Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson's disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used ...for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG.
A cohort of 11 Parkinson's disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes.
The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy.
Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm's effectiveness.
In the last three decades, a number of non-invasive brain stimulation (NIBS) protocols, capable of assessing and modulating plasticity in the human motor cortex (M1), have been described. For almost ...as long, NIBS has delivered the tantalising prospect of non-invasive neuromodulation as a therapeutic intervention for neurorehabilitation, psychiatry, chronic pain and other disease states. Apart from modest effects in depression, this early promise has not been realised since the symptomatic improvements produced by NIBS are generally weak. One key factor explaining this lack of clinical translation concerns variability in response to NIBS. Several studies have demonstrated a number of physiological, technical and statistical factors accounting for intra- and inter-subject variability. However, solutions to overcome this problem are still under debate. In the present review, we have provided a detailed description of methodological and technical solutions to control known factors influencing variability. We have also suggested potential strategies to strengthen and stabilize NIBS-induced after-effects. Finally, we propose new possible outcome variables which better reflect intrinsic cortical activity, allowing a more sensitive measurement and valid interpretation of responses to NIBS.
In humans, gamma (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 long-term potentiation (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 (HS) 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 TMS and short-interval intracortical inhibition (SICI) were recorded before and after the co-stimulation. A subgroup of patients also underwent iTBS during β tACS. iTBS-sham tACS facilitated single-pulse MEPs in HS, but not in patients. iTBS-γ tACS induced a larger MEPs facilitation than iTBS-sham tACS in both groups, with similar values in patients and HS. 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 UPDRS-III scores. The effect of iTBS-β 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.
In Parkinson's disease, the LTP-like plasticity of the primary motor cortex is impaired, and gamma oscillations are altered in the basal ganglia-thalamo-cortical network. Using a combined TMS-tACS approach (iTBS-γ tACS co-stimulation), we demonstrate that driving gamma 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 gamma 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.
We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder ...with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.
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.
In Parkinson's disease (PD), alpha-synuclein (a-syn) can be detected in biological fluids including saliva. Although previous studies found reduced a-syn total (a-syntotal) concentration in saliva of ...PD patients, no studies have previously examined salivary a-syn oligomers (a-synolig) concentrations or assessed the correlation between salivary a-syntotal, a-synolig and clinical features in a large cohort of PD patients. Is well known that a-synolig exerts a crucial neurotoxic effect in PD. We collected salivary samples from 60 PD patients and 40 age- and sex-comparable healthy subjects. PD was diagnosed according to the United Kingdom Brain Bank Criteria. Samples of saliva were analyzed by specific anti-a-syn and anti-oligomeric a-syn ELISA kits. A complete clinical evaluation of each patient was performed using MDS-Unified Parkinson's Disease Rating Scale, Beck Depression Inventory, Montreal Cognitive Assessment and Frontal Assessment Battery. Salivary a-syntotal was lower, whereas a-synolig was higher in PD patients than healthy subjects. The a-synolig/a-syntotal ratio was also higher in patients than in healthy subjects. Salivary a-syntotal concentration negatively correlated with that of a-synolig and correlated with several patients' clinical features. In PD, decreased salivary concentration of a-syntotal may reflect the reduction of a-syn monomers (a-synmon), as well as the formation of insoluble intracellular inclusions and soluble oligomers. The combined detection of a-syntotal and a-synolig in the saliva might help the early diagnosis of PD.
Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, ...various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.
It is well established that the primary motor cortex (M1) plays a significant role in motor learning in healthy humans. It is unclear, however, whether mechanisms of motor learning include M1 ...oscillatory activity. In this study, we aimed to test whether M1 oscillations, entrained by transcranial Alternating Current Stimulation (tACS) at motor resonant frequencies, have any effect on motor acquisition and retention during a rapid learning task, as assessed by kinematic analysis. We also tested whether the stimulation influenced the corticospinal excitability changes after motor learning. Sixteen healthy subjects were enrolled in the study. Participants performed the motor learning task in three experimental conditions: sham-tACS (baseline), β-tACS and γ-tACS. Corticospinal excitability was assessed with single-pulse TMS before the motor learning task and 5, 15, and 30 min thereafter. Motor retention was tested 30 min after the motor learning task. During training, acceleration of the practiced movement improved in the baseline condition and the enhanced performance was retained when tested 30 min later. The β-tACS delivered during training inhibited the acquisition of the motor learning task. Conversely, the γ-tACS slightly improved the acceleration of the practiced movement during training but it reduced motor retention. At the end of training, corticospinal excitability had similarly increased in the three sessions. The results are compatible with the hypothesis that entrainment of the two major motor resonant rhythms through tACS over M1 has different effects on motor learning in healthy humans. The effects, however, were unrelated to corticospinal excitability changes.
•β-tACS delivered during training inhibits the acquisition of a motor learning task.•γ-tACS slightly improved the acceleration of the practiced movement during training.•Neither β nor γ tACS influenced practice-related corticospinal excitability changes.•The effects of tACS on motor behavior were unrelated to corticospinal excitability changes.
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.
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.
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