Diagnostic Performance of the Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson's Disease: An Updated Systematic Review and Meta-Analysis

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.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Objectives

Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features.

Methods

We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.

Results

The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk.

Conclusions

The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

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.

Morphometric imaging and quantitative susceptibility mapping as complementary tools in the diagnosis of parkinsonisms

Background and purpose

In the quest for in vivo diagnostic biomarkers to discriminate Parkinson's disease (PD) from progressive supranuclear palsy (PSP) and multiple system atrophy (MSA, mainly p phenotype), many advanced magnetic resonance imaging (MRI) techniques have been studied. Morphometric indices, such as the Magnetic Resonance Parkinsonism Index (MRPI), demonstrated high diagnostic value in the comparison between PD and PSP. The potential of quantitative susceptibility mapping (QSM) was hypothesized, as increased magnetic susceptibility (Δχ) was reported in the red nucleus (RN) and medial part of the substantia nigra (SNImed) of PSP patients and in the putamen of MSA patients. However, disease‐specific susceptibility values for relevant regions of interest are yet to be identified. The aims of the study were to evaluate the diagnostic potential of a multimodal MRI protocol combining morphometric and QSM imaging in patients with determined parkinsonisms and to explore its value in a population of undetermined cases.

Method

Patients with suspected degenerative parkinsonism underwent clinical evaluation, 3 T brain MRI and clinical follow‐up. The MRPI was manually calculated on T1‐weighted images. QSM maps were generated from 3D multi‐echo T2*‐weighted sequences.

Results

In determined cases the morphometric evaluation confirmed optimal diagnostic accuracy in the comparison between PD and PSP but failed to discriminate PD from MSA‐p. Significant nigral and extranigral differences were found with QSM. RN Δχ showed excellent diagnostic accuracy in the comparison between PD and PSP and good accuracy in the comparison of PD and MSA‐p. Optimal susceptibility cut‐off values of RN and SNImed were tested in undetermined cases in addition to MRPI.

Conclusions

A combined use of morphometric imaging and QSM could improve the diagnostic phase of degenerative parkinsonisms.