In humans, γ oscillations in cortical motor areas reflect asynchronous synaptic activity and contribute to plasticity processes. In Parkinson9s 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 β 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 Parkinson9s Disease Rating Scale Part 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. SIGNIFICANCE STATEMENT In Parkinson9s 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 Parkinson9s 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 Parkinson9s disease. The iTBS-γ tACS approach may be potentially useful in rehabilitative settings in patients.

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.

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.

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.