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.

Clinical differentiation of progressive supranuclear palsy (PSP) from Parkinson’s disease (PD) is challenging due to overlapping phenotypes and late onset of PSP specific symptoms, highlighting the need for easily assessable biomarkers. We used proximity elongation assay (PEA) to analyze 460 proteins in serum samples from 46 PD, 30 PSP patients, and 24 healthy controls. ANCOVA was used to identify the most promising proteins and machine learning (ML) XGBoost and random forest algorithms to assess their classification performance. Promising proteins were also quantified by ELISA. Moreover, correlations between serum biomarkers and biological and clinical features were investigated. We identified five proteins (TFF3, CPB1, OPG, CNTN1, TIMP4) showing different levels between PSP and PD, which achieved good performance (AUC: 0.892) when combined by ML. On the other hand, when the three most significant biomarkers (TFF3, CPB1 and OPG) were analyzed by ELISA, there was no difference between groups. Serum levels of TFF3 positively correlated with age in all subjects’ groups, while for OPG and CPB1 such a correlation occurred in PSP patients only. Moreover, CPB1 positively correlated with disease severity in PD, while no correlations were observed in the PSP group. Overall, we identified CPB1 correlating with PD severity, which may support clinical staging of PD. In addition, our results showing discrepancy between PEA and ELISA technology suggest that caution should be used when translating proteomic findings into clinical practice.

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: 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.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms and widespread alterations in brain networks. Circulating biomarkers such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) reflect neuroaxonal damage and astroglial activation, respectively, but their relationship with large-scale brain connectivity remains poorly understood. Seventy-three PD patients and thirty-four healthy controls underwent diffusion magnetic resonance imaging. Whole-brain tractography was used to reconstruct structural connectivity networks, and graph-theoretical measures were derived. Serum NfL and GFAP levels were quantified, and their associations with network metrics and clinical variables were assessed. PD patients showed significant alterations in global and nodal network organization compared to controls. Higher NfL and GFAP levels were associated with reduced global clustering coefficient and efficiency, as well as increased path length and modularity. At the regional level, higher biomarker levels were associated with reduced network measures in the right thalamus and right cerebellar cortex. No significant associations were observed in healthy controls. These findings demonstrate that circulating biomarkers of neurodegeneration are linked to both global and regional disruptions of structural brain connectivity in PD, supporting the integration of blood-based biomarkers and connectomics to better characterize disease-related network alterations.

Background: Converging evidence supports a bidirectional link between epilepsy and Alzheimer's disease (AD). Limited data are available regarding the association of epilepsy with frontotemporal dementia (FTD) and dementia with Lewy bodies (DLB). Objective: To estimate the bidirectional association between epilepsy and AD, FTD, and DLB in the UK Biobank, a large, multi-source healthcare dataset. Methods: Epilepsy, AD, FTD, and DLB cohorts were extracted. Cox proportional-hazard models were used to compare the AD risk in the epilepsy cohort against matched controls, and the epilepsy risk in the AD cohort against matched controls. Prevalent association of epilepsy with FTD and DLB was assessed using Chi-squared/Fisher test. Results: Hazard-ratio (HR) for AD in the epilepsy population versus controls was 2.5 (1.8–3.4, p < 0.001); HR increased when epilepsy was diagnosed in the sixth-seventh decade and in individuals with epilepsy carrying the APOE ε4 allele. Conversely, HR for epilepsy in AD versus controls was 14.8 (9.7–22.5, p < 0.001). Epilepsy prevalence was higher in the FTD population compared to controls, with prevalence ratio (PR) of 5.3 (2.4–11.8, p = 0.001). Epilepsy PR in DLB versus controls approached but did not achieve statistical significance (2.4, 1.0–5.7, p = 0.068). Conclusions: Our findings reinforce the notion of a bidirectional association between epilepsy and AD, providing proof-of-concept that epilepsy, especially late-onset, may be successfully integrated into AD risk frameworks. Furthermore, we found a high prevalence of epilepsy in the FTD population. Careful stratification of individuals with epilepsy could offer an opportunity to identify those at higher risk of future or covert AD and neurodegeneration.