AIMC Topic: Supranuclear Palsy, Progressive

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Diagnostic utility of biomarkers in progressive supranuclear palsy: toward a biotyping framework.

Journal of neurology
Progressive supranuclear palsy (PSP) is a rare and debilitating four-repeat (4R) tauopathy, characterized by motor dysfunction, cognitive decline, and oculomotor abnormalities, yet it lacks reliable biomarkers for early diagnosis, disease stratificat...

Early subtypes and progressions of progressive supranuclear palsy: a data-driven brain bank study.

Journal of neurology
BACKGROUND: Progressive supranuclear palsy (PSP) is typically characterized by vertical supranuclear gaze palsy and early falls, referred to as Richardson's syndrome (PSP-RS). Other presentations include postural instability (PSP-PI), Parkinsonism (P...

Machine learning approach effectively discriminates between Parkinson's disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI.

Brain research bulletin
AIM: Parkinson's disease (PD) and progressive supranuclear palsy (PSP) present similar clinical symptoms, but their treatment options and clinical prognosis differ significantly. Therefore, we aimed to discriminate between PD and PSP based on multi-l...

Progression and natural history of Atypical Parkinsonism (ATPARK): Protocol for a longitudinal follow-up study from an underrepresented population.

PloS one
BACKGROUND: Atypical Parkinsonian Syndromes (APS) form the third largest group of neurodegenerative disorders including Progressive Supranuclear Palsy (PSP), Multiple System Atrophy (MSA), and Corticobasal Syndrome (CBS). These conditions are charact...

A radiomics approach to distinguish Progressive Supranuclear Palsy Richardson's syndrome from other phenotypes starting from MR images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Progressive Supranuclear Palsy (PSP) is an uncommon neurodegenerative disorder with different clinical onset, including Richardson's syndrome (PSP-RS) and other variant phenotypes (vPSP). Recognising the clinical progression...

Application of elastic net for clinical outcome prediction and classification in progressive supranuclear palsy: A multicenter cohort study.

Parkinsonism & related disorders
BACKGROUND: Previous studies have used machine learning to identify clinically relevant atrophic regions in progressive supranuclear palsy (PSP). This study applied Elastic Net (EN) in PSP to uncover key atrophic patterns, offering a novel approach t...

MRI classification of progressive supranuclear palsy, Parkinson disease and controls using deep learning and machine learning algorithms for the identification of regions and tracts of interest as potential biomarkers.

Computers in biology and medicine
BACKGROUND: Quantitative magnetic resonance imaging (MRI) analysis has shown promise in differentiating neurodegenerative Parkinsonian syndromes and has significantly advanced our understanding of diseases like progressive supranuclear palsy (PSP) in...

Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning.

NeuroImage
Differences in iron accumulation patterns have been observed in susceptibility-weighted images across different classes of atypical parkinsonian syndromes (APS). Deep learning methods have shown great potential in automatically detecting these differ...

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

Parkinsonism & related disorders
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 i...