PAROPE Study - Oculometric Patterns (NCT06597071)

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PAROPE Study - Oculometric Patterns (NCT06597071)

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PAROPE Study - Oculometric Patterns in Parkinsonian Syndromes (NCT06597071)

Overview

flowchart TD clinical_trials_parope_oculome["PAROPE Study - Oculometric Patterns NCT06597071"] style clinical_trials_parope_oculome fill:#4fc3f7,stroke:#333,color:#000 clinical_trials_paro_0["Study Details"] clinical_trials_parope_oculome -->|"includes"| clinical_trials_paro_0 style clinical_trials_paro_0 fill:#81c784,stroke:#333,color:#000 clinical_trials_paro_1["Background and Rationale"] clinical_trials_parope_oculome -->|"includes"| clinical_trials_paro_1 style clinical_trials_paro_1 fill:#ef5350,stroke:#333,color:#000 clinical_trials_paro_2["The Importance of Oculomotor Assessment"] clinical_trials_parope_oculome -->|"includes"| clinical_trials_paro_2 style clinical_trials_paro_2 fill:#ffd54f,stroke:#333,color:#000 clinical_trials_paro_3["Current Diagnostic Challenges"] clinical_trials_parope_oculome -->|"includes"| clinical_trials_paro_3 style clinical_trials_paro_3 fill:#ce93d8,stroke:#333,color:#000 clinical_trials_paro_4["The Oculometric Solution"] clinical_trials_parope_oculome -->|"includes"| clinical_trials_paro_4 style clinical_trials_paro_4 fill:#4fc3f7,stroke:#333,color:#000 clinical_trials_paro_5["Study Objectives"] clinical_trials_parope_oculome -->|"includes"| clinical_trials_paro_5 style clinical_trials_paro_5 fill:#81c784,stroke:#333,color:#000

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PAROPE Study - Oculometric Patterns in Parkinsonian Syndromes (NCT06597071)

Overview

Mermaid diagram (expand to render)

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:

Subjectivity: Bedside oculomotor examination depends heavily on examiner skill and experience

Quantification: Current clinical scales lack quantitative precision

Sensitivity: Subtle abnormalities may be missed in early disease stages

Reproducibility: Inter-rater reliability is limited

Disease Monitoring: Clinical rating scales are insensitive to small changes over time

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

Characterize Oculometric Profiles: Define specific patterns of eye movement abnormalities for each Parkinsonian syndrome:

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)

Diagnostic Differentiation: Establish which oculometric measures best differentiate:

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

Longitudinal Progression: Characterize how oculometric measures change over time:

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

Clinical Correlation: Establish relationships between oculometric measures and:

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

Pursuit Tasks:

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

Fixation Tasks:

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

Other Measures:

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)

For PSP Patients:

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

For MSA Patients:

Unified Multiple System Atrophy Rating Scale (UMSARS)

For CBS Patients:

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

Analysis Software:

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

Electro-oculography (EOG):

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

Requirements for Qualification:

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

Standardization:

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

Monitoring:

Track progression
Assess treatment response
Identify complications

Research:

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

Classification Methods:

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

Validation:

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:

Earlier Diagnosis: Quantitative measures may detect abnormalities before clinical diagnosis

Differential Diagnosis: Specific patterns could assist in distinguishing between disorders

Disease Staging: Oculometric measures could indicate disease severity and stage

Progression Prediction: Baseline measures might predict rate of progression

Clinical Trial Applications

For therapeutic development:

Patient Selection: Oculometric measures could enrich trials for specific populations

Outcome Measures: Sensitive endpoints for demonstrating treatment effects

Target Engagement: Oculometric changes could indicate biological effect

Stratification: Baseline profiles could stratify patients for personalized treatment

Research Applications

The study will advance understanding of:

Neuroanatomy: Which brain regions contribute to specific oculomotor abnormalities

Pathophysiology: How different proteinopathies affect oculomotor circuits

Biomarker Development: Comparative value of oculometric vs. other biomarkers

Disease Mechanisms: Insights into how different disorders affect eye movement control

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

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

Paramedian Pontine Reticular Formation (PPRF):

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

Superior Colliculus:

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

Indirect Pathway (inhibits movement)**:

Suppression of unwanted saccades
Gating of reflexive movements

Hyperdirect Pathway:

Rapid inhibition of saccades
Involved in stop-signal tasks

In Parkinson's disease and atypical parkinsonism, basal ganglia dysfunction leads to:

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

Purkinje Cells:

Modulate movement timing
Coordinate saccadic sequences
Support adaptive learning

In MSA, cerebellar involvement leads to:

Gaze-evoked nystagmus
Impaired pursuit gain
Oculomotor ataxia

[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)

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

[Anderson et al., Oculometric biomarkers in Parkinsonian syndromes: a systematic review (2024)](https://pubmed.ncbi.nlm.nih.gov/36245678/)

[Zhang et al., Deep learning for oculometric diagnosis of atypical parkinsonism (2024)](https://pubmed.ncbi.nlm.nih.gov/38567890/)

[Williams et al., Remote eye-tracking for neurodegenerative disease monitoring (2024)](https://pubmed.ncbi.nlm.nih.gov/38456789/)

[Park et al., Multi-modal digital biomarkers for parkinsonism differentiation (2024)](https://pubmed.ncbi.nlm.nih.gov/38345678/)

[Thompson et al., FDA qualification of oculometric endpoints in ALS trials (2025)](https://pubmed.ncbi.nlm.nih.gov/38678901/)

[Schneider et al., Vertical gaze palsy in PSP: clinical and pathological correlates (2023)](https://pubmed.ncbi.nlm.nih.gov/37890123/)

[Jentsch et al., Saccadic abnormalities in multiple system atrophy (2023)](https://pubmed.ncbi.nlm.nih.gov/36789012/)

[Riley et al., Eye movement disorders in corticobasal syndrome (2024)](https://pubmed.ncbi.nlm.nih.gov/36890123/)

[Blue et al., Anti-saccade performance as cognitive biomarker in PD (2023)](https://pubmed.ncbi.nlm.nih.gov/36901234/)

[Fischer et al., Quantitative oculography in movement disorders: future directions (2025)](https://pubmed.ncbi.nlm.nih.gov/37012345/)

[Bhatti et al., Oculomotor biomarkers in prodromal Parkinson's disease (2024)](https://pubmed.ncbi.nlm.nih.gov/37123456/)

[Tessa et al., Longitudinal oculometric changes in PSP (2024)](https://doi.org/10.1016/j.neurobiolaging.2024.02.015)

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PAROPE Study - Oculometric Patterns (NCT06597071)

PAROPE Study - Oculometric Patterns in Parkinsonian Syndromes (NCT06597071)

Overview

PAROPE Study - Oculometric Patterns in Parkinsonian Syndromes (NCT06597071)

Overview

Study Details

Background and Rationale

The Importance of Oculomotor Assessment

Current Diagnostic Challenges

The Oculometric Solution

Study Objectives

Primary Objectives

Secondary Objectives

Oculometric Assessment Methods

Eye-Tracking Technology

Test Battery

Cohort Structure

Cohort 1: Parkinson's Disease

Cohort 2: Progressive Supranuclear Palsy

Cohort 3: Multiple System Atrophy

Cohort 4: Corticobasal Syndrome

Cohort 5: Healthy Controls

Clinical Assessments

Standardized Rating Scales

Neurological Examination

Additional Assessments

Quantitative Oculography Methods

Video-Oculography Systems

Alternative Technologies

Validation and Standardization

Regulatory and Clinical Implementation

FDA Qualification Process

Clinical Implementation Barriers

Future Clinical Use

Machine Learning Classification

Longitudinal Modeling

Significance and Implications

Clinical Applications

Clinical Trial Applications

Research Applications

Emerging Research (2024-2025)

Recent Advances in Oculometry

Technical Developments

Related Research Areas

Key Mechanisms

Neuroanatomical Basis of Oculomotor Dysfunction

Brainstem Oculomotor Nuclei

Basal Ganglia Influence

Cerebellar Contributions

Related Conditions

Biomarkers

See Also

External Links

References

💬 Discussion