Parkinson's disease (PD) is characterized by alterations in movement dynamics that are difficult to quantify with conventional clinical assessment. This study proposes an integrated approach combining graph-based kinematic analysis with explainable machine learning to identify digital biomarkers of Parkinsonian motor impairment. Kinematic signals were acquired using Xsens inertial sensors from 51 patients with PD and 53 healthy controls. For each participant, subject-specific kinematic networks were constructed by modeling inter-segment similarities through Jensen-Shannon divergence, from which global and local graph-theoretical metrics were extracted. A machine learning pipeline incorporating voting feature selection, and XGBoost classification was evaluated using a nested cross-validation design. The model achieved robust performance (AUC = 0.87), and explainability analyses using SHAP identified a subset of 13 features capturing alterations in velocity, inter-segment connectivity, and network centrality. PD was characterized by increased positional variability, reduced distal limb velocity, and a redistribution of network centrality towards proximal body segments. These features were associated with clinical severity, confirming their physiological relevance. By integrating graph-theoretical modeling, explainable artificial intelligence, and machine learning methodology, this work provides a method of discovering quantitative biomarkers capturing alterations in motor coordination. These findings highlight the potential of ML and kinematic networks to support objective motor assessment in PD.

BACKGROUND:Isolated REM sleep behavior disorder (iRBD) is the prodromal phase of α-synucleinopathies. Peripheral inflammation, indexed by the neutrophil-to-lymphocyte ratio (NLR), has been associated with cognitive dysfunctions in established α-synucleinopathies, but its role in prodromal stages remains unclear. Based on this background, we investigated the association between NLR and cognitive performance in individuals with iRBD. METHODS:We analyzed cross-sectional data from 371 individuals with iRBD and 262 controls from the Parkinson's Progression Markers Initiative. Baseline assessments included demographic and clinical variables, NLR derived from peripheral blood counts, and a comprehensive neuropsychological battery. Cross-sectional associations between NLR and cognitive performance were examined using univariate and multivariable linear regression models adjusted for age, sex, education, APOE ε4 status, and motor severity. Longitudinal analyses were conducted in a subgroup of 38 iRBD participants followed for 2-5 years to evaluate the relationship between baseline NLR and changes in cognitive performance over time. RESULTS:At baseline, NLR was inversely associated with long-term verbal memory in the iRBD group, but not in the control group. In the iRBD group, higher NLR was also associated with worsening of visuospatial abilities, processing speed, and semantic fluency over time. CONCLUSION:These findings suggest that peripheral inflammation may be involved in early mechanisms of cognitive vulnerability in iRBD. Further studies are needed to clarify whether this represents an epiphenomenon of the underlying pathology or plays an active role in disease progression.

Background:Different profiles of cognitive functioning have been demonstrated in ET subjects, also in patients with normal cognition. However, the prognostic significance of these profiles remains still debated. In this study, we aimed to explore different cognitive patterns among cognitively normal ET subjects and their relationship with the cognitive profiles of healthy subjects. Methods:We enrolled 50 cognitively normal subjects (26 ET and 24 age-, sex-, and education-matched healthy subjects), which scored within normal ranges individually in all tests of a comprehensive neuropsychological battery covering memory, executive function, attention, visuospatial abilities, and language. Unsupervised clustering was applied separately within each group. Cluster membership was validated by post-hoc comparisons using ANOVA and Bonferroni-corrected pairwise tests to compare the variables among the clusters. Results:All HC clustered together into a single high-functioning cognitive profile. On the contrary, we found two different clusters within ET, C1 (n = 14), showing high performance across all domains, and C2 (n = 12) which exhibited significantly poorer performances in the RAVLT-IR (p < 0.0001), RAVLT-DR (p = 0.0002), and Digit Span Forward (p = 0.015) than both ET-C1 and HC subjects. Other domains showed no significant differences across ET clusters. Discussion:This study demonstrates a cognitive heterogeneity in ET and reveals a memory-impaired subgroup absent among HC. The ET cluster with lower memory performance likely reflects a pattern of vulnerability for longitudinal decline or progression to mild cognitive impairment. The identification of this profile has relevant translational implications for prognosis and identification of early intervention strategies. Highlights:A data-driven clustering approach was applied to cognitive variables in HC subjects and ET patients. HC formed a homogeneous cluster. ET were divided into two cognitive subgroups: one cluster with high performance, and one memory-impaired cluster, significantly diverging from both the intact ET subgroup and HC. This may represent a cognitively vulnerable ET subgroup, with strong implications for targeted screening, early neuroprotective interventions and personalized clinical management.

Background:REM sleep behavior disorder (RBD) is a rare REM-parasomnia, now considered a non-motor symptom of Essential Tremor (ET). Distinct structural alterations in the thalamus, as a key region modulating REM sleep, have been reported in patients with idiopathic and Parkinson's disease-related forms. In this work, we investigated thalamic regions in ET patients with and without RBD, using a graph theoretical analysis. Methods:MRI data were acquired from 96 participants (41 ET, 10 ET with polysomnographic-confirmed RBD, ET-RBD, 45 controls). T1-weighted scans were obtained, and grey matter volumes were estimated across 28 thalamic regions of the AAL3 template (Cat12 toolbox). An adjacency matrix for each group was calculated using Pearson correlation. Group-specific matrices were extracted and nodal measures such as centrality measures and clustering coefficient were calculated. Differences between ET groups were computed using a set of 10000 random networks. Results:Interestingly, among analyzed thalamic regions, ET-RBD patients showed increased local strength and weighted clustering coefficient in Geniculate Body and increased Betweenness centrality in Right Pulvinar Inferior Nucleus (p = 0.05 FDR-corrected). Moreover, ET-RBD patients showed an increased strength and weighted clustering coefficient in Left Lateral Geniculate Body and Right Medial Geniculate Body, compared to controls (p = 0.05 FDR-corrected). Discussion:Our study demonstrates, for the first time, that the presence of RBD in ET is associated with an altered structural connectivity in thalamic regions. Our findings support the pathophysiologic role of the thalamus in the complex circuit causing RBD, in this particular ET phenotype. Highlights:ET-RBD phenotype is associated with thalamic volume loss and altered structural connectivity, particularly in the medial and lateral geniculate and pulvinar nuclei.Our findings support the pathophysiologic role of the thalamus in the complex RBD pathophysiology in this particular ET phenotype.

IntroductionDifferential diagnosis of rest tremor (RT) disorders is challenging, often requiring 123I-ioflupane single-photon-emission-computed tomography (DaTscan), an expensive technique not available worldwide. In the current study, we investigated the performance of a new wearable mobile device termed “RT-ring” in predicting DaTscan result in patients presenting with RT based on rest tremor inertial features.MethodsConsecutive RT patients underwent RT-ring tremor analysis, surface electromyography (sEMG), and DaTscan. The RT-ring is a miniaturized mobile device that uses machine learning based on inertial tremor data to estimate the RT pattern. This electrophysiologic tremor feature has proven to accurately predict DaTscan result. The primary outcome was the RT-ring’s performance in distinguishing patients with and without striatal dopaminergic deficit.ResultsSixty-seven RT patients were enrolled, including 42 patients with striatal dopaminergic deficit and 25 with normal DaTscan. The RT-ring showed 85.0% sensitivity, 90.9% specificity, and 87.9% balanced accuracy in predicting DaTscan result, and demonstrated 96.8% agreement with sEMG in RT pattern classification.ConclusionThe RT-ring is a promising, non-invasive, user-friendly, wearable mobile device for supporting the diagnosis of tremulous Parkinson’s disease in primary care settings, especially in low-income countries with limited access to dopamine imaging.

Essential Tremor (ET) is characterized by action tremor often associated with resting tremor (rET). Although previous studies have identified widespread brain white matter (WM) alterations in ET patients, differences between ET and rET have been less explored. In this study we employed differential tractography to investigate WM microstructural alterations in these tremor disorders.We conducted a Diffusion Tensor Imaging (DTI) study on age- and sex-matched cohorts: 25 healthy controls (HC), 30 ET, and 30 rET patients. Differential tractography using DSI Studio was employed to pairwise compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) among cohorts.ET and rET patients compared to HC exhibited similar widespread MD increase especially in basal ganglia and brainstem projections. WM changes were more pronounced in the left cerebral hemisphere and cerebellum (crus I and II) in ET, while in rET patients WM alterations were prevalent in right cerebral hemisphere and cerebellum crus I. Small FA decrease was found in rET but not in ET patients. ET patients showed changes in the left non-decussating dentato-rubro-thalamic tract (ndDRTT), whereas rET patients showed changes in both left ndDRTT and right decussating DRTT. In conclusion, our findings confirmed the DRTT involvement in essential tremor and demonstrated that ET and rET exhibited similar microstructural WM changes in the brain, with different hemispheric involvement—greater on the left side in ET and on the right side in rET—suggesting that these tremor disorders may be distinct subtypes of the same disease.

[This corrects the article DOI: 10.3389/fneur.2025.1534205.].

We aim to understand whether tremor may be an intrinsic feature of juvenile myoclonic epilepsy (JME) and whether individuals with JME plus tremor experience a different disease course. Thirty-one individuals with JME plus tremor (17 females, mean age = 33.9 ± 13.8 years) and 30 age of onset- and gender-matched subjects with JME (21 females, mean age = 26.8 ± 11.2 years) prospectively underwent clinical and neurophysiologic assessment, including tremor assessment and somatosensory evoked potentials (SEPs). All JME plus tremor subjects experienced postural and action tremor affecting bilateral upper limbs. Nine of 31 individuals (29%) with tremor were never exposed to valproate (VPA), and 14 of 31 (45.2%) were not using VPA at the time of clinical evaluation. Twelve of 31 (38.7%) patients with JME plus tremor were drug-resistant compared to four of 30 (13.3%) with JME (p = .024). The JME plus tremor subjects had higher numbers of previous childhood absence epilepsy (n = 6/31 [19.4%]), interictal epileptiform discharges (n = 30/31 [96.8%]), photosensitivity (n = 8/31 [25.8%]), and psychiatric comorbidities (n = 12/31 [38.7%]). Six of 31 (19.4%) individuals with JME plus tremor had giant SEPs (1/30, 3.3% with JME; p = .05, chi-squared test). The clinical features and decreased sensorimotor inhibition in the JME plus tremor group suggest that tremor might be a marker of disease severity rather than an epiphenomenon of VPA exposure.

Background:Essential tremor (ET) is a common movement disorder characterized by postural and kinetic tremor. Some patients also show resting tremor, being classified as the ET-plus distinct subtype. The corpus callosum (CC) involvement is proven in several neurological diseases, including ET, but differences between ET with and without resting tremor have not been studied. In this study, we investigated structural characteristics of the CC in a cohort of ET and ET with resting tremor (ETrt) patients, compared to healthy controls (HC). Methods:We enrolled 128 participants (63 ET, 38 ETrt, and 27 HC). We performed a multimodal MRI evaluation (thickness, mean diffusivity [MD], and fractional anisotropy [FA]) of the CC's genu, body, and splenium, using different statistical approaches. We first performed a traditional group-based comparison, controlling for relevant covariates. Then, we used an unsupervised classification model based on MRI data to explore potential subgroup distinctions. Results:Our evaluation showed significant changes in structural parameters of CC in both ET and ETrt patients compared to HC, mainly represented by thickness reductions across all regions and MD increase in the body. Notably, we found no differences between the ET and ETrt groups. Clustering analysis reinforced this observation, placing ET and ETrt in a single cluster with similar abnormalities in all MRI parameters and clearly separating them from HC. Discussion:Despite their clinical differences, ET with and without resting tremor patients showed analogous macro- and microstructural changes in the CC, suggesting shared pathophysiological processes within this brain region. Highlights:We explored structural integrity of the Corpus Callosum in ET patients with and without resting tremor. We found a thinning of the corpus callosum and microstructural abnormalities overlapping in ET and ETrt groups, suggesting that despite their different clinical presentations, they share some underlying mechanisms.

IntroductionDistinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data.MethodsWe enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients.ResultsBoth structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase.ConclusionMachine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

ObjectiveTo investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET.MethodsWe enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100–300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients.ResultsML models based exclusively on MRI features reached acceptable performance (AUC: 0.85–0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81–0.82 in rET vs. ET and AUC: 0.88–0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms.ConclusionThis study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.

This study aimed to develop a practical and objective measure of postural instability in movement disorder patients using kinematic measurements of the pull test. This study focused on patients with Parkinson's disease (PD) without postural instability (PI) compared with PD patients with postural instability (classified as PIGD (Postural Instability Gait Disorder)), and control subjects. Inertial measurement units were used to capture kinematic data during pull tests performed by a trained clinician. The kinematic data obtained from each pull test were analyzed and aggregated. The center of mass profile was found to effectively differentiate between patient groups. The patients with postural instability exhibited an increase in center of mass and number of steps and a decrease in step length. The groups could be differentiated based on the relationship between step length, center of mass and number of steps. This suggests that a quantitative pull test can provide kinematic metrics that are useful for measuring PI in patients with PD. In summary, this study demonstrated the potential of using kinematic measurements from a purposefully varied pull test as quantitative biomarkers for diagnosing, monitoring, and assessing the therapeutic outcomes of postural instability in patients with movement disorders.

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.

There is some debate on the relationship between essential tremor with rest tremor (rET) and the classic ET syndrome, and only few MRI studies compared ET and rET patients. This study aimed to explore structural cortical differences between ET and rET, to improve the knowledge of these tremor syndromes.Thirty-three ET patients, 30 rET patients and 45 control subjects (HC) were enrolled. Several MR morphometric variables (thickness, surface area, volume, roughness, mean curvature) of brain cortical regions were extracted using Freesurfer on T1-weighted images and compared among groups. The performance of a machine learning approach (XGBoost) using the extracted morphometric features was tested in discriminating between ET and rET patients.rET patients showed increased roughness and mean curvature in some fronto-temporal areas compared with HC and ET, and these metrics significantly correlated with cognitive scores. Cortical volume in the left pars opercularis was also lower in rET than in ET patients. No differences were found between ET and HC. XGBoost discriminated between rET and ET with mean AUC of 0.86 ± 0.11 in cross-validation analysis, using a model based on cortical volume. Cortical volume in the left pars opercularis was the most informative feature for classification between the two ET groups.Our study demonstrated higher cortical involvement in fronto-temporal areas in rET than in ET patients, which may be linked to the cognitive status. A machine learning approach based on MR volumetric data demonstrated that these two ET subtypes can be distinguished using structural cortical features.

Background Imaging studies investigating cerebellar gray matter (GM) in essential tremor (ET) showed conflicting results. Moreover, no large study explored the cerebellum in ET patients with resting tremor (rET), a syndrome showing enhanced blink reflex recovery cycle (BRrc). Objective To investigate cerebellar GM in ET and rET patients using voxel-based morphometry (VBM) analysis. Methods Seventy ET patients with or without resting tremor and 39 healthy controls were enrolled. All subjects underwent brain 3 T-MRI and BRrc recording. We compared the cerebellar GM volumes between ET (n = 40) and rET (n = 30) patients and controls through a VBM analysis. Moreover, we investigated possible correlations between cerebellar GM volume and R2 component of BRrc. Results rET and ET patients had similar disease duration. All rET patients and none of ET patients had enhanced BRrc. No differences in the cerebellar volume were found when ET and rET patients were compared to each other or with controls. By considering together the two tremor syndromes in a large patient group, the VBM analysis showed bilateral clusters of reduced GM volumes in Crus II in comparison with controls. The linear regression analysis in rET patients revealed a cluster in the left Crus II where the decrease in GM volume correlated with the R2BRrc increase. Conclusion Our study suggests that ET and rET are different tremor syndromes with similar mild cerebellar gray matter involvement. In rET patients, the left Crus II may play a role in modulating the brainstem excitability, encouraging further studies on the role of cerebellum in these patients.

Navigated transcranial magnetic stimulation (nTMS) is a painless method for targeting stimulation of the human brain. The responses from peripheral muscles provide a direct measure for the integrity of the cortical interneurons, corticospinal neurons, and spinal motoneurons. Parkinson's disease (PD) is characterized by the degeneration of dopaminergic nigrostriatal pathways and by the lateralization of motor dysfunction. In this study, we applied nTMS on a cohort of PD patients and healthy subjects (HC) in order to investigate the asymmetry of the cortical excitability. During the experiments, resting motor threshold (rMT) in each hemisphere and its difference between brain sides (ΔrMT), motor evoked potentials (MEPs) amplitude, and the electric field strength at the optimal stimulus location (E-field) were evaluated for each subject. A statistical analysis was performed and a significant difference between HC and PD was found in resting motor threshold asymmetry descripted by ΔrMT. This finding suggested that ΔrMT could be considered as an informative biomarker of PD disease. The innovative approach of navigated magnetic stimulation procedure allowed the respect of the cortical architecture through the accurate spatial location. Indeed, no significant differences were found in E-field strength in both hemispheres. The optimal spatial specificity of navigated TMS provides support for its application in the neurodegenerative disease scenario.

Rest tremor (RT) can be observed in several positions (seated, standing, lying down) but it is unknown whether the tremor features may vary across them. This study aimed to compare the RT electrophysiological features across different positions in tremor-dominant Parkinson’s disease (PD) and essential tremor plus (ET with RT, rET). We consecutively enrolled 90 tremor-dominant PD and 24 rET patients. The RT presence was evaluated in three positions: with the patient seated, the arm flexed at 90°, the forearm supported against gravity, and the hand hanging down from the chair armrest (hand-hanging position), in lying down supine and in standing position. RT electrophysiological features (amplitude, frequency, burst duration, pattern) were compared between the two patient groups and across the different positions. All PD and rET patients showed RT in hand-hanging position. Supine and standing RT were significantly more common in PD (67.8% and 75.6%, respectively) than in rET patients (37.5% and 45.8%, respectively). RT amplitude, frequency and pattern were significantly different between groups in hand-hanging position whereas only pattern was significantly different between PD and rET in both standing and supine positions. In each patient group, all RT electrophysiological features did not significantly vary across different recording positions (p > 0.05). In our study, PD and rET showed RT in hand-hanging, supine, and standing positions. RT pattern was the only electrophysiological feature significantly different between PD and rET patients in all these positions, enabling clinicians to perform the RT analysis for diagnostic purposes in different tremor positions.