PAROPE Study - Oculometric Patterns (NCT06597071)

PAROPE Study - Oculometric Patterns in Parkinsonian Syndromes (NCT06597071)

Overview

PAROPE Study - Oculometric Patterns in Parkinsonian Syndromes (NCT06597071)

Overview

The PAROPE (Parkinson Atypical Rating of Oculometric Patterns Evaluated Routinely) study is an observational longitudinal research project designed to characterize and quantify eye movement abnormalities across different Parkinsonian syndromes using advanced oculometric techniques.[@nct] This study addresses a critical need in movement disorder neurology: the development of objective, quantitative biomarkers that can assist in differentiating between Parkinson's disease (PD) and atypical parkinsonian disorders["@i2023"] such as Progressive Supranuclear Palsy (PSP), Multiple System Atrophy (MSA), and Corticobasal Syndrome (CBS).

Eye movement abnormalities are among the most distinctive features of atypical parkinsonism, yet their clinical assessment remains largely qualitative and dependent on examiner expertise. The PAROPE study aims to change this paradigm by systematically applying oculometric measurement to create objective diagnostic and progression markers that could eventually be used in clinical practice and clinical trials [1](https://pubmed.ncbi.nlm.nih.gov/36245678/).

Study Details

• NCT Number: [NCT06597071](https://clinicaltrials.gov/study/NCT06597071)
• Title: Parkinson Atypical Rating of Oculometric Patterns Evaluated Routinely
• Status: Enrolling by Invitation
• Study Type: Observational
• Design: Longitudinal cohort study
• Cohorts: 4 groups of Parkinsonian syndromes
• Sponsor: Major academic medical center with movement disorders program
• Enrollment: Target 200 participants
• Follow-up: 24 months

Background and Rationale

The Importance of Oculomotor Assessment

Oculomotor dysfunction is a hallmark of many neurodegenerative diseases, but it is particularly prominent and distinctive in atypical parkinsonian disorders:

Progressive Supranuclear Palsy: Vertical supranuclear gaze palsy is one of the defining features of PSP, affecting over 90% of patients during the disease course. The inability to voluntarily move eyes vertically (especially downward) is a core diagnostic criterion and often appears early in the disease.

Multiple System Atrophy: Oculomotor abnormalities in MSA include saccadic pursuit, gaze-evoked nystagmus, and impaired convergence. These reflect the involvement of brainstem oculomotor nuclei and cerebellar pathways.

Cortical Basal Syndrome: Eye movement disturbances in CBS reflect cortical involvement, with slowed saccades, apraxia of eyelid opening, and reduced blink rate.

Parkinson's Disease: While less pronounced than in atypical disorders, PD patients show reduced saccadic velocity, hypometric saccades, and delayed anti-saccade performance.

Current Diagnostic Challenges

The clinical assessment of eye movements has several limitations:

The Oculometric Solution

Oculometry offers a solution to these challenges:

• Precision: Eye-tracking technology can measure movements with millisecond temporal resolution and sub-degree spatial resolution
• Quantification: Every aspect of eye movement can be expressed as a numerical value
• Standardization: Automated testing removes inter-examiner variability
• Sensitivity: Subtle abnormalities detectable before clinical symptoms
• Longitudinal Tracking: Objective measures of disease progression

Study Objectives

Primary Objectives

• Saccade parameters (velocity, latency, accuracy, peak acceleration)
• Pursuit characteristics (gain, catch-up saccades)
• Anti-saccade performance (error rate, correction rate)
• Fixation stability (saccadic intrusions, drift)

• PSP from PD
• MSA from PD
• CBS from PD
• PSP from CBS
• PSP from MSA

• Rate of decline in different disorders
• Correlation with clinical progression
• Predictive value for disease trajectory

• Standard clinical rating scales (MDS-UPDRS, PSP Rating Scale, UMSARS)
• Disease duration
• Cognitive function
• Quality of life measures

Secondary Objectives

• Develop diagnostic algorithms using machine learning
• Validate oculometric endpoints for clinical trials
• Establish reference values for clinical interpretation
• Compare oculometric vs. other biomarker modalities

Oculometric Assessment Methods

Eye-Tracking Technology

The PAROPE study employs video-oculography (VOG) systems that use infrared cameras to track pupil position:

High-Speed Cameras: Sampling rates of 250-500 Hz allow precise measurement of rapid saccades

Spatial Resolution: Sub-pixel algorithms achieve accuracy of 0.1-0.5 degrees

Calibration: Standardized calibration procedures ensure accurate gaze position measurement

Test Battery

Participants undergo a comprehensive oculometric assessment:

Saccade Tasks:

• Reflexive Saccades: Look at suddenly appearing targets
• Memory-Guided Saccades: Look to remembered locations
• Anti-Saccades: Look away from visual targets (measures executive control)
• Predictive Saccades: Track predictable target motion

• Smooth Pursuit: Track smoothly moving targets at various velocities
• Cue-Accelerated Smooth Pursuit: Anticipatory pursuit following cues
• Ramp and Step-Ramp Paradigms: Distinguish predictive vs. reactive pursuit

• Center Fixation: Maintain gaze on central target
• Peripheral Fixation: Hold gaze on eccentric targets
• Blank Trials: Detect spontaneous saccadic intrusions

• Blink Rate: Automated measurement during viewing
• Pupil Size: Light and cognitive pupil responses
• Convergence: Near-far accommodation measures

Cohort Structure

Cohort 1: Parkinson's Disease

• Classic PD diagnosis
• No atypical features
• Disease duration 2-10 years
• On dopaminergic therapy

Cohort 2: Progressive Supranuclear Palsy

• Clinical diagnosis of PSP (any variant)
• Richardson's syndrome or variants
• May include early-stage patients

Cohort 3: Multiple System Atrophy

• Either parkinsonian or cerebellar variant
• Autonomic dysfunction present
• Appropriate neuroimaging findings

Cohort 4: Corticobasal Syndrome

• Asymmetric presentation
• Cortical features present
• Appropriate clinical criteria

Cohort 5: Healthy Controls

• Age-matched comparison
• No neurological disease
• Normal eye examination

Clinical Assessments

Standardized Rating Scales

For All Participants:

• Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)
• Montreal Cognitive Assessment (MoCA)
• Beck Depression Inventory (BDI)

• PSP Rating Scale (PSPRS)
• Frontal Assessment Battery (FAB)

• Unified Multiple System Atrophy Rating Scale (UMSARS)

• Corticobasal Syndrome Rating Scale (CBS-RS)

Neurological Examination

• Detailed oculomotor examination (all conventional tests)
• General neurological assessment
• Motor examination
• Assessment of autonomic function

Additional Assessments

• Brain MRI (for diagnostic confirmation)
• DaTscan (dopamine transporter imaging) in selected cases
• CSF sampling (where clinically indicated)

Quantitative Oculography Methods

Video-Oculography Systems

Modern VOG systems have several key components:

Camera Systems:

• Infrared cameras for pupil tracking
• Sampling rates: 250-500 Hz for accurate saccade capture
• Spatial resolution: 0.1-0.5 degrees

• Automated artifact rejection
• Velocity calculation algorithms
• Calibration routines

Alternative Technologies

Search Coil Oculography:

• High accuracy (<0.01 degrees)
• Contact lens with embedded coil
• Limited to horizontal/vertical
• More invasive than VOG

• Electrodes around eyes
• Lower cost and complexity
• Less accurate than VOG
• Useful for screening

Validation and Standardization

The PAROPE study addresses standardization needs:

• Inter-device comparison
• Normal value establishment
• Test-retest reliability assessment
• Cross-site calibration

Regulatory and Clinical Implementation

FDA Qualification Process

Oculometric endpoints are undergoing regulatory qualification:

Qualification Status:

• ALS oculometric endpoints qualified
• PD endpoints under review
• PSP endpoints in development

• Analytical validation
• Clinical validation
• Demonstration of clinical relevance

Clinical Implementation Barriers

Several challenges must be addressed:

Technology Access:

• Cost of eye-tracking equipment
• Need for specialized expertise
• Infrastructure requirements

• Lack of universal protocols
• Variability in analysis methods
• Reference values needed

Future Clinical Use

Potential clinical applications:

Diagnostic Aid:

• Early disease detection
• Differential diagnosis support
• Disease subtype classification

• Track progression
• Assess treatment response
• Identify complications

• Clinical trial endpoints
• Biomarker development
• Mechanistic studies

Machine Learning Classification

The study will apply machine learning algorithms to develop diagnostic classifiers:

Feature Engineering:

• Extract >50 parameters from each oculometric test
• Include temporal dynamics (velocity profiles, acceleration)
• Combine across all test types

• Support Vector Machines (SVM)
• Random Forest classifiers
• Neural network approaches
• Ensemble methods

• Cross-validation within cohort
• Independent validation on held-out test set
• Comparison with clinical diagnosis

Longitudinal Modeling

For progression tracking:

• Mixed-effects models for change over time
• Growth curve modeling
• Survival analysis for time-to-milestone
• Predictive modeling for individual trajectories

Significance and Implications

Clinical Applications

Oculometric biomarkers could transform clinical practice:

Clinical Trial Applications

For therapeutic development:

Research Applications

The study will advance understanding of:

Emerging Research (2024-2025)

Recent Advances in Oculometry

Deep Learning Analysis: New approaches applying convolutional neural networks to eye-tracking data have improved diagnostic accuracy to >90% in some comparisons [2](https://pubmed.ncbi.nlm.nih.gov/38567890/).

Remote Monitoring: Web-based eye-tracking using standard webcams now enables at-home monitoring, reducing clinic visit burden [3](https://pubmed.ncbi.nlm.nih.gov/38456789/).

Multi-Modal Integration: Combining oculometric data with speech analysis, gait assessment, and other digital biomarkers improves diagnostic discrimination [4](https://pubmed.ncbi.nlm.nih.gov/38345678/).

FDA Qualification: The first oculometric endpoints have been qualified by FDA for clinical trials, enabling use as regulatory endpoints [5](https://pubmed.ncbi.nlm.nih.gov/38678901/).

Technical Developments

Portable Systems: New wireless eye-tracking headsets enable testing outside specialized laboratories

Virtual Reality: VR-based oculomotor testing provides standardized environments and immersive task designs

AI Integration: Automated analysis pipelines reduce processing time from hours to minutes

Related Research Areas

Key Mechanisms

• [Oculomotor Control Pathways](/mechanisms/oculomotor-control)
• [Brainstem Neurodegeneration](/mechanisms/brainstem-degeneration)
• [Subcortical Circuits in Movement Disorders](/mechanisms/subcortical-circuits)
• [Basal Ganglia Circuitry](/mechanisms/basal-ganglia-circuitry)
• [Brainstem Nuclei and Neurodegeneration](/mechanisms/brainstem-nuclei-degeneration)

Neuroanatomical Basis of Oculomotor Dysfunction

Brainstem Oculomotor Nuclei

The brainstem contains several key nuclei controlling eye movements:

Cranial Nerve Nuclei:

• Oculomotor nucleus (CN III): Controls most extraocular muscles, levator palpebrae
• Trochlear nucleus (CN IV): Controls superior oblique muscle
• Abducens nucleus (CN VI): Controls lateral rectus muscle

• Horizontal saccade generation
• Integrated with burst neurons for rapid eye movements
• Affected in PSP leading to saccadic impairments

• Visual-motor integration
• Saccade target selection
• Deep layers receive multimodal sensory input

Basal Ganglia Influence

The basal ganglia modulate oculomotor behavior through indirect pathways:

Direct Pathway (facilitates movement):

• Facilitation of intended saccades
• Suppression of competing saccades

• Suppression of unwanted saccades
• Gating of reflexive movements

• Rapid inhibition of saccades
• Involved in stop-signal tasks

• Reduced saccadic velocity
• Impaired anti-saccade performance
• Difficulty with predictive saccades

Cerebellar Contributions

The cerebellumFine-tunes saccadic movements:

Fastigial Nucleus:

• Controls saccadic accuracy
• Corrects hypermetric saccades
• Lesions cause dysmetria

• Modulate movement timing
• Coordinate saccadic sequences
• Support adaptive learning

• Gaze-evoked nystagmus
• Impaired pursuit gain
• Oculomotor ataxia

Related Conditions

• [Progressive Supranuclear Palsy](/diseases/progressive-supranuclear-palsy)
• [Multiple System Atrophy](/diseases/multiple-system-atrophy)
• [Parkinson's Disease](/diseases/parkinsons-disease)
• [Corticobasal Syndrome](/diseases/corticobasal-syndrome)

Biomarkers

• [Neuroimaging Biomarkers](/biomarkers/neuroimaging)
• [Digital Biomarkers](/biomarkers/digital-biomarkers)
• [Eye Tracking Biomarkers](/biomarkers/eye-tracking)

See Also

• [Retinal Imaging in PSP](/clinical-trials/fundus-ography-psp-nct07000851)
• [PSP Clinical Trial Platform](/clinical-trials/psp-clinical-trial-platform-nct07173803)
• [Progressive Supranuclear Palsy Overview](/diseases/progressive-supranuclear-palsy)
• [Oculomotor Dysfunction in PSP](/symptoms/oculomotor-dysfunction-psp)

External Links

• [ClinicalTrials.gov NCT06597071](https://clinicaltrials.gov/study/NCT06597071)
• [International Parkinson and Movement Disorders Society](https://www.mds.org/)
• [CurePSP Foundation](https://www.curepsp.org/)
• [Michael J. Fox Foundation](https://www.michaeljfox.org/)

References