemical sensors in PD, such as the attractive opportunity for personalized closed-loop therapeutic approaches.

Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease.

Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson's disease (PD). However, this approach is burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered "optimum" in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.

Enhancing Gamma Oscillations Restores Primary Motor Cortex Plasticity in Parkinson's Disease.

In humans, γ oscillations in cortical motor areas reflect asynchronous synaptic activity and contribute to plasticity processes. In Parkinson's disease (PD), γ oscillatory activity in the basal ganglia-thalamo-cortical network is altered and the LTP-like plasticity elicited by intermittent theta burst stimulation (iTBS) is reduced in the primary motor cortex (M1). In this study, we tested whether transcranial alternating current stimulation (tACS) delivered at γ frequency promotes iTBS-induced LTP-like plasticity in M1 in PD patients. Sixteen patients (OFF condition) and 16 healthy subjects (HSs) underwent iTBS during γ-tACS (iTBS-γ tACS) and during sham-tACS (iTBS-sham tACS) in two sessions. Motor-evoked potentials (MEPs) evoked by single-pulse transcranial magnetic stimulation and short-interval intracortical inhibition (SICI) were recorded before and after the costimulation. A subgroup of patients also underwent iTBS during ß tACS. iTBS-sham tACS facilitated single-pulse MEPs in HSs, but not in patients. iTBS-γ tACS induced a larger MEP facilitation than iTBS-sham tACS in both groups, with similar values in patients and HSs. In patients, SICI improved after iTBS-γ tACS. The effect produced by iTBS-γ tACS on single-pulse MEPs correlated with disease duration, while changes in SICI correlated with Unified Parkinson's Disease Rating Scale Part III scores. The effect of iTBS-ß tACS on both single-pulse MEPs and SICI was similar to that obtained in the iTBS-sham tACS session. Our data suggest that γ oscillations have a role in the pathophysiology of the abnormal LTP-like plasticity in PD. Entraining M1 neurons at the γ rhythm through tACS may be an effective method to restore impaired plasticity.SIGNIFICANCE STATEMENT In Parkinson's disease, the LTP-like plasticity of the primary motor cortex is impaired, and γ oscillations are altered in the basal ganglia-thalamo-cortical network. Using a combined transcranial magnetic stimulation-transcranial alternating current stimulation approach (iTBS-γ tACS costimulation), we demonstrate that driving γ oscillations restores the LTP-like plasticity in patients with Parkinson's disease. The effects correlate with clinical characteristics of patients, being more evident in less affected patients and weaker in patients with longer disease duration. These findings suggest that cortical γ oscillations play a beneficial role in modulating the LTP-like plasticity of M1 in Parkinson's disease. The iTBS-γ tACS approach may be potentially useful in rehabilitative settings in patients.

Voice Analysis with Machine Learning: One Step Closer to an Objective Diagnosis of Essential Tremor.

BACKGROUND: Patients with essential tremor have upper limb postural and action tremor often associated with voice tremor. The objective of this study was to objectively examine voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor using voice analysis consisting of power spectral analysis and machine learning. METHODS: We investigated 58 patients (24 men; mean age ± SD, 71.7 ± 9.2 years; range, 38-85 years) and 74 age- and sex-matched healthy subjects (20 men; mean age ± SD, 71.0 ± 12.4 years; range, 43-95 years). We recorded voice samples during sustained vowel emission using a high-definition audio recorder. Voice samples underwent sound signal analysis, including power spectral analysis and support vector machine classification. We compared voice recordings in patients with essential tremor who did and did not manifest clinically overt voice tremor and in patients who were and were not under the symptomatic effect of the best medical treatment. RESULTS: Power spectral analysis demonstrated a prominent oscillatory activity peak at 2-6 Hz in patients who manifested a clinically overt voice tremor. Voice analysis with support vector machine classifier objectively discriminated with high accuracy between controls and patients who did and did not manifest clinically overt voice tremor and between patients who were and were not under the symptomatic effect of the best medical treatment. CONCLUSIONS: In patients with essential tremor, voice tremor is characterized by abnormal oscillatory activity at 2-6 Hz. Voice analysis, including power spectral analysis and support vector machine classification, objectively detected voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor. © 2021 International Parkinson and Movement Disorder Society.

Acute-onset binocular diplopia in neurological unit: Aetiological factors and diagnostic assessment.

OBJECTIVES: To investigate the aetiology of acute-onset binocular diplopia (AOBD) in neurological units and identify the key diagnostic procedures in this setting. MATERIALS AND METHODS: Clinico-demographic data from patients hospitalized for AOBD from 2008 to 2019 were retrospectively reviewed. AOBD due to an underlying neurological disorder known to cause diplopia was addressed as secondary diplopia. Ophthalmoparesis plus was defined when subtle neurological signs/symptoms other than ophthalmoparesis were detected during neurological examination. RESULTS: A total of 171 patients (mean age 57.6 years) were included in the study. A total of 89 subjects (52%) had an oculomotor disturbance consistent with sixth nerve palsy, and 42 (24.6%) showed multiple oculomotor nerve involvement. The most common cause of AOBD was presumed to be microvascular in 56 patients (32.7%), while a secondary aetiology was identified in 102 (59.6%). Ophthalmoparesis plus and multiple oculomotor nerve involvement significantly predicted a secondary aetiology in multivariable logistic regression analysis. Brain CT was never diagnostic in isolated ophthalmoparesis. A combination of neuroimaging examinations established AOBD diagnosis in 54.9% of subjects, whereas rachicentesis and neurophysiological examinations were found to be performant in the remaining cases. CONCLUSIONS: AOBD may herald insidious neurological disease, and an extensive diagnostic workup is often needed to establish a diagnosis. Neurological examination was pivotal in identifying patients at higher risk of secondary aetiology. Even in cases of apparently benign presentation, a serious underlying disease cannot be excluded. Brain MRI was found to perform well in all clinical scenarios, and it should be always considered when managing AOBD.

Shedding Light on Nocturnal Movements in Parkinson's Disease: Evidence from Wearable Technologies.

In Parkinson's disease (PD), abnormal movements consisting of hypokinetic and hyperkinetic manifestations commonly lead to nocturnal distress and sleep impairment, which significantly impact quality of life. In PD patients, these nocturnal disturbances can reflect disease-related complications (e.g., nocturnal akinesia), primary sleep disorders (e.g., rapid eye movement behaviour disorder), or both, thus requiring different therapeutic approaches. Wearable technologies based on actigraphy and innovative sensors have been proposed as feasible solutions to identify and monitor the various types of abnormal nocturnal movements in PD. This narrative review addresses the topic of abnormal nocturnal movements in PD and discusses how wearable technologies could help identify and assess these disturbances. We first examine the pathophysiology of abnormal nocturnal movements and the main clinical and instrumental tools for the evaluation of these disturbances in PD. We then report and discuss findings from previous studies assessing nocturnal movements in PD using actigraphy and innovative wearable sensors. Finally, we discuss clinical and technical prospects supporting the use of wearable technologies for the evaluation of nocturnal movements.

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender.

BACKGROUND: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. METHODS: 138 younger adults (65 males and 73 females, age range: 15-30) and 123 older adults (47 males and 76 females, age range: 40-85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. RESULTS: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. CONCLUSIONS: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.

Muscle Synergies in Parkinson's Disease.

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson's disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.

Robust and language-independent acoustic features in Parkinson's disease.

Introduction: The analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring. Intriguingly, speech analysis embeds several complexities influenced by speaker characteristics (e.g., gender and language) and recording conditions (e.g., professional microphones or smartphones, supervised, or non-supervised data collection). Moreover, the set of vocal tasks performed, such as sustained phonation, reading text, or monologue, strongly affects the speech dimension investigated, the feature extracted, and, as a consequence, the performance of the overall algorithm. Methods: We employed six datasets, including a cohort of 176 Healthy Control (HC) participants and 178 PDP from different nationalities (i.e., Italian, Spanish, Czech), recorded in variable scenarios through various devices (i.e., professional microphones and smartphones), and performing several speech exercises (i.e., vowel phonation, sentence repetition). Aiming to identify the effectiveness of different vocal tasks and the trustworthiness of features independent of external co-factors such as language, gender, and data collection modality, we performed several intra- and inter-corpora statistical analyses. In addition, we compared the performance of different feature selection and classification models to evaluate the most robust and performing pipeline. Results: According to our results, the combined use of sustained phonation and sentence repetition should be preferred over a single exercise. As for the set of features, the Mel Frequency Cepstral Coefficients demonstrated to be among the most effective parameters in discriminating between HC and PDP, also in the presence of heterogeneous languages and acquisition techniques. Conclusion: Even though preliminary, the results of this work can be exploited to define a speech protocol that can effectively capture vocal alterations while minimizing the effort required to the patient. Moreover, the statistical analysis identified a set of features minimally dependent on gender, language, and recording modalities. This discloses the feasibility of extensive cross-corpora tests to develop robust and reliable tools for disease monitoring and staging and PDP follow-up.

Parkinsonism Is Associated with Altered SMA-Basal Ganglia Structural and Functional Connectivity in Frontotemporal Degeneration.

BACKGROUND: Patients with frontotemporal degeneration (FTD) often manifest parkinsonism, which likely results from cortical and subcortical degeneration of brain structures involved in motor control. We used a multimodal magnetic resonance imaging (MRI) approach to investigate possible structural and/or functional alterations in FTD patients with and without parkinsonism (Park+ and Park-). METHODS: Thirty FTD patients (12 Park+, 18 Park-) and 30 healthy controls were enrolled and underwent 3T MRI scanning. MRI analyses included: (1) surface-based morphometry; (2) basal ganglia and thalamic volumetry; (3) diffusion-based probabilistic tractography of fiber tracts connecting the supplementary motor area (SMA) and primary motor cortex (M1) to the putamen, globus pallidus, and thalamus; and (4) resting-state functional connectivity (RSFC) between the aforementioned regions. RESULTS: Patients in Park+ and Park- groups showed comparable patterns of cortical thinning in frontotemporal regions and reduced thalamic volume with respect to controls. Only Park+ patients showed reduced putaminal volume and reduced fractional anisotropy of the fibers connecting the SMA to the globus pallidus, putamen, and thalamus, with respect to controls. Park+ patients also showed decreased RSFC between the SMA and putamen with respect to both Park- patients and controls. CONCLUSIONS: The present findings support the hypothesis that FTD patients with parkinsonism are characterized by neurodegenerative processes in specific corticobasal ganglia-thalamocortical motor loops.

Pain-motor integration in chronic pain: A neurophysiological study.

OBJECTIVE: Chronic pain may lead to functional changes in several brain regions, including the primary motor cortex (M1). Our neurophysiological study aimed to probe M1 plasticity, through a non-invasive transcranial magnetic stimulation protocol, in a cohort of patients with chronic pain. METHODS: Twenty patients with chronic pain (age ± SD: 62.9 ± 9.9) and 20 age- and sex-matched healthy controls (age ± SD: 59.6 ± 15.8) were recruited. Standardized scales were used for the evaluation of pain severity. Neurophysiological measures included laser-evoked potentials (LEPs) and motor-evoked potentials (MEPs) collected at baseline and over 60 minutes following a standardized Laser-paired associative stimulation (Laser-PAS) protocol. RESULTS: LEPs and MEPs were comparable in patients with chronic pain and controls. The pain threshold was lower in patients than in controls. Laser-PAS elicited decreased responses in patients with chronic pain. The response to Laser-PAS was similar in subgroups of patients with different chronic pain phenotypes. CONCLUSIONS: M1 plasticity, as tested by Laser-PAS, is altered in patients with chronic pain, possibly reflecting abnormal pain-motor integration processes. SIGNIFICANCE: Chronic pain is associated with a disorder of M1 plasticity raising from abnormal pain-motor integration.

Acoustic analysis in stuttering: a machine-learning study.

Background: Stuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS). Objective: We assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine - SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering. Methods: Fifty-three PWS (20 children, 33 younger adults) and 71 age-/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN). Results: Acoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings. Conclusion: Acoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment).

Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis.

Introduction: Deep brain stimulation of the subthalamic nucleus (STN-DBS) can exert relevant effects on the voice of patients with Parkinson's disease (PD). In this study, we used artificial intelligence to objectively analyze the voices of PD patients with STN-DBS. Materials and methods: In a cross-sectional study, we enrolled 108 controls and 101 patients with PD. The cohort of PD was divided into two groups: the first group included 50 patients with STN-DBS, and the second group included 51 patients receiving the best medical treatment. The voices were clinically evaluated using the Unified Parkinson's Disease Rating Scale part-III subitem for voice (UPDRS-III-v). We recorded and then analyzed voices using specific machine-learning algorithms. The likelihood ratio (LR) was also calculated as an objective measure for clinical-instrumental correlations. Results: Clinically, voice impairment was greater in STN-DBS patients than in those who received oral treatment. Using machine learning, we objectively and accurately distinguished between the voices of STN-DBS patients and those under oral treatments. We also found significant clinical-instrumental correlations since the greater the LRs, the higher the UPDRS-III-v scores. Discussion: STN-DBS deteriorates speech in patients with PD, as objectively demonstrated by machine-learning voice analysis.