Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life.

BACKGROUND: Dyskinesias and freezing of gait are episodic disorders in Parkinson's disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. METHODS: Seventy-one patients with Parkinson's disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. RESULTS: The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. CONCLUSIONS: A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson's disease.

Disentangling Bradykinesia and Rigidity in Parkinson Disease: Evidence from Short- and Long-Term Subthalamic Nucleus Deep Brain Stimulation.

OBJECTIVE: Bradykinesia and rigidity are considered closely related motor signs in Parkinson disease (PD), but recent neurophysiological findings suggest distinct pathophysiological mechanisms. This study aims to examine and compare longitudinal changes in bradykinesia and rigidity in PD patients treated with bilateral subthalamic nucleus deep brain stimulation (STN-DBS). METHODS: In this retrospective cohort study, the clinical progression of appendicular and axial bradykinesia and rigidity was assessed up to 15 years after STN-DBS in the best treatment conditions (ON medication and ON stimulation). The severity of bradykinesia and rigidity was examined using ad hoc composite scores from specific subitems of the Unified Parkinson's Disease Rating Scale motor part (UPDRS-III). Short- and long-term predictors of bradykinesia and rigidity were analyzed through linear regression analysis, considering various preoperative demographic and clinical data, including disease duration and severity, phenotype, motor and cognitive scores (eg, frontal score), and medication. RESULTS: A total of 301 patients were examined before and 1 year after surgery. Among them, 101 and 56 individuals were also evaluated at 10-year and 15-year follow-ups, respectively. Bradykinesia significantly worsened after surgery, especially in appendicular segments (p < 0.001). Conversely, rigidity showed sustained benefit, with unchanged clinical scores compared to preoperative assessment (p > 0.05). Preoperative motor disability (eg, composite scores from the UPDRS-III) predicted short- and long-term outcomes for both bradykinesia and rigidity (p < 0.01). Executive dysfunction was specifically linked to bradykinesia but not to rigidity (p < 0.05). INTERPRETATION: Bradykinesia and rigidity show long-term divergent progression in PD following STN-DBS and are associated with independent clinical factors, supporting the hypothesis of partially distinct pathophysiology. ANN NEUROL 2024.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.

Cohort analysis of novel SPAST variants in SPG4 patients and implementation of in vitro and in vivo studies to identify the pathogenic mechanism caused by splicing mutations.

Introduction: Pure hereditary spastic paraplegia (SPG) type 4 (SPG4) is caused by mutations of SPAST gene. This study aimed to analyze SPAST variants in SPG4 patients to highlight the occurrence of splicing mutations and combine functional studies to assess the relevance of these variants in the molecular mechanisms of the disease. Methods: We performed an NGS panel in 105 patients, in silico analysis for splicing mutations, and in vitro minigene assay. Results and discussion: The NGS panel was applied to screen 105 patients carrying a clinical phenotype corresponding to upper motor neuron syndrome (UMNS), selectively affecting motor control of lower limbs. Pathogenic mutations in SPAST were identified in 12 patients (11.42%), 5 missense, 3 frameshift, and 4 splicing variants. Then, we focused on the patients carrying splicing variants using a combined approach of in silico and in vitro analysis through minigene assay and RNA, if available. For two splicing variants (i.e., c.1245+1G>A and c.1414-2A>T), functional assays confirm the types of molecular alterations suggested by the in silico analysis (loss of exon 9 and exon 12). In contrast, the splicing variant c.1005-1delG differed from what was predicted (skipping exon 7), and the functional study indicates the loss of frame and formation of a premature stop codon. The present study evidenced the high splice variants in SPG4 patients and indicated the relevance of functional assays added to in silico analysis to decipher the pathogenic mechanism.

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.

Effective connectivity abnormalities in Lewy body disease with visual hallucinations.

OBJECTIVE: To assess the changes in effective connectivity of important regions of the visual network (VIS) and dorsal attention network (DAN) underlying visual hallucinations (VHs) in Dementia with Lewy Bodies (DLB), Parkinson's Disease (PD) and Parkinson's Disease Dementia (PDD), as measured by a transcranial magnetic stimulation-electroencephalographic technique (TMS-EEG). METHODS: We stimulated the right visual cortex (V1/V2), the right intraparietal sulcus and the right frontal eye fields, two key regions of the DAN, and measured TMS-evoked cortical activation within the VIS and the DAN. We compared 11 patients with VHs and 15 patients without VHs. RESULTS: Patients with VHs showed lower TMS-evoked cortical activation within the DAN following intraparietal sulcus and frontal eye fields stimulation than patients without VHs. No difference was found between patients with and without cognitive impairment. Also, when considering only patients with cognitive impairment, VHs were associated with lower TMS-evoked cortical activation following intraparietal sulcus stimulation. CONCLUSIONS: DLB, PD, and PDD patients with VHs had less effective connectivity of the right intraparietal sulcus within the DAN than patients without VHs. SIGNIFICANCE: We provided the first evidence that VHs are associated with specific intraparietal sulcus dysfunction within the DAN in patients with PDD, PD, and DLB.

Four Days Are Enough to Provide a Reliable Daily Step Count in Mild to Moderate Parkinson's Disease through a Commercial Smartwatch.

Daily steps could be a valuable indicator of real-world ambulation in Parkinson's disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland-Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2-5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann-Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84-0.90; SAM range: 7.8-9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD.

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).

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.

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.

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.

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.

Botulinum toxin in clinical practice: state of the art of administration provisions in Italy.

Botulinum neurotoxin type A is a remarkable therapeutic approach for muscle hyperactivity syndromes, pain, and related disorders. Despite its wide application in neurology, there is a poor knowledge on delivery protocols and dispatch from the healthcare providers. In this study, we reported the result of a 2020 survey about the administration provisions of botulinum neurotoxin type A in Italy. Seven questions including information on characteristics of botulinum neurotoxin facilities, prescription, reimbursement, and execution modalities were adopted. Sixty participants answered the survey. Despite the wide availability of dedicated centers all over the national territory, there was a surprising lack of standardized and shared administration provisions. Most of the Italian medical structures delivered botulinum neurotoxin through outpatient clinics located in public hospital facilities, through the "F file" reimbursement modality. However, there was no agreement on the reimbursement request modality, creating differences in public costs relative to the botulinum toxin consumption across Italy.

Familial adult myoclonus epilepsy: Neurophysiological investigations.

Familial adult myoclonus epilepsy (FAME) also described as benign adult familial myoclonus epilepsy (BAFME) is a high-penetrant autosomal dominant condition featuring cortical myoclonus of varying frequency and occasional/rare convulsive seizures. In this update we provide a detailed overview of the main neurophysiological findings so far reported in patients with FAME/BAFME. After reviewing the diagnostic contribution of each neurophysiological technique, we discuss the possible mechanisms underlying cortical hyperexcitability and suggest the involvement of more complex circuits engaging cortical and subcortical structures, such as the cerebellum. We, thus, propose that FAME/BAFME clinical features should arise from an "abnormal neuronal network activity," where the cerebellum represents a possible common denominator. In the last part of the article, we suggest that future neurophysiological studies using more advanced transcranial magnetic stimulation (TMS) protocols could be used to evaluate the functional connectivity between the cerebellum and cortical structures. Finally, non-invasive brain stimulation techniques such as repetitive TMS or transcranial direct current stimulation could be assessed as potential therapeutic tools to ameliorate cortical excitability.

Language-related motor facilitation in Italian Sign Language signers.

Linguistic tasks facilitate corticospinal excitability as revealed by increased motor evoked potential (MEP) induced by transcranial magnetic stimulation (TMS) in the dominant hand. This modulation of the primary motor cortex (M1) excitability may reflect the relationship between speech and gestures. It is conceivable that in healthy individuals who use a sign language this cortical excitability modulation could be rearranged. The aim of this study was to evaluate the effect of spoken language tasks on M1 excitability in a group of hearing signers. Ten hearing Italian Sign Language (LIS) signers and 16 non-signer healthy controls participated. Single-pulse TMS was applied to either M1 hand area at the baseline and during different tasks: (i) reading aloud, (ii) silent reading, (iii) oral movements, (iv) syllabic phonation and (v) looking at meaningless non-letter strings. Overall, M1 excitability during the linguistic and non-linguistic tasks was higher in LIS group compared to the control group. In LIS group, MEPs were significantly larger during reading aloud, silent reading and non-verbal oral movements, regardless the hemisphere. These results suggest that in hearing signers there is a different modulation of the functional connectivity between the speech-related brain network and the motor system.