Development and Validation of Automated Magnetic Resonance Parkinsonism Index 2.0 to Distinguish Progressive Supranuclear Palsy-Parkinsonism From Parkinson's Disease.

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.

Evaluation of rest tremor in different positions in Parkinson's disease and essential tremor plus.

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.

Incidental evidence of hypointensity in brain grey nuclei on routine MR imaging: when to suspect a neurodegenerative disorder?

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.

A New MRI Measure to Early Differentiate Progressive Supranuclear Palsy From De Novo Parkinson's Disease in Clinical Practice: An International Study.

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.

Magnetic Resonance Imaging Biomarkers Distinguish Normal Pressure Hydrocephalus From Progressive Supranuclear Palsy.

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.

Usefulness of cardiac parasympathetic index in CPAP-treated patients with obstructive sleep apnea: A preliminary study.

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.

Refining initial diagnosis of Parkinson's disease after follow-up: A 4-year prospective clinical and magnetic resonance imaging study.

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.

The embodiment of language in tremor-dominant Parkinson's disease patients.

According to embodied cognition, processing language with motor content involves a simulation of this content by the brain motor system. Patients with brain lesions involving the motor system are characterized by deficits in action verbs processing in the absence of dementia. We sought to assess whether action verbs interfere with the motor behavior of patients with Parkinson's disease (PD) having tremor dominant symptoms. PD tremor is considered to result from dysfunction of cortical-subcortical motor circuits driven by dopamine depletion. In addition, PD tremor is reduced during active movement execution. Therefore, likewise movement execution, the motor simulation of bodily actions predicted by the embodiment may show to be effective in modifying tremor by interfering with a dysfunctional motor system. Here, we asked to simply read and repeat words expressing a hand-related bodily action. Abstract verbs served as control. Changes in tremor kinematics were evaluated using a monoaxial accelerometer. Seventeen PD patients with rest tremor of the upper limbs were enrolled. Tremor amplitude was significantly smaller when reading action verbs as compared to abstract verbs. We provide empirical evidence supporting the embodied cognition theory by showing that circuits mediating tremor of PD patients are distinctively affected by processing action language.

Comparison between Electrocardiographic and Earlobe Pulse Photoplethysmographic Detection for Evaluating Heart Rate Variability in Healthy Subjects in Short- and Long-Term Recordings.

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.

Effect of aging on magnetic resonance measures differentiating progressive supranuclear palsy from Parkinson's disease.

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 (R(2) = 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.

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.

Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging.

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.

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.

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.

Neuroimaging correlates of postural instability in Progressive Supranuclear Palsy.

OBJECTIVE: We aimed to identify the brain structures associated with postural instability (PI) in Progressive Supranuclear Palsy (PSP). METHODS: Forty-seven PSP patients and 45 control subjects were enrolled in this study. PI was assessed using the items 27 and 28 of the PSP rating scale (postural instability score, PIS). PSP patients were compared with controls using voxel-based morphometry (VBM). In PSP patients, LASSO regression model was used to investigate associations between VBM-based Region-Of-Interest grey matter (GM) volumes and different categories of the PSP rating scale. A whole-brain multi-regression analysis was also used to identify brain areas where GM volumes correlated with the PIS in PSP patients. RESULTS: VBM analysis showed widespread GM atrophy (fronto-temporal-parietal-occipital regions, limbic lobes, insula, cerebellum, and basal ganglia) in PSP patients compared with control subjects. In PSP patients, LASSO regression analysis showed associations of the right cerebellar lobules IV-V with ocular motor category score, and the left Rolandic area with bulbar category score, while the right inferior frontal gyrus (IFG) was negatively correlated with the PIS. The whole-brain multi-regression analysis identified the right IFG as the only area significantly associated with the PIS. CONCLUSIONS: In our study, two different approaches demonstrated that the IFG volume was associated with PIS in PSP patients, suggesting that this area may play a role in the pathophysiological mechanisms underlying PI. Our findings may have important implications for developing optimal Transcranial Magnetic Stimulation protocols targeting IFG in parkinsonism with postural disorders.

Development of a New Wearable Device for the Characterization of Hand Tremor.

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.

Differentiating between common PSP phenotypes using structural MRI: a machine learning study.

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.

A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder.

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.

Video-oculographic biomarkers for evaluating vertical ocular dysfunction in progressive supranuclear palsy.

INTRODUCTION: Progressive supranuclear palsy (PSP) patients show reduced amplitude and velocity of vertical saccades, but saccadic abnormalities have also been reported in Parkinson's disease (PD). We investigated amplitude and velocity of vertical saccades in PSP and PD patients, to establish the best video-oculographic (VOG) parameters for PSP diagnosis. METHODS: Fifty-one PSP patients, 113 PD patients and 40 controls were enrolled. The diagnosis was performed on a clinico-radiological basis (MR Parkinsonism index [MRPI] and MRPI 2.0). We used VOG to assess the diagnostic performances of saccadic amplitude, peak velocity, and their product (AxV) in upward or downward direction and in vertical gaze (upward and downward averaged) in distinguishing PSP from PD patients. The vestibulo-ocular reflex, necessary to establish the supranuclear nature of ocular dysfunction, was evaluated clinically. RESULTS: PSP patients showed significantly reduced amplitude and peak velocity of ocular saccades in upward and downward directions compared to PD and healthy subjects. In PD patients, upward gaze amplitude was lower than in controls. In vertical gaze, the peak velocity showed 99.1% specificity and 54.7% sensitivity for PSP classification. The AxV product showed high specificity (94.7%) and sensitivity (84.3%) and yielded higher accuracy (91.5%) than velocity and amplitude used alone in distinguishing PSP from PD. CONCLUSION: Our study demonstrates that the peak velocity of vertical saccades was a very low sensitive parameter and cannot be used alone for PSP diagnosis. A new index combining amplitude and peak velocity in vertical gaze seems the most suitable video-oculographic biomarker for differentiating PSP from PD and controls.