Serum Oligomeric α-Synuclein and p-tau181 in Progressive Supranuclear Palsy and Parkinson's Disease.

Clinical differentiation of progressive supranuclear palsy (PSP) from Parkinson's disease (PD) is challenging due to overlapping phenotypes and the late onset of specific atypical signs. Therefore, easily assessable diagnostic biomarkers are highly needed. Since PD is a synucleopathy while PSP is a tauopathy, here, we investigated the clinical usefulness of serum oligomeric-α-synuclein (o-α-synuclein) and 181Thr-phosphorylated tau (p-tau181), which are considered as the most important pathological protein forms in distinguishing between these two parkinsonisms. We assessed serum o-α-synuclein and p-tau181 by ELISA and SIMOA, respectively, in 27 PSP patients, 43 PD patients, and 39 healthy controls (HC). Moreover, we evaluated the correlation between serum biomarkers and biological and clinical features of these subjects. We did not find any difference in serum concentrations of p-tau181 and o-α-synuclein nor in the o-α-synuclein/p-tau181 ratio between groups. However, we observed that serum p-tau181 positively correlated with age in HC and PD, while serum o-α-synuclein correlated positively with disease severity in PD and negatively with age in PSP. Finally, the o-α-synuclein/p-tau181 ratio showed a negative correlation with age in PD.

Sex Differences in Conversion Risk from Mild Cognitive Impairment to Alzheimer's Disease: An Explainable Machine Learning Study with Random Survival Forests and SHAP.

Alzheimer's disease (AD) exhibits sex-linked variations, with women having a higher prevalence, and little is known about the sexual dimorphism in progressing from Mild Cognitive Impairment (MCI) to AD. The main aim of our study was to shed light on the sex-specific conversion-to-AD risk factors using Random Survival Forests (RSF), a Machine Learning survival approach, and Shapley Additive Explanations (SHAP) on dementia biomarkers in stable (sMCI) and progressive (pMCI) patients. With this purpose, we built two separate models for male (M-RSF) and female (F-RSF) cohorts to assess whether global explanations differ between the sexes. Similarly, SHAP local explanations were obtained to investigate changes across sexes in feature contributions to individual risk predictions. The M-RSF achieved higher performance on the test set (0.87) than the F-RSF (0.79), and global explanations of male and female models had limited similarity (<71.1%). Common influential variables across the sexes included brain glucose metabolism and CSF biomarkers. Conversely, the M-RSF had a notable contribution from hippocampus, which had a lower impact on the F-RSF, while verbal memory and executive function were key contributors only in F-RSF. Our findings confirmed that females had a higher risk of progressing to dementia; moreover, we highlighted distinct sex-driven patterns of variable importance, uncovering different feature contribution risks across sexes that decrease/increase the conversion-to-AD risk.

Systematic assessment of square-wave jerks in progressive supranuclear palsy: a video-oculographic study.

BACKGROUND: The presence of frequent macro-square-wave jerks (SWJs) has been recently included in the diagnostic criteria for progressive supranuclear palsy (PSP). The aim of the current video-oculographic study was to systematically assess the presence and features of SWJs during a brief fixation task in PSP, in comparison with Parkinson's disease (PD) patients and healthy controls (HC). METHODS: Thirty-eight PSP patients, 55 PD patients and 40 HC were enrolled in the study. All patients underwent a video-oculographic (VOG) examination including a 5-s fixation task, and the number, duration and amplitude of SWJs were recorded. The diagnostic performance of several SWJs parameters were then compared in distinguishing PSP from PD patients and controls. RESULTS: PSP patients showed a higher number and amplitude of SWJs compared to PD patients and controls. At least two SWJs within the 5-s fixation task were observed in 81.6% of PSP patients, 52.7% of PD patients and 25% of HC. The SWJs amplitude was the parameter showing the highest performances in distinguishing PSP from PD (AUC: 0.78) and HC (AUC: 0.88), outperforming the SWJ number and duration. The SWJ amplitude was larger in PSP-Richardson's syndrome than in PSP-Parkinsonism patients, while no difference was found between PSP patients with different degrees of vertical ocular motor dysfunction. CONCLUSIONS: This video-oculographic study provides robust evidence of larger SWJs number and amplitude in PSP than in PD patients, with some potential for differential diagnosis, supporting the inclusion of this ocular sign in PSP criteria.

Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study.

Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.

Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.

Objective: To 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. Methods: We 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. Results: ML 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. Conclusion: This 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.

Measurement invariance across countries of the Test of Memory Strategies (TMS): A contribution to the cross-national validity study.

Previous literature showed a complex interpretation of recall tasks due to the complex relationship between Executive Functions (EF) and Long Term Memory (M). The Test of Memory Strategies (TMS) could be useful for assessing this issue, because it evaluates EF and M simultaneously. This study aims to explore the validity of the TMS structure, comparing the models proposed by Vaccaro et al. (2022) and evaluating the measurement invariance according to three countries (Italy, Spain, and Portugal) through Confirmatory Factor Analysis (CFA). Four hundred thirty-one healthy subjects (Age mean = 54.84, sd = 20.43; Education mean = 8.85, sd =4.05; M = 177, F = 259) were recruited in three countries (Italy, Spain, and Portugal). Measurement invariance across three country groups was evaluated through Structural Equation modeling. Also, convergent and divergent validity were examined through the correlation between TMS and classical neuropsychological tests. CFA outcomes suggested that the best model was the three-dimensional model, in which list 1 and list2 reflect EF, list 3 reflects a mixed factor of EF and M (EFM) and list4 and list5 reflect M. This result is in line with the theory that TMS decreases EF components progressively. TMS was metric invariant to the country, but scalar invariance was not tenable. Finally, the factor scores of TMS showed convergent validity with the classical neuropsychological tests. The overall results support cross-validation of TMS in the three countries considered.

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study.

INTRODUCTION: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases. METHODS: Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC. RESULTS: PSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85-0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04. CONCLUSION: Our findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.

Neuroimaging correlates of postural instability in Parkinson's disease.

BACKGROUND: Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD), but little is known on its pathophysiological basis. OBJECTIVE: In this study, we aimed to identify the brain structures associated with PI in PD patients, using different MRI approaches. METHODS: We consecutively enrolled 142 PD patients and 45 control subjects. PI was assessed using the MDS-UPDRS-III pull-test item (PT). A whole-brain regression analysis identified brain areas where grey matter (GM) volume correlated with the PT score in PD patients. Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) were also used to compare unsteady (PT ≥ 1) and steady (PT = 0) PD patients. Associations between GM volume in regions of interest (ROI) and several clinical features were then investigated using LASSO regression analysis. RESULTS: PI was present in 44.4% of PD patients. The whole-brain approach identified the bilateral inferior frontal gyrus (IFG) and superior temporal gyrus (STG) as the only regions associated with the presence of postural instability. VBM analysis showed reduced GM volume in fronto-temporal areas (superior, middle, medial and inferior frontal gyrus, and STG) in unsteady compared with steady PD patients, and the GM volume of these regions was selectively associated with the PT score and not with any other motor or non-motor symptom. CONCLUSIONS: This study demonstrates a significant atrophy of fronto-temporal regions in unsteady PD patients, suggesting that these brain areas may play a role in the pathophysiological mechanisms underlying postural instability in PD. This result paves the way for further studies on postural instability in Parkinsonism.

Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor.

Introduction: Distinguishing 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. Methods: We 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. Results: Both 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. Conclusion: Machine 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.

Corpus callosum damage in PSP and unsteady PD patients: A multimodal MRI study.

INTRODUCTION: Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD) patients, but the brain alterations underlying this sign are not fully understood yet. This study aimed to investigate the association between PI and callosal damage in PD and progressive supranuclear palsy (PSP) patients, using multimodal MR imaging. METHODS: One-hundred and two PD patients stratified according to the presence/absence of PI (PD-steady N=58; PD-unsteady N=44), 69 PSP patients, and 38 healthy controls (HC) underwent structural and diffusion 3T brain MRI. Thickness, fractional anisotropy (FA) and mean diffusivity (MD) were calculated over 50 equidistant points covering the whole midsagittal profile of the corpus callosum (CC) and compared among groups. Associations between imaging metrics and postural instability score were investigated using linear regression. RESULTS: Both PSP and PD-unsteady patient groups showed CC involvement in comparison with HC, while no difference was found between PD-steady patients and controls. The CC damage was more severe and widespread in PSP than in PD patients. The CC genu was the regions most damaged in PD-unsteady patients compared with PD-steady patients, showing significant microstructural alterations of MD and FA metrics. Linear regression analysis pointed at the MD in the CC genu as the main contributor to PI among the considered MRI metrics. CONCLUSION: This study identified callosal microstructural alterations associated with PI in unsteady PD and PSP patients, which provide new insights on PI pathophysiology and might serve as imaging biomarkers for assessing postural instability progression and treatment response.