Saccadic network dysfunction underlies eye-movement variability in early Parkinson's disease
- 1Institut du Cerveau et de la Moelle epiniere (Paris Brain Institute)
Abstract
Parkinsons disease is a neurodegenerative disorder characterized by altered subcortical-cortical connectivity, leading to motor and cognitive deficits. Saccadic eye movement abnormalities are among the earliest and most persistent markers of the disease, serving as sensitive indicators of disrupted oculomotor and cognitive control. While changes in average saccadic behavioral measures are widely reported in Parkinson's disease, intra-individual variability in saccadic performance and the underlying network dysfunction remain poorly understood. Here, we quantified intra-individual saccadic variability in early-stage Parkinsons disease and identified its functional correlates within the resting-state saccadic network. We analyzed data from 91 patients with early-stage Parkinsons disease and 46 healthy controls who underwent oculomotor testing and resting-state functional magnetic resonance imaging. First, at the behavioral level, we assessed intra-individual variability in saccadic performance within each group using the coefficient of variation of reaction time and normalized peak velocity across trials. Second, at the brain level, we assessed group differences in static and dynamic functional connectivity across 39 regions comprising the saccadic network. We examined the relationship between network connectivity and saccadic variability using partial least squares analysis. We also quantified network organization using graph-theory measures of global efficiency and clustering coefficient. Finally, we tested the relevance of intra-individual saccadic variability and network features in distinguishing patients from healthy controls using a multivariate Mahalanobis classifier. Our results showed that, compared to HC, patients had higher intra-individual variability in saccadic behavioral measures of reaction time (pHolm = 0.012) and normalized peak velocity (pHolm = 0.012). Interestingly, patients had (i) reduced static and dynamic connectivity within the saccadic network and (ii) weaker associations between network connectivity and intra-individual saccadic variability of specifically normalized peak velocity. These results were particularly driven by regions within the cognitive saccade network. The classifier that integrated intra-individual saccadic variability and network connectivity achieved 95.62% accuracy in distinguishing patients from healthy controls, with dynamic connectivity contributing more strongly to classification performance than static connectivity. We demonstrate that elevated intra-individual variability of saccades, underpinned by altered functional connectivity within cognitive-oculomotor networks, is a prominent feature of early-stage Parkinsons disease. Emerging as a promising physiological marker of disease pathophysiology, intra-individual variability offers a powerful new framework to refine early diagnosis and improve the tracking of neurodegeneration before severe clinical milestones are reached.
Related
- Inter-Individual Variability in Cycling Biomechanics During Experimentally Induced Movement-Evoked Knee Pain
- Innate immune stress pathway activation underlies heterochromatin dysfunction pathology
- Dysregulation of miR-103a-3p/AGFG1 axis links endocytic trafficking dysfunction to ER stress in Parkinson's disease
- Validation and modification of a Chinese smell identification test (CSIT-12) in patients with Parkinson's disease and rapid eye movement sleep behavior disorder.
- Computational modelling of acetylcholine deficiency-mediated dysfunction in the basal ganglia-thalamocortical network related to Parkinson’s disease dementia
BibTeX
@misc{vasudevan2026saccadic,
title = {{Saccadic network dysfunction underlies eye-movement variability in early Parkinson's disease}},
author = {V. Vasudevan and S. Mournet and B. Beranger and C. P. Rogers and G. Mangone and I. ARNULF and J.-C. Corvol and M. Vidailhet and S. Lehericy and P. Pouget and N. Thirugnanasambandam and C. Gallea},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.25.754307},
url = {https://www.biorxiv.org/content/10.64898/2026.09.25.754307v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Saccadic network dysfunction underlies eye-movement variability in early Parkinson's disease
AU - V. Vasudevan
AU - S. Mournet
AU - B. Beranger
AU - C. P. Rogers
AU - G. Mangone
AU - I. ARNULF
AU - J.-C. Corvol
AU - M. Vidailhet
AU - S. Lehericy
AU - P. Pouget
AU - N. Thirugnanasambandam
AU - C. Gallea
PY - 2026
DA - 2026/09/25
PB - bioRxiv
DO - 10.64898/2026.09.25.754307
UR - https://www.biorxiv.org/content/10.64898/2026.09.25.754307v1
AB - Parkinsons disease is a neurodegenerative disorder characterized by altered subcortical-cortical connectivity, leading to motor and cognitive deficits. Saccadic eye movement abnormalities are among the earliest and most persistent markers of the disease, serving as sensitive indicators of disrupted oculomotor and cognitive control. While changes in average saccadic behavioral measures are widely reported in Parkinson's disease, intra-individual variability in saccadic performance and the underlying network dysfunction remain poorly understood. Here, we quantified intra-individual saccadic variability in early-stage Parkinsons disease and identified its functional correlates within the resting-state saccadic network. We analyzed data from 91 patients with early-stage Parkinsons disease and 46 healthy controls who underwent oculomotor testing and resting-state functional magnetic resonance imaging. First, at the behavioral level, we assessed intra-individual variability in saccadic performance within each group using the coefficient of variation of reaction time and normalized peak velocity across trials. Second, at the brain level, we assessed group differences in static and dynamic functional connectivity across 39 regions comprising the saccadic network. We examined the relationship between network connectivity and saccadic variability using partial least squares analysis. We also quantified network organization using graph-theory measures of global efficiency and clustering coefficient. Finally, we tested the relevance of intra-individual saccadic variability and network features in distinguishing patients from healthy controls using a multivariate Mahalanobis classifier. Our results showed that, compared to HC, patients had higher intra-individual variability in saccadic behavioral measures of reaction time (pHolm = 0.012) and normalized peak velocity (pHolm = 0.012). Interestingly, patients had (i) reduced static and dynamic connectivity within the saccadic network and (ii) weaker associations between network connectivity and intra-individual saccadic variability of specifically normalized peak velocity. These results were particularly driven by regions within the cognitive saccade network. The classifier that integrated intra-individual saccadic variability and network connectivity achieved 95.62% accuracy in distinguishing patients from healthy controls, with dynamic connectivity contributing more strongly to classification performance than static connectivity. We demonstrate that elevated intra-individual variability of saccades, underpinned by altered functional connectivity within cognitive-oculomotor networks, is a prominent feature of early-stage Parkinsons disease. Emerging as a promising physiological marker of disease pathophysiology, intra-individual variability offers a powerful new framework to refine early diagnosis and improve the tracking of neurodegeneration before severe clinical milestones are reached.
ER -