Search Results (180)

Many people with Parkinson’s disease (PwPD) lack optimal care due to limited access to neurologists and a reliance on subjective rating scales for treatment decisions. The Ceraxis Insight platform was designed to provide quantitative measures of the cardinal motor symptoms of Parkinson’s disease (PD) through self-administered assessments performed using a tablet paired with a sensor-embedded stylus. The aim of this study was to assess the validity of the Ceraxis Insight outcome metrics against clinical gold-standard measures of PD motor symptoms. Nineteen PwPD completed a clinical examination and the nine Ceraxis Insight assessment modules. Quantitative performance metrics were calculated from the platform’s IMU, force transducer, and touchscreen inputs. Mixed-effect models and correlation analyses determined that multiple quantitative metrics from the Ceraxis Insight modules significantly predicted (p < 0.05) and were significantly correlated (correlation coefficients > 0.70) with the MDS-UPDRS III total score, bradykinesia, tremor, rigidity, and postural instability and gait difficulty sub-scores. Logistic regression models determined that multiple Ceraxis Insight metrics discriminated between ON- and OFF-deep brain stimulation (DBS) conditions, with Area Under the Receiver Operating Characteristic Curve (AUC) values exceeding 0.70. The Ceraxis Insight platform provides a validated, objective assessment of PD motor symptoms that may be performed within clinical or remote settings for data-driven evaluation and treatment. Full article

Background and Objectives: Head trauma (HT) has been suggested as a risk factor for Parkinson’s disease (PD), but its impact on the motor and non-motor manifestations remains unclear. We investigated whether patients with early PD and a history of HT differ from those without HT in terms of their motor and non-motor symptom profiles. Materials and Methods: We enrolled patients with early PD (disease duration of ≤5 years, modified Hoehn and Yahr stages [mHY] 1–3). HT history was ascertained by structured questionnaire. Motor and non-motor symptoms were evaluated using standardized clinical rating scales. Motor subtypes—tremor-dominant (TD), akinetic-rigid (AR), and mixed—were determined according to established criteria based on the tremor-to-AR score ratio. Subtype distribution and motor scores were compared between HT and non-HT groups using univariate tests, mHY-adjusted ANCOVA, and multivariable models adjusting for age, sex, disease duration, education, mHY stage, MMSE, and BDI score. Results: Of 237 patients, 35 (14.8%) reported HT. The HT group had a higher mHY stage than the non-HT group and showed lower total and rest tremor scores on the Unified Parkinson’s Disease Rating Scale, whereas rigidity scores were similar. Bradykinesia and gait/posture scores tended to be higher in the HT group, but these differences did not persist after adjustment for disease severity. The TD subtype was less frequent in the HT group than in the non-HT group (5.7% vs. 30.2%), whereas the AR subtype was more common (82.9% vs. 61.9%). The categorical subtype redistribution remained significant after multivariable adjustment (adjusted OR for AR vs. TD = 4.61, 95% CI 1.28–16.67). No detectable between-group difference in non-motor symptom burden was observed. Conclusions: In this single-center cross-sectional cohort, a self-reported history of HT in early PD was associated with a redistribution of motor phenotype categories toward AR-predominant presentations, with no detectable difference in non-motor symptom burden. Given the retrospective binary exposure assessment, these findings should be interpreted as hypothesis-generating, and prospective studies with structured exposure ascertainment are needed to clarify how HT may shape PD motor phenotype expression. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects millions globally, particularly those in the elderly population. Several occupational exposures typical of maritime environments are recognized or suspected risk factors for PD, warranting attention within occupational health frameworks. The disease is characterized by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor impairments including speech abnormalities. Objective: Early diagnosis is crucial for effective disease management but remains challenging due to symptoms overlapping with normal aging and other neurological conditions. This study presents a machine learning (ML)-based approach for the early diagnosis of PD using speech signal analysis. Methods: We employed six supervised ML classifiers to differentiate between PD patients and healthy controls based on vocal features. The experimental dataset, MDVR-KCL, consists of speech recordings from both reading tasks and spontaneous dialogs, collected via mobile devices. From these recordings, we extracted Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Frequency Cepstral Coefficients (GTCCs), and acoustic features such as jitter, shimmer, and harmonic-to-noise ratio. These features capture a broad range of prosodic, spectral, and articulatory characteristics associated with PD-related speech impairments. Speaker diarization was applied in spontaneous dialog recordings to separate participant speech. Hyperparameter tuning was performed using GridSearchCV with 10-fold cross-validation, while final model evaluation was conducted using Leave-One-Subject-Out Cross-Validation (LOSOCV) to ensure subject-independent performance assessment. Results: In the read-text task, the SVM model performed exceptionally, yielding 95.45% accuracy, 94.62% sensitivity, 95.97% specificity, an F1-score of 94.12%, and an AUC of 0.98 with an MCC value of 0.90, for GTCCs with the acoustic features. In the spontaneous dialog task, the XGB model demonstrated the highest overall performance across all metrics, with a test accuracy of 83.7%, a sensitivity of 76.3.9%, a specificity of 88.9%, an F1-score of 79.5%, an AUC value of 0.88, and an MCC value of 0.66. Conclusions: Comparable results were obtained on both spontaneous dialog and reading speech subsets, demonstrating the robustness of the approach across different speaking contexts. These results demonstrate the effectiveness of integrating cepstral and acoustic features with machine learning models for non-invasive PD classification. The findings support the use of speech-based digital biomarkers in early PD detection and highlight the potential for developing scalable tools. This work highlights the potential of speech-based digital diagnostics to support clinical decision-making and improve patient outcomes. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder affecting millions worldwide, characterized by motor symptoms such as tremor, rigidity, and bradykinesia that significantly impair daily life. The current diagnosis and monitoring rely primarily on clinical observations and rating scales (e.g., the MDS-UPDRS), which are subjective and limited in detecting subtle motor alterations, leading to inter- and intra-rater variability. In recent years, wearable sensors such as surface electromyography (sEMG) and inertial measurement units (IMUs) have emerged as non-invasive tools for quantifying neuromuscular activity and motor performance in PD. When combined with machine learning (ML) and deep learning (DL) techniques, these signals enable the development of models for disease detection, patient classification, and symptom severity assessment. This review provides a structured overview of recent ML and DL approaches applied to surface electromyography for PD monitoring, addressing a gap in the current literature. It analyzes data acquisition strategies, preprocessing techniques, feature extraction methods, model architectures, and evaluation protocols across tasks such as diagnosis, tremor analysis, freezing of gait detection, and gait assessment. Despite promising results, key challenges remain, including limited dataset size, lack of standardization, and poor generalization. Finally, this work highlights emerging trends and identifies a representative processing pipeline to support real-world clinical translation. Full article

Continuous monitoring of the Stand-to-Sit (STS) transition serves as a critical indicator of lower-limb frailty in the elderly, for which millimeter-wave radar provides an ideal privacy-preserving, device-free sensing solution. However, robustly distinguishing between safe Controlled Sits (CSs) and dangerous Uncontrolled Descents (UDs) is severely hindered by the prohibitive cost of subjective expert scoring for fine-grained labels, alongside the pervasive “Clever Hans” effect where existing deep models overfit static environmental clutter rather than learning intrinsic human kinematics. To circumvent these bottlenecks, we formulate STS evaluation as a dynamic boundary detection problem and propose SCA-BiMamba, a linear-complexity bidirectional State-Space Model that utilizes actual fall events as extreme kinematic surrogates for UDs. This forces the network to learn a strict physical boundary between CS and physiological failure without subjective grading. Furthermore, we establish a stringent Real-to-Sim diagnostic audit as a core methodological contribution. By projecting models trained on noisy real-world data onto pure-kinematics simulations—incorporating stochastic temporal phase shifts, kinematic overlaps, and unified physiological tremors—we explicitly quantify feature disentanglement. This protocol serves as a formal ‘probing test’ to expose the ‘Clever Hans’ effect, ensuring the model relies on invariant human physics rather than transient environmental artifacts. Extensive experiments demonstrate that SCA-BiMamba achieves highly robust classification on real-world data (averaging 94.2% Macro F1 with 100.0% Uncontrolled Descent Recall), and achieves a highly robust 99.4% ± 1.1% Macro F1 in the simulated zero-shot transfer. We emphasize that this optimal performance reflects the successful abstraction of extreme kinematic boundaries, rather than a flawless resolution of all clinical complexities. Concurrently, it exhibits strict resistance to shortcut learning and sustains robust real-world scalability using merely 20% of the training data, thereby establishing a promising privacy-preserving boundary-based radar motion classification framework for distinguishing controlled sitting from extreme instability surrogates. Full article

Background/Objectives: Preservation of critical white matter (WM) pathways is essential for maximizing surgical safety in neuro-oncology and functional neurosurgery. Constrained spherical deconvolution (CSD) offers superior modeling of complex fiber architecture compared to diffusion tensor imaging (DTI). This case series evaluates the clinical utility of CSD in surgical planning and intraoperative navigation. Methods: A retrospective review of 20 patients (15 brain tumors, 5 functional disorders) treated between September 2022, and September 2024 was performed. All patients underwent preoperative MRI with CSD-based reconstruction of eloquent WM tracts. Clinical presentation, tract involvement, surgical strategy, and postoperative outcomes were analyzed. Results: CSD reliably reconstructed CST, AF, IFOF, OT, and DRTT depending on tumor location or DBS target. Compared with standard DTI, CSD provided improved delineation of tract extent and tumor–tract interfaces. Gross total resection (GTR) was achieved in all tumor patients without new neurological deficits. DBS cases showed precise correlation between stimulation thresholds, side effects, and CSD-predicted distances to critical WM tracts. DRTT targeting resulted in marked clinical improvement in Holmes tremor. Conclusions: CSD enhances anatomical accuracy in WM tract visualization, supporting safer resections in eloquent areas and improving DBS targeting. Its integration into routine workflow may optimize neurosurgical outcomes. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily caused by the degeneration of dopamine-producing neurons in the substantia nigra, leading to characteristic motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor manifestations including depression, sleep disturbances, and speech impairments. Among these symptoms, speech abnormalities affect approximately 90% of individuals with PD, making acoustic analysis a promising non-invasive cue for early detection. However, subtle speech variations are often imperceptible to the human ear, and speech-only analysis may overlook complementary visual manifestations, such as hypomimia—reduced facial expressivity commonly observed in PD patients. To address these limitations, we propose Parkinson’s Detection via Attentional Fusion Network (PDAF-Net), a novel multimodal deep learning framework for early PD detection that jointly models acoustic and facial dynamic features in a binary classification setting. The proposed architecture consists of a Dual-Stream Feature Encoder (DSFE), with an audio branch based on a one-dimensional convolutional neural network (1D-CNN) and bidirectional long short-term memory (BiLSTM), and a visual branch built upon a two-dimensional convolutional neural network (2D-CNN) and a Transformer encoder. Multimodal integration is achieved through a Cross-Attention-guided Attentional Feature Fusion (CA-AFF) module, which explicitly models bidirectional cross-modal interactions and performs adaptive feature recalibration via an iterative attentional fusion mechanism. We conducted experiments on a self-collected Chinese multimodal dataset comprising 100 PD patients and 100 healthy controls. Although the data are balanced at the subject level, sliding-window segmentation introduces sample-level imbalance; to address this issue, a class-balanced focal loss is employed. Model performance was evaluated using subject-wise five-fold cross-validation. The results demonstrate that PDAF-Net consistently outperforms unimodal baselines across multiple evaluation metrics, achieving an accuracy of 89.3%, an F1-score of 0.884, and an AUC of 0.916. These findings highlight the effectiveness of explicit cross-modal interaction modeling and adaptive feature fusion for improving automated early PD screening in real-world clinical settings. Full article

The study investigates the application of machine learning techniques for classifying rockbursts among non-destructive tremors recorded in the Rydułtowy part of the ROW hard coal mine in the Upper Silesian Coal Basin, Poland. The mining environment is dominated by ultra-thick, high-strength sandstone strata, which significantly increase the likelihood of high-energy tremors. The interaction of geological/geomechanical, mining, technical/technological, and seismic factors is highly nonlinear, rendering deterministic analytical approaches insufficient for reliable rockburst identification. A dataset comprising 99 records, including 16 dynamic phenomena, was divided into training and testing subsets, with 75% of the data used to evaluate the discriminative power of the input variables and to train the machine learning models. Three parameters consistently exhibit the highest predictive relevance: peak particle velocity, seismic energy, and the rock mass bursting tendency index. Ten machine learning classifiers were evaluated using stratified 10-fold cross-validation. Ensemble-based models—particularly XGBoost, AdaBoost and Random Forest—demonstrated the most stable and accurate performance. The results indicate that machine learning models provide an effective computational framework for supporting rockburst hazard assessment in geologically complex mining conditions associated with ultra-thick sandstone strata. Full article

Background and Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder marked by bradykinesia, rigidity, and tremor. While deep brain stimulation (DBS) of the subthalamic nucleus (STN) and globus pallidus internus (GPi) effectively alleviates motor symptoms, the potential of targeting the substantia nigra pars reticulata (SNr) is less understood. This study investigates the effects of mid-term DBS of the SNr on motor function and neuroplasticity in a 6-hydroxydopamine (6-OHDA) rat model of PD. Methods: Adult male Sprague-Dawley rats (280–300 g) were divided into healthy control (n = 10), PD (n = 9), sham-DBS (n = 7), and SNr-DBS (n = 7) groups. Bilateral striatal 6-OHDA lesions induced PD. High-frequency (130 Hz, 60 µs) SNr-DBS was delivered for 14 days. Locomotor activity (open-field), gait (footprint method), and motor coordination (rotarod) were assessed. Tyrosine hydroxylase (TH) expression in the SN and c-Fos and BDNF expression in the cerebellum, prefrontal cortex (PFC), and ventrolateral thalamus were analyzed histologically. Results: SNr-DBS significantly improved ambulation and horizontal activity compared to the PD group (p < 0.05). Gait analysis showed significant improvements in forelimb/hindlimb stride length and stance width, while rotarod performance indicated enhanced motor coordination (p < 0.05). Histology revealed increased TH expression in the SN and elevated c-Fos and BDNF levels in the cerebellum, PFC, and thalamus in the SNr-DBS group vs. PD rats (p < 0.05). Conclusions: Mid-term SNr-DBS produced significant functional gains in motor activity and coordination in a 6-OHDA PD model, together with molecular evidence of dopaminergic enhancement and neuroplastic activation. These translational findings suggest that targeting the SNr may offer a clinically relevant alternative for patients with PD, particularly for those who may not optimally respond to conventional STN or GPi stimulation. Full article

Background and Objectives: Surface electromyography (EMG) is widely used to assess muscle activation. However, direct interpretation of its functional biomechanical consequences remains challenging. This study aimed to develop and evaluate an EMG-driven musculoskeletal simulation framework for investigating how controlled modifications of muscle activation patterns influence joint-level biomechanics in the upper limb. The objective was not to reproduce specific clinical pathologies but to enable systematic virtual scenario analysis of activation-dependent movement alterations. Materials and Methods: Surface EMG signals were recorded from five healthy adults (3 males, 2 females; age 22 ± 1 years) during cyclic elbow flexion/extension tasks using a wireless system (sampling frequency: 2000 Hz). Processed and normalized EMG envelopes were directly applied as prescribed neural inputs in forward dynamic simulations implemented in OpenSim, without optimization-based muscle recruitment. Controlled virtual scenarios were generated through parametric modification of activation signals to represent reduced activation capacity, increased antagonist co-activation, spasticity-like activation modulation, and tremor-like oscillatory modulation. Joint kinematics, joint moments, and movement stability were evaluated. A Movement Quality Index (MQI) was introduced as a comparative research metric integrating biomechanical performance indicators. Simulations were deterministic and analyzed descriptively. Results: Distinct activation modifications produced characteristic kinematic and kinetic responses. Reduced activation capacity decreased simulated joint moment output, increased co-activation altered joint moment timing and mechanical stability, and tremor-like oscillatory modulation generated periodic fluctuations in joint kinematics and kinetics. The MQI enabled quantitative differentiation between simulated scenarios and severity levels within the controlled modelling framework. Conclusions: The proposed EMG-driven forward dynamic simulation framework provides a methodological platform for controlled virtual scenario analysis of activation-dependent biomechanical changes. The findings highlight the sensitivity of joint-level mechanics to altered muscle activation patterns, within the deterministic modelling environment. The framework is intended for research-oriented biomechanical investigation and hypothesis testing rather than direct clinical diagnosis of neuromuscular disorders. Full article

This work presents the results of an integrated monitoring of soil radon gas and seismic activity at Mt. Etna from August 2023 to May 2025, aimed at enhancing comprehension of magma migration and eruption dynamics. Radon data were collected using a permanent station with an alpha particle probe, aggregated hourly. The INGV-OE network monitored seismic activity at 100 Hz; volcanic tremor was analyzed using Root-Mean-Square (RMS) values from the Serra La Nave station. Earthquakes were located using the Hypoellipse algorithm and a 1D crustal velocity model. A robust correlation was found between radon and RMS anomalies, with the former preceding the latter with increasing probability over time (e.g., 30.1% within 1 day, 46.4% within 3 days). Correlations were also found between radon anomalies and Strombolian activity at the summit craters (e.g., 23.8% within 1 day for the Central Crater), suggesting a potential predictive role for radon. Conversely, correlations with paroxysmal events were weaker in the short term but increased over longer time windows. No clear correlation was found between radon anomalies and seismic strain release, likely due to differing temporal resolutions. These results support the idea that radon plays a role as a short-term precursor in volcanic unrest. Full article

This meta-analysis investigates the acute (immediate) pre–post changes in the modulation of physiological tremor in healthy adults following physical exercise, including resistance-based protocols. Physiological tremor is characterized by low-amplitude, high-frequency oscillations during posture or movement and reflects transient changes in neuromuscular control. Background/Objectives: Quantify the pooled effect of physical exercise on physiological tremor amplitude in healthy adults using magnitude-based metrics (RMS, peak power). A secondary objective was to synthesize evidence from acute resistance-based protocols separately. Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and followed the methodological framework outlined in the Cochrane Handbook for Systematic Reviews of Interventions. Thirteen experimental studies met the inclusion criteria, with eleven included in the general exercise analysis and eight in the acute resistance-based subset. Results: Random-effects models revealed a moderate reduction in tremor amplitude following acute exercise (Hedges’ g = −0.42, p < 0.001). The resistance-based synthesis was restricted to acute single-session protocols only and indicated a directionally consistent reduction in tremor amplitude. Conclusions: These findings suggest that physical exertion is associated with transient suppression of physiological tremor amplitude. Acute single-session resistance-based exercise protocols showed a consistent direction of effect, although pooled estimates should be interpreted cautiously due to heterogeneity. Overall, physiological tremor may serve as a sensitive, non-invasive outcome measure reflecting short-term neuromuscular state. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative condition with a multifactorial etiology, characterized by dopaminergic neurons being selectively absent in the midbrain. Clinically, PD manifests primarily with core motor symptoms of resting tremor, bradykinesia, and muscle rigidity, and is often accompanied by non-motor symptoms including depression, cognitive impairment, and gastrointestinal dysfunction. Among the extensive relevant research, few have explored the precise pathogenic mechanisms underlying PD, and no curative treatment is available. Current pharmacological therapies mainly provide symptomatic relief by enhancing central dopaminergic function or modulating cholinergic activity; however, their long-term efficacy is frequently constrained by waning therapeutic response, drug tolerance, and adverse reactions. Accumulating evidence suggests that several naturally derived neuroactive compounds—such as gastrodin, uncarin, and paeoniflorin—demonstrate significant potential in combating PD. In this systematic review, we examined original research articles published from 2010 to 2025, retrieved from PubMed, Web of Science, and CNKI databases, using predefined keywords of Parkinson’s disease, neuroprotective herbal compounds, traditional medicine, multi-target mechanisms, natural product, autophagy, neuroinflammation, and oxidative stress. Studies were included if they specifically investigated the mechanistic actions of natural compounds in PD models. Conference abstracts, review articles, publications not in English or Chinese, and studies lacking clearly defined mechanisms were excluded. Analysis of the available literature reveals that natural neuroactive compounds may exert anti-PD effects through multiple mechanisms, e.g., inhibiting pathological α-synuclein aggregation, attenuating neuronal apoptosis, suppressing neuroinflammation, mitigating oxidative stress, and restoring mitochondrial dysfunction. This review provides insights that may inform the clinical application of natural bioactive compounds and guide their further development as potential therapeutic candidates against PD. Full article

Background: Essential tremor (ET) is among the most common neurological disorders, requiring precise diagnosis and severity assessment for personalized and effective management. Methods: This study explores an innovative approach to evaluate ET severity using the gold-standard Archimedes spiral test. The family-based dataset covers the entire range of tremor severity, from very mild (level 1) to advanced stages, offering a valuable resource for studying early diagnosis and tracking disease progression. The proposed method introduces a machine learning pipeline that combines Principal Component Analysis (PCA), linear discriminant analysis (LDA), and support vector machines (SVMs) to classify ET severity based on Archimedean spiral radius data. Results: By incorporating the Fahn–Tolosa–Marin Tremor Rating Scale (FMT-TRS), the pipeline effectively distinguishes between tremor presence and severity. Its robustness was demonstrated through rigorous cross-validation and tests involving Gaussian noise perturbations. Conclusions: These results underscore the machine learning-based pipeline’s potential as a non-invasive and trustworthy diagnostic tool for clinical use and telemedicine applications. Moreover, the combination of geometric features, FMT-TRS scores, clinically oriented evaluation metrics, and classical statistical and machine learning models offers a robust, interpretable, explainable, and clinically meaningful analytical framework. Full article

Background/Objective: Central pathophysiological heterogeneity among Parkinson’s disease (PD) motor subtypes has been increasingly recognized, yet subtype-specific peripheral disturbances are limited. We aimed to characterize demographic, biochemical, and neurophysiological differences among PD motor subtypes, evaluate hematoinflammatory effects on peripheral and proximal motor conduction, and identify prognostic phenotypic biomarkers. Methods: A total of 110 participants (60 idiopathic PD patients (30 akinetic-rigid (AR), 30 tremor-predominant (TD), and 50 age- and sex-matched healthy controls (HCs)) were enrolled. Demographic data, nerve conduction studies (NCS) including detailed F-wave analysis, and hematoinflammatory markers were collected. Kruskal–Wallis, linear mixed models, multivariable regression, and ROC analyses were applied. Results: Hematoinflammatory indices were elevated in both subtypes compared with HCs, with more pronounced changes in AR (mean platelet volume (MPV) H = 4.367, p = 0.003; systemic inflammatory response index (SIRI) H = 3.929, p = 0.004). AR showed severe upper-limb–predominant motor involvement (median motor onset latency H = 55.30, p < 0.001; amplitude H = 50.52, p = 0.04; conduction velocity H = 49.15, p < 0.001), whereas TD showed milder, lower-limb–predominant changes (tibial motor onset latency H = 19.89, p < 0.001; amplitude H = 51.50, p = 0.02; velocity H = 15.39, p < 0.001). AR also demonstrated prolonged minimal (Fmin)/mean (Fmean) ulnar F-wave latencies versus TD (respectively, H = 10.51, p = 0.001; H = 8.79, p = 0.003), with both showing increased tibial Fmean/Fmax latencies. Platelet–eosinophil indices independently predicted ulnar F-latencies (B = 0.104–0.105; p = 0.001; model R² = 0.21–0.39). Select F-wave metrics yielded ROC AUCs ≈ 0.65–0.92 (ulnar Fmin AUC ≈ 0.92 vs. HCs); AR achieved sensitivity/specificity ≈ 70–74%. Conclusions: The AR subtype showed increased hematoinflammatory changes, specifically in MPV and SIRI, as well as a tendency toward more pronounced proximal motor and peripheral nerve conduction impairment compared with TD. Platelet–eosinophil indices and F-wave metrics may represent potential candidate markers for diagnostic or stratification purposes in PD subtyping and could possibly aid in prognostic estimation. Full article