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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 onMRI 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, andmean curvature), reaching AUC: 0.97 ± 0.03 and outperformingmodels 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. [ABSTRACT FROM AUTHOR]

Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Differentiating Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data. Methods: Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes. Results: MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients. Conclusion: The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background and purpose: Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characterized by a high risk of phenoconversion to synucleinopathies. This study aimed to develop a model using ML algorithms to identify iRBD patients and test its accuracy. Methods: Data were acquired from 32 participants (20 iRBD patients and 12 controls). All subjects underwent a video-polysomnography. In all subjects, we measured the components of heart rate variability (HRV) during 24 h recordings and calculated night-to-day ratios (cardiac autonomic indices). Discriminating performances of single HRV features were assessed. ML models based on Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were trained on HRV data. The utility of HRV features and ML models for detecting iRBD was evaluated by area under the ROC curve (AUC), sensitivity, specificity and accuracy corresponding to optimal models. Results: Cardiac autonomic indices had low performances (accuracy 63–69%) in distinguishing iRBD from control subjects. By contrast, the RF model performed the best, with excellent accuracy (94%), sensitivity (95%) and specificity (92%), while XGBoost showed accuracy (91%), specificity (83%) and sensitivity (95%). The mean triangular index during wake (TIw) was the best discriminating feature between iRBD and HC, with 81% accuracy, reaching 84% accuracy when combined with VLF power during sleep using an LR model. Conclusions: Our findings demonstrated that ML algorithms can accurately identify iRBD patients. Our model could be used in clinical practice to facilitate the early detection of this form of RBD. [ABSTRACT FROM AUTHOR]

The clinical differential diagnosis between Parkinson's disease (PD) and progressive supranuclear palsy (PSP) is often challenging. The description of milder PSP phenotypes strongly resembling PD, such as PSP-Parkinsonism, further increased the diagnostic challenge and the need for reliable neuroimaging biomarkers to enhance the diagnostic certainty. This review aims to summarize the contribution of a relatively simple and widely available imaging technique such as MR planimetry in the differential diagnosis between PD and PSP, focusing on the recent advancements in this field. The development of accurate MR planimetric biomarkers, together with the implementation of automated algorithms, led to robust and objective measures for the differential diagnosis of PSP and PD at the individual level. Evidence from longitudinal studies also suggests a role of MR planimetry in predicting the development of the PSP clinical signs, allowing to identify PSP patients before they meet diagnostic criteria when their clinical phenotype can be indistinguishable from PD. Finally, promising evidence exists on the possible association between MR planimetric measures and the underlying pathology, with important implications for trials with new disease-modifying target therapies. [ABSTRACT FROM AUTHOR]

Background: Differentiating progressive supranuclear palsy‐parkinsonism (PSP‐P) from Parkinson's disease (PD) is clinically challenging. Objective: This study aimed to develop an automated Magnetic Resonance Parkinsonism Index 2.0 (MRPI 2.0) algorithm to distinguish PSP‐P from PD and to validate its diagnostic performance in two large independent cohorts. Methods: We enrolled 676 participants: a training cohort (n = 346; 43 PSP‐P, 194 PD, and 109 control subjects) from our center and an independent testing cohort (n = 330; 62 PSP‐P, 171 PD, and 97 control subjects) from an international research group. We developed a new in‐house algorithm for MRPI 2.0 calculation and assessed its performance in distinguishing PSP‐P from PD and control subjects in both cohorts using receiver operating characteristic curves. Results: The automated MRPI 2.0 showed excellent performance in differentiating patients with PSP‐P from patients with PD and control subjects both in the training cohort (area under the receiver operating characteristic curve [AUC] = 0.93 [95% confidence interval, 0.89–0.98] and AUC = 0.97 [0.93–1.00], respectively) and in the international testing cohort (PSP‐P versus PD, AUC = 0.92 [0.87–0.97]; PSP‐P versus controls, AUC = 0.94 [0.90–0.98]), suggesting the generalizability of the results. The automated MRPI 2.0 also accurately distinguished between PSP‐P and PD in the early stage of the diseases (AUC = 0.91 [0.84–0.97]). A strong correlation (r = 0.91, P < 0.001) was found between automated and manual MRPI 2.0 values. Conclusions: Our study provides an automated, validated, and generalizable magnetic resonance biomarker to distinguish PSP‐P from PD. The use of the automated MRPI 2.0 algorithm rather than manual measurements could be important to standardize measures in patients with PSP‐P across centers, with a positive impact on multicenter studies and clinical trials involving patients from different geographic regions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Background: 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). Methods: 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. Results: 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). Discussion: 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. [ABSTRACT FROM AUTHOR]

Background: The R2 component of blink reflex recovery cycle (R2BRrc) is a simple neurophysiological tool to detect the brainstem hyperexcitability commonly occurring in several neurological diseases such as Parkinson's disease and atypical parkinsonisms. In our study, we investigated for the first time the usefulness of R2BRrc to assess brainstem excitability in patients with idiopathic Normal Pressure Hydrocephalus (iNPH) in comparison with healthy subjects. Methods: Eighteen iNPH patients and 25 age-matched control subjects were enrolled. R2BRrc was bilaterally evaluated at interstimulus intervals (ISIs) of 100, 150, 200, 300, 400, 500 and 750 ms in all participants. We investigated the diagnostic performance of R2BRrc in differentiating iNPH patients from control subjects using ROC analysis. Midbrain area and Magnetic Resonance Hydrocephalic Index (MRHI), an MRI biomarker for the diagnosis of iNPH, were measured on T1-weighted MR images, and correlations between R2BRrc values and MRI measurements were investigated. Results: Fourteen (78%) of 18 iNPH patients showed an enhanced R2BRrc at ISIs 100–150–200 ms, while no control subjects had abnormal R2BRrc. The mean amplitude of bilateral R2BRrc at the shortest ISIs (100–150–200 ms) showed high accuracy in differentiating iNPH patients from controls (AUC = 0.89). R2BRrc values significantly correlated with midbrain area and MRHI values. Conclusions: This study represents the first evidence of brainstem hyperexcitability in iNPH patients. Given its low cost and wide availability, R2BRrc could be a useful tool for selecting elderly subjects with mild gait and urinary dysfunction who should undergo an extensive diagnostic workup for the diagnosis of NPH. [ABSTRACT FROM AUTHOR]

Deep grey nuclei of the human brain accumulate minerals both in aging and in several neurodegenerative diseases. Mineral deposition produces a shortening of the transverse relaxation time which causes hypointensity on magnetic resonance (MR) imaging. The physician often has difficulties in determining whether the incidental hypointensity of grey nuclei seen on MR images is related to aging or neurodegenerative pathology. We investigated the hypointensity patterns in globus pallidus, putamen, caudate nucleus, thalamus and dentate nucleus of 217 healthy subjects (ages, 20-79 years; men/women, 104/113) using 3T MR imaging. Hypointensity was detected more frequently in globus pallidus (35.5%) than in dentate nucleus (32.7%) and putamen (7.8%). A consistent effect of aging on hypointensity (p < 0.001) of these grey nuclei was evident. Putaminal hypointensity appeared only in elderly subjects whereas we did not find hypointensity in the caudate nucleus and thalamus of any subject. In conclusion, the evidence of hypointensity in the caudate nucleus and thalamus at any age or hypointensity in the putamen seen in young subjects should prompt the clinician to consider a neurodegenerative disease. [ABSTRACT FROM AUTHOR]

Tremor is an impairing symptom associated with several neurological diseases. Some of such diseases are neurodegenerative, and tremor characterization may be of help in differential diagnosis. To date, electromyography (EMG) is the gold standard for the analysis and diagnosis of tremors. In the last decade, however, several studies have been conducted for the validation of different techniques and new, non-invasive, portable, or even wearable devices have been recently proposed as complementary tools to EMG for a better characterization of tremors. Such devices have proven to be useful for monitoring the efficacy of therapies or even aiding in differential diagnosis. The aim of this review is to present systematically such new solutions, trying to highlight their potentialities and limitations, with a hint to future developments. [ABSTRACT FROM AUTHOR]

Background: Enlargement of the third ventricle has been reported in atypical parkinsonism. We investigated whether the measurement of third ventricle width could distinguish Parkinson's disease (PD) from progressive supranuclear palsy (PSP). Methods: We assessed a new MR T1‐weighted measurement (third ventricle width/internal skull diameter) in a training cohort of 268 participants (98 PD, 73 PSP, 98 controls from our center) and in a testing cohort of 291 participants (82 de novo PD patients and 133 controls from the Parkinson's Progression Markers Initiative, 76 early‐stage PSP from an international research group). PD diagnosis was confirmed after a 4‐year follow‐up. Diagnostic performance of the third ventricle/internal skull diameter was assessed using receiver operating characteristic curve with bootstrapping; the area under the curve of the training cohort was compared with the area under the curve of the testing cohort using the De Long test. Results: In both cohorts, third ventricle/internal skull diameter values did not differ between PD and controls but were significantly lower in PD than in PSP patients (P < 0.0001). In PD, third ventricle/internal skull diameter values did not change significantly between baseline and follow‐up evaluation. Receiver operating characteristic analysis accurately differentiated PD from PSP in the training cohort (area under the curve, 0.94; 95% CI, 91.1–97.6; cutoff, 5.72) and in the testing cohort (area under the curve, 0.91; 95% CI, 87.0–97.0; cutoff,: 5.88), validating the generalizability of the results. Conclusion: Our study provides a new reliable and validated MRI measurement for the early differentiation of PD and PSP. The simplicity and generalizability of this biomarker make it suitable for routine clinical practice and for selection of patients in clinical trials worldwide. © 2020 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Background: Idiopathic normal pressure hydrocephalus and PSP share several clinical and radiological features, making differential diagnosis, at times, challenging.Objectives: To differentiate idiopathic normal pressure hydrocephalus from PSP using MR volumetric and linear measurements.Methods: Twenty-seven idiopathic normal pressure hydrocephalus patients, 103 probable PSP patients, and 43 control subjects were consecutively enrolled. Automated ventricular volumetry was performed using Freesurfer 6 on MR T1 -weighted images. Linear measurements, such as callosal angle and a new measure, termed MR Hydrocephalic Index, were calculated on MR T1 -weighted images. Receiver operating characteristic analyses were used for differentiating between patient groups. Generalizability and reproducibility of the results were validated, dividing each participant group in two cohorts used as training and testing subsets.Results: Ventricular volumes and linear measurements (callosal angle and Magnetic Resonance Hydrocephalic Index) revealed greater ventricular enlargement in patients with idiopathic normal pressure hydrocephalus than in PSP patients and controls. PSP patients had ventricular volume larger than controls. Automated ventricular volumetry and Magnetic Resonance Hydrocephalic Index were the most accurate measures (98.5%) in differentiating patients with idiopathic normal pressure hydrocephalus from PSP patients, whereas callosal angle misclassified several PSP patients and showed low positive predictive value (70.0%) in differentiating between these two diseases. All measurements accurately differentiated idiopathic normal pressure hydrocephalus patients from controls. Accuracy values obtained in the training set (automated ventricular volumetry, 98.4%; Magnetic Resonance Hydrocephalic Index, 98.4%; callosal angle, 87.5%) were confirmed in the testing set.Conclusions: Our study demonstrates that AVV and Magnetic Resonance Hydrocephalic Index were the most accurate measures for differentiation between idiopathic normal pressure hydrocephalus and PSP patients. Magnetic Resonance Hydrocephalic Index is easy to measure and can be used in clinical practice to prevent misdiagnosis and ineffective shunt procedures in idiopathic normal pressure hydrocephalus mimics. © 2020 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Cardiac autonomic indexes, including cardiac parasympathetic index and cardiac sympathetic index, have been reported to accurately identify patients with sleep disorders such as obstructive sleep apnea. Our study aimed to assess cardiac autonomic indexes in patients with obstructive sleep apnea before and during a single full‐night continuous positive airway pressure therapy using a combined approach. Our simultaneous heart rate variability‐polysomnographic study included 16 never‐treated obstructive sleep apnea patients. Two patients dropped out. Patients underwent combined recordings in two consecutive days, at baseline and during a single full‐night of acute continuous positive airway pressure treatment. We calculated cardiac parasympathetic index and cardiac sympathetic index as night/day ratio for high‐frequency and low‐frequency heart rate variability spectral components, respectively. Continuous positive airway pressure treatment significantly reduced cardiac autonomic indexes values in comparison with baseline values (cardiac parasympathetic index: p <.0001; cardiac sympathetic index: p =.001). After acute continuous positive airway pressure treatment, the percentage of decrease of cardiac parasympathetic index was greater than that of cardiac sympathetic index (51.02 ± 15.72 versus 34.64 ± 26.93). A positive statistical correlation was also found between decrease of cardiac parasympathetic index and decrease of apnea–hypopnea index after continuous positive airway pressure (p <.001). This study improves the knowledge on cardiac autonomic modulation during acute continuous positive airway pressure therapy in obstructive sleep apnea. Our results demonstrate that both autonomic indexes decreased significantly after a single‐night of acute continuous positive airway pressure therapy. Cardiac parasympathetic index more than cardiac sympathetic index was related to decrease of apnea–hypopnea index after continuous positive airway pressure therapy, thus representing a potential help in everyday clinical practice. [ABSTRACT FROM AUTHOR]

Objective: REM sleep behavior disorder (RBD) is an important risk factor for the dementia development and for the deterioration of autonomic functions in patients with Parkinson's Disease. RBD has also been reported in patients with Essential Tremor (ET). However, its clinical significance in ET remains still unknown. We aimed to investigate clinical, neuropsychological and cardiac autonomic scintigraphic differences between ET patients with and without RBD. Methods: To assess RBD symptoms, RBD Single-Question has been administered in a cohort of 55 patients with a clinical diagnosis of ET. Patients with clinical RBD underwent polysomnography (PSG) confirmation. All patients completed a battery of neuropsychological assessment of memory, executive function, attention, language, and visuospatial function. Cardiac MIBG scintigraphy was performed in order to measure the cardiac autonomic innervation. Results: Ten ET patients (18%) had a PSG-confirmed RBD (ETRBD+). Compared to ET patients without RBD (ETRBD−), significantly reduced scores on memory domain tests such as Rey auditory verbal learning test immediate recall (p = 0.015) and Rey auditory verbal learning test delayed recall (p = 0.004) and phonemic fluency test (p = 0.028) were present in ETRBD+. By contrast, no other significant clinical difference has emerged from the comparison between two ET groups. Similarly, ETRBD+ patients have cardiac MIBG tracer uptake in the normal value range as occurred in those with ETRBD−. Conclusions: This study improves the knowledge on clinical significance of RBD symptoms in ET patients. Our preliminary findings demonstrate that presence of RBD in ET is associated with neurocognitive impairment, but not with cardiac autonomic dysfunction. Further longitudinal studies are needed to investigate whether ET patients with RBD will develop a frank dementia over the time. [ABSTRACT FROM AUTHOR]

Background: No prospective study of patients with Parkinson's disease (PD) has investigated the appearance of vertical gaze abnormalities, a feature suggestive of progressive supranuclear palsy (PSP).Objective: To identify, within a cohort of patients with an initial diagnosis of PD, those who developed vertical gaze abnormalities during a 4-year follow-up, and to investigate the performance of new imaging biomarkers in predicting vertical gaze abnormalities.Methods: A total of 110 patients initially classified as PD and 74 controls were enrolled. All patients underwent clinical assessment at baseline and every year up to the end of the follow-up. The pons/midbrain area ratio 2.0 and the Magnetic Resonance Parkinsonism Index 2.0 were calculated.Results: After 4-year follow-up, 100 of 110 patients maintained the diagnosis of PD, whereas 10 PD patients (9.1%) developed vertical gaze abnormalities, suggesting an alternative diagnosis of PSP-parkinsonism. At baseline, the Magnetic Resonance Parkinsonism Index 2.0 was the most accurate biomarker in differentiating PD patients who developed vertical gaze abnormalities from those who maintained an initial diagnosis of PD. At the end of follow-up, both of these biomarkers accurately distinguished PSP-parkinsonism from PD.Conclusions: Our results demonstrate that a number of patients with an initial diagnosis of PD developed vertical gaze abnormalities during a 4-year follow-up, and the diagnosis was changed from PD to PSP-parkinsonism. In PD patients, baseline Magnetic Resonance Parkinsonism Index 2.0 showed the best performance in predicting the clinical evolution toward a PSP-parkinsonism phenotype, enabling PSP-parkinsonism patients to be identified at the earliest stage of the disease for promising disease-modifying therapies. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Objective: To evaluate circadian fluctuations and night/day ratio of Heart Rate Variability (HRV) spectral components in patients with obstructive sleep apnea (OSA) in comparison with controls. Participants and methods: This is a simultaneous HRV-polysomnographic (PSG) study including 29 patients with OSA and 18 age-sex-matched controls. Four patients with OSA dropped out. All participants underwent PSG and HRV analysis. We measured the 24-hour fluctuations and the night/day ratio of low frequency (LF) and high frequency (HF) spectral components of HRV in all subjects and controls. The LF night/day ratio was termed the cardiac sympathetic index while the HF night/day ratio was termed the cardiac parasympathetic index. Results: All twenty-five OSA patients were PSG positive (presence of OSA) while 18 controls were PSG negative (absence of OSA). There was no significant difference in LF and HF 24-hour fluctuation values between OSA patients and controls. In OSA patients, LF and HF values were significantly higher during night-time than day time recordings (p<0.001). HF night/day ratio (cardiac parasympathetic index) accurately (100%) differentiated OSA patients from controls without an overlap of individual values. The LF night/day ratio (cardiac sympathetic index) had sensitivity of 84%, specificity of 72.2% and accuracy of 79.1% in distinguishing between groups. Conclusions: The cardiac parasympathetic index accurately differentiated patients with OSA from controls, on an individual basis. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is commonly used to assess autonomic functions and responses to environmental stimuli. It is usually derived from electrocardiographic signals; however, in the last few years, photoplethysmography has been successfully used to evaluate beat-to-beat time intervals and to assess changes in the human heart rate under several conditions. The present work describes a simple design of a photoplethysmograph, using a wearable earlobe sensor. Beat-to-beat time intervals were evaluated as the time between subsequent pulses, thus generating a signal representative of heart rate variability, which was compared to RR intervals from classic electrocardiography. Twenty-minute pulse photoplethysmography and ECG recordings were taken simultaneously from 10 healthy individuals. Ten additional subjects were recorded for 24 h. Comparisons were made of raw signals and on time-domain and frequency-domain HRV parameters. There were small differences between the inter-beat intervals evaluated with the two techniques. The current findings suggest that our wearable earlobe pulse photoplethysmograph may be suitable for short and long-term home measuring and monitoring of HRV parameters. [ABSTRACT FROM AUTHOR]

In this work, we investigated motor network structure in patients affected by essential tremor (ET) with or without resting tremor, using probabilistic tractography of the cerebello-thalamo-basal ganglia-cortical loop. Twenty-five patients with ET, twenty-two patients with ET associated with resting tremor (rET), and twenty-five age- and sex-matched healthy controls were included in the study. All participants underwent whole-brain 3D T1-weighted and diffusion-weighted MRI, and DAT-SPECT. Probabilistic tractography was performed on diffusion data in network mode, reconstructing connections between the different structures of the cerebello-thalamo-basal ganglia-cortical loop. All patients with ET, regardless of the presence of resting tremor, had normal DAT-SPECT, but showed significantly decreased connectivity in the cerebello-thalamo-precentral cortex network bilaterally, compared to healthy controls. In addition, patients with rET showed reduced connectivity in a pathway connecting globus pallidus, caudate, and supplementary motor area, compared to ET and controls. This latter circuit was significantly damaged in the hemisphere contralateral to the side clinically most affected by resting tremor. These findings provide insights upon structural changes underlying the different clinical presentations of ET. Our study demonstrates that ET and rET share common alterations in the cerebello-thalamo-precentral cortex circuit, while rET patients are characterized by specific damage to additional structures of motor network, such as globus pallidus, caudate nucleus, and supplementary motor area. Our findings suggest that ET and rET are different subtypes of the same neurodegenerative disorder. [ABSTRACT FROM AUTHOR]

ABSTRACT Imaging measurements, such as the ratio of the midsagittal areas of the midbrain and pons (midbrain/pons) and the Magnetic Resonance Parkinsonism Index (MRPI), have been proposed to differentiate progressive supranuclear palsy (PSP) from Parkinson's disease (PD). However, abnormal midbrain/pons values suggestive of PSP have also been reported in elderly individuals and in patients with PD. We investigated the effect of aging on single or combined imaging measurements of the brainstem. We calculated the midbrain/pons and the MRPI (the ratio of the midsagittal areas of the pons and the midbrain multiplied by the ratio of the middle cerebellar peduncle and superior cerebellar peduncle widths) in 152 patients affected by PD, 25 patients with PSP, and a group of 81 age-matched and sex-matched healthy controls using a 3-Tesla magnetic resonance imaging scanner. In healthy controls, aging was negatively correlated with midsagittal area of the midbrain and midbrain/pons values. In patients with PD, in addition to the effect of aging, the disease status further influenced the midbrain/pons values (R2 = 0.23; P < 0.001). In both groups, MRPI values were not influenced either by aging or by disease status. No effect of aging on either midbrain/pons or MRPI values was shown in the patients with PSP. Our findings indicated that the MRPI was not significantly influenced by aging or disease-related changes occurring in PD; whereas, in contrast, the midbrain/pons was influenced. Therefore, the MRPI appears to be a more reliable imaging measurement compared with midbrain/pons values for differentiating PSP from PD and controls in an elderly population. © 2014 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Involuntary tremor at rest is observed in patients with Parkinson's disease (PD) or essential tremor (ET). Electromyography (EMG) studies have shown that phase displacement between antagonistic muscles at prevalent tremor frequency can accurately differentiate resting tremor in PD from that detected in ET. Currently, phase evaluation is qualitative in most cases. The aim of this study is to develop and validate a new mobile tool for the automated and quantitative characterization of phase displacement (resting tremor pattern) in ambulatory clinical settings. A new low-cost, wearable mobile device, called µEMG, is described, based on low-end instrumentation amplifiers and simple digital signal processing (DSP) capabilities. Measurements of resting tremor characteristics from this new device were compared with standard EMG. A good level of agreement was found in a sample of 21 subjects (14 PD patients with alternating resting tremor pattern and 7 ET patients with synchronous resting tremor pattern). Our results demonstrate that tremor analysis using µEMG is easy to perform and it can be used in routine clinical practice for the automated quantification of resting tremor patterns. Moreover, the measurement process is handy and operator-independent. [ABSTRACT FROM AUTHOR]