Exploring Pupil-Brain Dynamics: Impact of Grouping Methods in Resting-State fMR
Poster No:
2118
Submission Type:
Abstract Submission
Authors:
Berilsu ÖNER1, Elif Can2, Pınar Özbay3
Institutions:
1Boğaziçi University, Istanbul,Turkey, 2Boğaziçi University, İstanbul, Turkey, 3Boğaziçi University, Istanbul, Turkey
First Author:
Co-Author(s):
Introduction:
Pupil dynamics provide a valuable physiological marker for assessing cognitive states, reflecting changes in alertness and cognitive load. Previous studies have linked pupil dilation to neural activity in regions involved in arousal and cognitive control [1,4,7]. However, the temporal alignment between pupil size fluctuations and resting-state brain activity remains underexplored, particularly across varying alertness levels. This study examines how different methods of grouping subjects by alertness levels influence correlations between pupil size and resting-state brain activity. Using the "Yale Resting-State fMRI/Pupillometry: Arousal Study" dataset [3] of 27 subjects, we evaluate pupil-brain correlations across four grouping methods and explore network-specific patterns for subjects consistently classified within the same alertness group and also focused on voxelwise mapping of the seventh visual area (V7) and lateral intraparietal ventral area (LIPv).
Methods:
We utilized the "Yale Resting-State fMRI/Pupillometry: Arousal Study" dataset, comprising 27 subjects. Functional T2*-weighted BOLD images were acquired using a multiband EPI sequence (TR = 1,000 ms, TE = 30 ms, flip angle = 55°, multiband factor = 5, voxel size = 2 mm isotropic).Subjects were categorized into "High" and "Low" alertness groups using four grouping methods: weighted scoring, quantile thresholds, k-means clustering, and smoothed moving averages. The first derivative of pupil size was calculated to capture dynamic fluctuations. Group-averaged voxelwise correlations between BOLD signal and pupil size were computed for time-shifted lags and normalized using Fisher's z-transformation.Voxel mapping focused on V7 and LIPv due to their observed negative correlation in standard voxelwise maps and their association with attention. Additionally, derivative correlations were evaluated to examine dynamic changes in neural activity linked to pupil size fluctuations. Parcellation into 116 regions was performed using the AAL atlas, and network-specific patterns were analyzed.
Results:
Across all methods, the correlation maps indicated moderate relationships between resting brain activity and overall pupil size. Among the four grouping methods, Method3 revealed the strongest correlation patterns between pupil size and resting-state brain activity. In high-alertness subjects, correlations using Method3 typically showed a unique trajectory, with a negative correlation at negative lags. These findings suggest that overall pupil size, especially within auditory, sensory, and visual networks, might serve as a potential marker for differences in brain activity.Further analysis highlighted V7 and LIPv as regions of interest due to their distinct negative correlations with pupil size in standard voxel maps and their established roles in attention processes. Method3 and Method4 showed particularly clear distinctions between alertness groups in these regions. While standard correlation maps revealed consistent negative trends, derivative correlations exhibited slight variations in dynamics, indicating differing neural responses to sustained versus dynamic pupil changes.These findings align with literature suggesting that neural activity in these regions correlates with spontaneous pupil size changes[6].Conversely, the low-alertness group displayed a more stable, weaker pattern with a slight negative trend, consistent across all lags.
·Cross-correlation Graphs
·Method3 and Method4 Voxelwise Maps
Conclusions:
This study provides insights into the relationship between pupil dynamics and resting-state brain activity, illustrating that both the grouping method and pupil metric selection significantly shape the results.Future work should further explore the statistical significance of these correlations and investigate individual differences in pupil-brain connectivity patterns to deepen our understanding of the physiological underpinnings of alertness states.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Perception, Attention and Motor Behavior:
Sleep and Wakefulness
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
Data analysis
FUNCTIONAL MRI
Statistical Methods
1|2Indicates the priority used for review
Abstract Information
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2- Ekman, M. (2017). Time-compressed preplay of anticipated events in human primary visual cortex. Nature Communications, 8(1), 15276. https://doi.org/10.1038/ncomms15276
3- Glasser, M. F. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. https://doi.org/10.1038/nature18933
4- Joshi, S. (2016). Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron, 89(1), 221–234. https://doi.org/10.1016/j.neuron.2015.11.028
5- Mathôt, S. (2018). Pupillometry: Psychology, physiology, and function. Journal of Cognition, 1(1), 16. https://doi.org/10.5334/joc.18
6- Murphy, P. R. (2014). Pupil diameter covaries with BOLD activity in human locus coeruleus. Human Brain Mapping, 35(8), 4140–4154. https://doi.org/10.1002/hbm.22466
7- Murphy, P. R.(2014). Pupil-linked arousal determines variability in perceptual decision making. PLOS Computational Biology, 10(9), e1003854. https://doi.org/10.1371/journal.pcbi.1003854
8- Siegle, G. J. (2008). Blink before and after you think: Blinks occur prior to and following cognitive load indexed by pupil dilation. Psychophysiology, 45(5), 679–687. https://doi.org/10.1111/j.1469-8986.2008.00681.x