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

Clinical Utility of Tectal Plate Measurements on Magnetic Resonance Imaging in Progressive Supranuclear Palsy

BACKGROUND

Midbrain atrophy is a characteristic feature of progressive supranuclear palsy (PSP), observed in PSP-Richardson's syndrome (PSP-RS) and to a lesser extent PSP-parkinsonism (PSP-P).

OBJECTIVE

Our aim was to critically evaluate the utility of manual magnetic resonance imaging measurements of the midbrain tectal plate as a diagnostic biomarker in PSP.

METHODS

Length of the tectal plate and width of the superior and inferior colliculi were measured in 40 PSP (20 PSP-RS and 20 PSP-P) patients and compared with 20 Parkinson's disease and 20 healthy control subjects.

RESULTS

Tectal plate length was reduced in both PSP groups compared with Parkinson's disease and control subjects and was most abnormal in PSP-RS followed by PSP-P. Reduced tectal plate length was associated with worse PSP Rating Scale scores.

CONCLUSIONS

Simple manual measurements of tectal plate length show utility as a diagnostic biomarker in PSP, particularly for PSP-RS.

Progressive supranuclear palsy: Neuropathology, clinical presentation, diagnostic challenges, management, and emerging therapies

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by the accumulation of 4R-tau protein aggregates in various brain regions. PSP leads to neuronal loss, gliosis, and tau-positive inclusions, such as neurofibrillary tangles, tufted astrocytes, and coiled bodies. These pathological changes mainly affect the brainstem and the basal ganglia, resulting in distinctive MRI features, such as the hummingbird and morning glory signs. PSP shows clinical heterogeneity and presents as different phenotypes, the most classical of which is Richardson's syndrome (PSP-RS). The region of involvement and the mode of atrophy spread can further distinguish subtypes of PSP. PSP patients can experience various signs and symptoms, such as postural instability, supranuclear ophthalmoplegia, low amplitude fast finger tapping, and irregular sleep patterns. The most common symptoms of PSP are postural instability, falls, vertical gaze palsy, bradykinesia, and cognitive impairment. These features often overlap with those of Parkinson's disease (PD) and other Parkinsonian syndromes, making the diagnosis challenging. PSP is an essential clinical topic to research because it is a devastating and incurable disease. However, there are still many gaps in knowledge about its pathophysiology, diagnosis, and treatment. Several clinical trials are underway to test noveltherapies that target tau in various ways, such as modulating its post-translational modifications, stabilizing its interaction with microtubules, or enhancing its clearance by immunotherapy. These approaches may offer new hope for slowing down the progression of PSP. In this review, we aim to provide an overview of the current knowledge on PSP, from its pathogenesis to its management. We also discuss the latest advances and future directions in PSP research.

Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum

Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features and to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51 with cortical and subcortical variant subtypes). Ninety-one were used as the training set and 20 as a test set. The presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia and supranuclear gaze palsy were noted. Domains of akinesia, ocular motor impairment, postural instability and cognitive dysfunction as defined by the Movement Disorders Society criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose-positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity, suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so sub-cortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI- and FDG-PET-based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic Impairment Scale was stronger than with the Movement Disorders Society Diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set; however, prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data-driven approaches to clinical neuroimaging analyses.

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.

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.

Potential Exosome Biomarkers for Parkinson's Disease Diagnosis: A Systematic Review and Meta-Analysis

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Given its prevalence, reliable biomarkers for early diagnosis are required. Exosomal proteins within extracellular nanovesicles are promising candidates for diagnostic, screening, prognostic, and disease monitoring purposes in neurological diseases such as PD. This review aims to evaluate the potential of extracellular vesicle proteins or miRNAs as biomarkers for PD. A comprehensive literature search until January 2024 was conducted across multiple databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, to identify relevant studies reporting exosome biomarkers in blood samples from PD patients. Out of 417 articles screened, 47 studies were selected for analysis. Among exosomal protein biomarkers, α-synuclein, tau, Amyloid β 1-42, and C-X-C motif chemokine ligand 12 (CXCL12) were identified as significant markers for PD. Concerning miRNA biomarkers, miRNA-24, miR-23b-3p, miR-195-3p, miR-29c, and mir-331-5p are promising across studies. α-synuclein exhibited increased levels in PD patients compared to control groups in twenty-one studies, while a decrease was observed in three studies. Our meta-analysis revealed a significant difference in total exosomal α-synuclein levels between PD patients and healthy controls (standardized mean difference [SMD] = 1.369, 95% confidence interval [CI] = 0.893 to 1.846, p < 0.001), although these results are limited by data availability. Furthermore, α-synuclein levels significantly differ between PD patients and healthy controls (SMD = 1.471, 95% CI = 0.941 to 2.002, p < 0.001). In conclusion, certain exosomal proteins and multiple miRNAs could serve as potential biomarkers for diagnosis, prognosis prediction, and assessment of disease progression in PD.

Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here?

Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.

Brain MRI in Progressive Supranuclear Palsy with Richardson's Syndrome and Variant Phenotypes

BACKGROUND

Brain magnetic resonance imaging (MRI) is used to support the diagnosis of progressive supranuclear palsy (PSP). However, the value of visual descriptive, manual planimetric, automatic volumetric MRI markers and fully automatic categorization is unclear, particularly regarding PSP predominance types other than Richardson's syndrome (RS).

OBJECTIVES

To compare different visual reading strategies and automatic classification of T1-weighted MRI for detection of PSP in a typical clinical cohort including PSP-RS and (non-RS) variant PSP (vPSP) patients.

METHODS

Forty-one patients (21 RS, 20 vPSP) and 46 healthy controls were included. Three readers using three strategies performed MRI analysis: exclusively visual reading using descriptive signs (hummingbird, morning-glory, Mickey-Mouse), visual reading supported by manual planimetry measures, and visual reading supported by automatic volumetry. Fully automatic classification was performed using a pre-trained support vector machine (SVM) on the results of atlas-based volumetry.

RESULTS

All tested methods achieved higher specificity than sensitivity. Limited sensitivity was driven to large extent by false negative vPSP cases. Support by automatic volumetry resulted in the highest accuracy (75.1% ± 3.5%) among the visual strategies, but performed not better than the midbrain area (75.9%), the best single planimetric measure. Automatic classification by SVM clearly outperformed all other methods (accuracy, 87.4%), representing the only method to provide clinically useful sensitivity also in vPSP (70.0%).

CONCLUSIONS

Fully automatic classification of volumetric MRI measures using machine learning methods outperforms visual MRI analysis without and with planimetry or volumetry support, particularly regarding diagnosis of vPSP, suggesting the use in settings with a broad phenotypic PSP spectrum. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Structural and Molecular Imaging for Clinically Uncertain Parkinsonism

Neuroimaging is an important adjunct to the clinical assessment of Parkinson disease (PD). Parkinsonism can be challenging to differentiate, especially in early disease stages, when it mimics other movement disorders or when there is a poor response to dopaminergic therapies. There is also a discrepancy between the phenotypic presentation of degenerative parkinsonism and the pathological outcome. The emergence of more sophisticated and accessible neuroimaging can identify molecular mechanisms of PD, the variation between clinical phenotypes, and the compensatory mechanisms that occur with disease progression. Ultra-high-field imaging techniques have improved spatial resolution and contrast that can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. We highlight the imaging modalities that can be accessed in clinical practice and recommend an approach to the diagnosis of clinically uncertain parkinsonism.

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

The challenging quest of neuroimaging: From clinical to molecular-based subtyping of Parkinson disease and atypical parkinsonisms

The current framework of Parkinson disease (PD) focuses on phenotypic classification despite its considerable heterogeneity. We argue that this method of classification has restricted therapeutic advances and therefore limited our ability to develop disease-modifying interventions in PD. Advances in neuroimaging have identified several molecular mechanisms relevant to PD, variation within and between clinical phenotypes, and potential compensatory mechanisms with disease progression. Magnetic resonance imaging (MRI) techniques can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging have informed the neurotransmitter, metabolic, and inflammatory dysfunctions that could potentially distinguish disease phenotypes and predict response to therapy and clinical outcomes. However, rapid advancements in imaging techniques make it challenging to assess the significance of newer studies in the context of new theoretical frameworks. As such, there needs to not only be a standardization of practice criteria in molecular imaging but also a rethinking of target approaches. In order to harness precision medicine, a coordinated shift is needed toward divergent rather than convergent diagnostic approaches that account for interindividual differences rather than similarities within an affected population, and focus on predictive patterns rather than already lost neural activity.

Magnetic resonance imaging modalities aid in the differential diagnosis of atypical parkinsonian syndromes

Accurate and timely diagnosis of atypical parkinsonian syndromes (APS) remains a challenge. Especially early in the disease course, the clinical manifestations of the APS overlap with each other and with those of idiopathic Parkinson's disease (PD). Recent advances in magnetic resonance imaging (MRI) technology have introduced promising imaging modalities to aid in the diagnosis of APS. Some of these MRI modalities are also included in the updated diagnostic criteria of APS. Importantly, MRI is safe for repeated use and more affordable and accessible compared to nuclear imaging. These advantages make MRI tools more appealing for diagnostic purposes. As the MRI field continues to advance, the diagnostic use of these techniques in APS, alone or in combination, are expected to become commonplace in clinical practice.

Clinical, cognitive, and morphometric profiles of progressive supranuclear palsy phenotypes

The International Parkinson's and Movement Disorder Society (MDS) criteria for progressive supranuclear palsy (PSP) have broadened the clinical spectrum of the disease and established phenotypic characterization according to the predominant manifestation at onset. The objective of this study is to describe clinical/cognitive and imaging features of a monocentric cohort of PSP patients, highlighting different patterns of functional disability according to the assigned phenotype. We retrospectively reviewed clinical/imaging data of 53 PSP patients diagnosed with probable PSP according to the MDS criteria and 40 age/sex-matched healthy controls (HCs). Neurological/neuropsychological assessments were performed using standardized scales, as well as comprehensive magnetic resonance imaging (MRI) morphometric measurements. In our cohort, there were 24/53 PSP-RS (Richardson's syndrome), 13/53 PSP-P (Parkinsonism), 7/53 PSP-PGF (Progressive gait freezing), and 9/53 PSP-Cog (Cognitive impairment). PSP-Cog presented the worst motor profiles, the highest percentages of dementia and impaired functional autonomy; 4/9 PSP-Cog and 2/7 PSP-PGF died. PSP-P had the lowest motor/cognitive burden. All MRI parameters had good discriminative efficacy vs. HCs, with P/M 2.0 discriminating PSP-PGF from PSP-RS and PSP-Cog. We highlighted discrete clinical and imaging patterns that best characterize different PSP phenotypes. PSP-Cog and PSP-PGF/RS manifest greater incidence of dementia and motor disability, respectively, while PSP-P has a more benign course. The identification of different phenotypes may be the expression of different progression patterns requiring tailored approaches in terms of follow-up and treatment. These findings support the concept of discrete patterns of Tau pathology within the PSP spectrum and encourage research for phenotype-specific outcome measures.

Diagnostic Performance of Putaminal Hypointensity on Susceptibility MRI in Distinguishing Parkinson Disease from Progressive Supranuclear Palsy: A Meta-Analysis

Background

Idiopathic Parkinson's disease (IPD) and progressive supranuclear palsy (PSP) have similar clinical signs and symptoms, making accurate clinical diagnosis difficult. T2* gradient echo (T2* GRE), susceptibility-weighted imaging (SWI), and quantitative susceptibility mapping (QSM) are susceptibility MR imaging sequences that provide more information about brain iron levels than other conventional MR imaging.

Objective

This study aimed to evaluate the diagnostic power of putaminal hypointensity on T2* GRE, SWI, and QSM in distinguishing PSP from IPD.

Methods

Eligible studies were identified via systematic searches of PubMed and Clarivate Analytics® Web of Science® Core Collection. Studies that satisfied the inclusion and exclusion criteria were reviewed. A meta-analysis was conducted using the hierarchical summary receiver operating characteristic curve approach.

Results

Our literature search of the two databases yielded 562 primary articles, 10 of which were deemed relevant and only six were eligible for further analyses. We performed a meta-analysis of putaminal hypointensity measurements: 438 patients with IPD and 109 patients with PSP were enrolled in the quantitative synthesis. The meta-analysis of six studies with 547 patients revealed a sensitivity of 69% (95% confidence interval (CI): 33%-90%) and specificity of 91% (95% CI: 80%-96%) for putaminal hypointensity on T2* GRE, SWI, or QSM distinguishing PSP from IPD.

Conclusions

Putaminal hypointensity on T2* GRE, SWI, or QSM is able to distinguish patients with PSP from those with IPD with high specificity. Further multicenter prospective studies on patients are needed to verify our results.

Cortical atrophy distinguishes idiopathic normal-pressure hydrocephalus from progressive supranuclear palsy: A machine learning approach

INTRODUCTION

Progressive supranuclear palsy (PSP) and idiopathic normal pressure hydrocephalus (iNPH) share several clinical and radiological features, making the differential diagnosis challenging. In this study, we aimed to differentiate between these two diseases using a machine learning approach based on cortical thickness and volumetric data.

METHODS

Twenty-three iNPH patients, 50 PSP patients and 55 control subjects were enrolled. All participants underwent a brain 3T-MRI, and cortical thickness and volumes were extracted using Freesurfer 6 on T1-weighted images and compared among groups. Finally, the performance of a machine learning approach with random forest using the extracted cortical features was investigated to differentiate between iNPH and PSP patients.

RESULTS

iNPH patients showed cortical thinning and volume loss in the frontal lobe, temporal lobe and cingulate cortex, and thickening in the superior parietal gyrus in comparison with controls and PSP patients. PSP patients only showed mild thickness and volume reduction in the frontal lobe, compared to control subjects. Random Forest algorithm distinguished iNPH patients from controls with AUC of 0.96 and from PSP patients with AUC of 0.95, while a lower performance (AUC 0.76) was reached in distinguishing PSP from controls.

CONCLUSION

This study demonstrated a more severe and widespread cortical involvement in iNPH than in PSP, possibly due to the marked lateral ventricular enlargement which characterizes iNPH. A machine learning model using thickness and volumetric data led to accurate differentiation between iNPH and PSP patients, which may help clinicians in the differential diagnosis and in the selection of patients for shunt procedures.

Magnetic Resonance Planimetry in the Differential Diagnosis between Parkinson’s Disease and Progressive Supranuclear Palsy

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.

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

Regional Selectivity of Neuromelanin Changes in the Substantia Nigra in Atypical Parkinsonism

BACKGROUND

Neurodegeneration in the substantia nigra pars compacta (SNc) in parkinsonian syndromes may affect the nigral territories differently.

OBJECTIVE

The objective of this study was to investigate the regional selectivity of neurodegenerative changes in the SNc in patients with Parkinson's disease (PD) and atypical parkinsonism using neuromelanin-sensitive magnetic resonance imaging (MRI).

METHODS

A total of 22 healthy controls (HC), 38 patients with PD, 22 patients with progressive supranuclear palsy (PSP), 20 patients with multiple system atrophy (MSA, 13 with the parkinsonian variant, 7 with the cerebellar variant), 7 patients with dementia with Lewy body (DLB), and 4 patients with corticobasal syndrome were analyzed. volume and signal-to-noise ratio (SNR) values of the SNc were derived from neuromelanin-sensitive MRI in the whole SNc. Analysis of signal changes was performed in the sensorimotor, associative, and limbic territories of the SNc.

RESULTS

SNc volume and corrected volume were significantly reduced in PD, PSP, and MSA versus HC. Patients with PSP had lower volume, corrected volume, SNR, and contrast-to-noise ratio than HC and patients with PD and MSA. Patients with PSP had greater SNR reduction in the associative region than HC and patients with PD and MSA. Patients with PD had reduced SNR in the sensorimotor territory, unlike patients with PSP. Patients with MSA did not differ from patients with PD.

CONCLUSIONS

This study provides the first MRI comparison of the topography of neuromelanin changes in parkinsonism. The spatial pattern of changes differed between PSP and synucleinopathies. These nigral topographical differences are consistent with the topography of the extranigral involvement in parkinsonian syndromes. © 2022 International Parkinson and Movement Disorder Society.

The Strengths and Obstacles in the Differential Diagnosis of Progressive Supranuclear Palsy—Parkinsonism Predominant (PSP-P) and Multiple System Atrophy (MSA) Using Magnetic Resonance Imaging (MRI) and Perfusion Single Photon Emission Computed Tomography (SPECT)

Multiple System Atrophy—Parkinsonism Predominant (MSA-P) and Progressive Supranuclear Palsy—Parkinsonism Predominant (PSP-P) are the clinical manifestations of atypical parkinsonism. Currently, there are no efficient in vivo methods available relating to neuroimaging or biochemical analysis in the examination of these entities. Among the advanced methods available, using positron emission tomography is constrained by high cost and low accessibility. In this study the authors examined patients with two types of atypical parkinsonism—MSA-P and PSP-P, which are difficult to differentiate, especially in the early years of their development. The aim of this study was to assess whether the examination of patients in the period following the early years (3–6-year duration of symptoms) could be enhanced by perfusion single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI) or evaluation of cognitive abilities. Extended examination using MRI and perfusion SPECT showed that the evaluation of the mesencephalon/pons ratio, mesencephalic volume decrease, the Magnetic Resonance Parkinsonism Index (MRPI) and frontal perfusion should be considered more feasible than screening cognitive evaluation in MSA-P and PSP-P with a 3–6-year duration of symptoms.

Exosomal miRNA as peripheral biomarkers in Parkinson's disease and progressive supranuclear palsy: A pilot study

INTRODUCTION

Parkinson's disease (PD), a progressive neurodegenerative disease, can be misdiagnosed with atypical conditions such as Progressive Supranuclear Paralysis (PSP) due to overlapping clinical features. MicroRNAs (miRNAs) are small non-coding RNAs with a key role in post-transcriptional gene regulation. The aim was to identify a set of differential exosomal miRNAs biomarkers, which may aid in diagnosis.

METHODS

We analyzed the serum level of 188 miRNAs in a discovery set, by using RTqPCR based TaqMan assay, in a small cohort of healthy controls, PD and PSP patients. Subsequently, the differentially expressed miRNAs, between PSP and PD patients, were further tested in a larger and independent cohort of 33 healthy controls, 40 PD and 20 PSP patients. The most accurate diagnostic exosomal miRNAs classifiers were identified in a logistic regression model.

RESULTS

A statistically significant set of three exosomal miRNAs: miR-21-3p, miR-22-3p and miR-223-5p, discriminated PD from HC (area under the curve of 0.75), and a set of three exosomal miRNAs, miR-425-5p, miR-21-3p, and miR-199a-5p, discriminated PSP from PD with good diagnostic accuracy (area under the curve of 0.86). Finally, the classifier that best discriminated PSP from PD consisted of six exosomal miRNAs (area under the curve = 0.91), with diagnostic sensitivity and specificity of 0.89 and 0.90, respectively.

CONCLUSIONS

Based on our analysis, these data showed that exosomal miRNAs could act as biomarkers to differentiate between PSP and PD.

Human wildtype tau expression in cholinergic pedunculopontine tegmental neurons is sufficient to produce PSP-like behavioural deficits and neuropathology

Progressive Supranuclear Palsy (PSP) is the most common atypical parkinsonism and exhibits hallmark symptomology including motor function impairment and dysexecutive dementia. In contrast to Parkinson's disease, the underlying pathology displays aggregation of the protein tau, which is also seen in disorders such as Alzheimer's disease. Currently, there are no pharmacological treatments for PSP, and drug discovery efforts are hindered by the lack of an animal model specific to PSP. Based on previous results and clinical pathology, it was hypothesized that viral deposition of tau in cholinergic neurons within the hindbrain would produce a tauopathy along neural connections to produce PSP-like symptomology and pathology. By using a combination of ChAT-CRE rats and CRE-dependent AAV vectors, wildtype human tau (the PSP-relevant 1N4R isoform; hTau) was expressed in hindbrain cholinergic neurons. Compared to control subjects (GFP), rats with tau expression displayed deficits in a variety of behavioural paradigms: acoustic startle reflex, marble burying, horizontal ladder and hindlimb motor reflex. Postmortem, the hTau rats had significantly reduced number of cholinergic pedunculopontine tegmentum and dopaminergic substantia nigra neurons, as well as abnormal tau deposits. This preclinical model has multiple points of convergence with the clinical features of PSP, some of which distinguish between PSP and Parkinson's disease.

Progressive supranuclear palsy with marked ventricular dilatation mimicking normal pressure hydrocephalus

BACKGROUND

Progressive supranuclear palsy (PSP) patients can show ventricular enlargement mimicking normal pressure hydrocephalus (NPH). The aim of this study was to distinguish PSP patients with marked ventricular dilatation (PSP-vd) from those with normal ventricular system and to evaluate the coexistence of NPH in PSP-vd patients.

METHODS

One hundred three probable PSP patients, 18 definite NPH patients, and 41 control subjects were enrolled in the study. Evans index (EI) > 0.32 associated with callosal angle (CA) < 100° was used to identify PSP-vd patients. Automated ventricular volumetry (AVV) and Magnetic Resonance Hydrocephalic Index (MRHI) were performed on T1-weighted MR images to evaluate the presence of NPH in PSP-vd patients.

RESULTS

Twelve (11.6%) out of 103 PSP patients had both abnormal EI and CA values (PSP-vd). In two of these 12 patients, AVV and MRHI values suggested PSP + NPH. In the remaining 10 PSP-vd patients, AVV and MRHI values were higher than PSP patients with normal ventricular system and controls, but lower than PSP + NPH and NPH patients, suggesting a non-hydrocephalic ventricular enlargement.

DISCUSSION

Our study provides evidence that the combination of EI and CA biomarkers allowed to identify PSP patients with marked ventricular dilatation mimicking NPH. Only a few of these patients had PSP + NPH. Recognition of these PSP patients with enlarged ventricles can positively impact the care of this disease, helping clinicians to identify patients with PSP + NPH who could benefit from shunt procedure and avoid surgery in those with enlarged ventricles without NPH.

Harnessing the immune system for the treatment of Parkinson's disease

Current treatment options for Parkinson's disease (PD) typically aim to replace dopamine, and hence only provide symptomatic relief. However, in the long run, this approach alone loses its efficacy as it is associated with debilitating side effects. Hence there is an unmet clinical need for addressing levodopa resistant symptoms, and an urgency to develop therapies that can halt or prevent the course of PD. The premise that α-syn can transmit from cell-to-cell in a prion like manner has opened up the possibility for the use of immunotherapy in PD. There is evidence for inflammation in PD as is evidenced by microglial activation, as well as the involvement of the peripheral immune system in PD, and peripheral inflammation can exacerbate dopaminergic degeneration as seen in animal models of the disease. However, mechanisms that link the immune system with PD are not clear, and the sequence of immune responses with respect to PD are still unknown. Nevertheless, our present knowledge offers avenues for the development of immune-based therapies for PD. In order to successfully employ such strategies, we must comprehend the state of the peripheral immune system during the course of PD. This review describes the developments in the field of both active and passive immunotherapies in the treatment of PD, and highlights the crucial need for future research for clarifying the role of inflammation and immunity in this debilitating disease.

Prevalence and impact of COVID-19 in Parkinson's disease: evidence from a multi-center survey in Tuscany region

Background

If Parkinson’s Disease (PD) may represent a risk factor for Coronavirus disease 2019 (COVID-19) is debated and there are few data on the direct and indirect effects of this pandemic in PD patients.

Objective

In the current study we evaluated the prevalence, mortality and case-fatality of COVID-19 in a PD cohort, also exploring possible risk factors. We also aimed to investigate the effect of lockdown on motor/non-motor symptoms in PD patients as well as their acceptability/accessibility to telemedicine.

Method

A case-controlled survey about COVID-19 and other clinical features in PD patients living in Tuscany was conducted. In non-COVID-19 PD patients motor/non-motor symptoms subjective worsening during the lockdown as well as feasibility of telemedicine were explored.

Results

Out of 740 PD patients interviewed, 7 (0.9%) were affected by COVID-19, with 0.13% mortality and 14% case-fatality. COVID-19 PD patients presented a higher presence of hypertension (p < 0.001) and diabetes (p = 0.049) compared to non-COVID-19. In non-COVID-19 PD population (n = 733) about 70% did not experience a subjective worsening of motor symptoms or mood, anxiety or insomnia. In our population 75.2% of patients was favorable to use technology to perform scheduled visits, however facilities for telemedicine were available only for 51.2% of cases.

Conclusion

A higher prevalence of COVID-19 respect to prevalence in Tuscany and Italy was found in the PD population. Hypertension and diabetes, as for general population, were identified as risk factors for COVID-19 in PD. PD patients did not experience a subjective worsening of symptoms during lockdown period and they were also favorable to telemedicine, albeit we reported a reduced availability to perform it.