Pupillometry as a Window into Arousal Related Changes in fMRI
Poster No:
1668
Submission Type:
Abstract Submission
Authors:
Elif Can1, Berilsu ÖNER1, Kübra Eren1, Kadir Berat Yıldırım1, Pınar Özbay1
Institutions:
1Boğaziçi University, İstanbul, Turkiye
First Author:
Co-Author(s):
Introduction:
Resting-state fMRI (rs-fMRI) connectome is valued for its ability to provide insights into the brain's functional architecture, including alterations caused by disease. However, physiological signals, motion and arousal level can influence rs-fMRI functional connectivity (Handwerker, D. A. (2012)). Despite these challenges, growing evidence highlights the neurobehavioral relevance of dynamic functional connectivity (DFC), particularly its association with fluctuating arousal levels and diverse mental states (Liu, X. (2013). Pupil dilation differs between states of alertness and drowsiness. In alertness, pupil diameter remains relatively stable, whereas drowsiness causes significant fluctuations in pupil size (Lüdtke, H. (1998)). By monitoring pupil size during fMRI, we examined arousal-related changes in fMRI and investigated the role of systemic physiology in these changes.
Methods:
We used an open access dataset (Lee, K. (2022)) comprising 27 subjects. T1-weighted anatomical images were acquired with: TR = 2,400 ms, TE = 1.22 ms, flip angle = 8°, slice thickness = 1 mm, in-plane resolution = 1 × 1 mm, matrix size = 256 × 256, FOV = 256 mm. T2*-weighted BOLD acquisition was performed using EPI pulse sequence with TR = 1000 ms, TE = 30 ms, flip angle = 55°, multiband acceleration factor = 5. We preprocessed fMRI data using the following steps with AFNI (Cox, R. W. (1996)): despike, tshift, align, tlrc, volreg, blur, mask, scale, regress. Based on pupillary unrest index (Lüdtke, H. (1998)) values, k-means clustering was applied to obtain arousal groups; namely consisting of 16 high and 11 low arousal subjects (Figure 1). In order to understand the relationship between cardiac signals and fMRI signal changes, we extracted cardiac waveform from the fMRI data (Aslan, S. (2019)) using RapidTide software (Frederick, B. (2016–2024)). Predicted cardiac waveform was regressed out of fMRI using RETROICOR (Glover, G. H. (2000)), with ricor in AFNI. Pearson correlations and Fisher's z values were calculated between brain regions, parcelated using AAL atlas (Rolls, E. T. (2020)). Dynamic functional connectivity matrices (DFC) were estimated using a sliding-window approach with 30 TR window length using Dynamic Connectivity Software (Leonardi, N. (2015)).
Results:
For arousal groups, 2 preprocessing pipelines with/out cardiac signal removal are compared. Lagged correlations between ROIs and 4th ventricle signal from 2 voxels from bottom slices revealed that cardiac signal regression alters the dynamics between signal from cortical regions and ventricles, differing in effects in different arousal groups (Figure 1). For both arousal groups, static connectivities were altered following cardiac signal regression. Prominently, the connectivity difference between arousal groups was decreased. Without cardiac regression, low arousal states showed stronger Salience Network (SN) connectivity in state 1 and more negative dorsal attention network (DAN) connectivity in state 2. With cardiac regression, high arousal states exhibited stronger SN and cerebellar connectivity (Figure 2). Shorter mean dwell times in high arousal group, regardless of preprocessing, indicated more frequent state transitions.
·Figure 1
·Figure 2
Conclusions:
This study demonstrates the influence of cardiac regression on rs-fMRI connectivity in relation to arousal levels. Regressing out cardiac signals altered connectivity patterns and reduced differences between high and low arousal groups, suggesting that the coupling between arousal levels and autonomic activity is reflected onto the functional connectivity. Pupil, as a marker of autonomic nervous system activity, can help mark different arousal levels in fMRI studies. Our results demonstrate that incorporating arousal and cardiac regression refines the understanding of mental-state-dependent fMRI signal changes and highlights the potential influence of systemic physiology on dynamic connectivity analyses.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis 1
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Perception, Attention and Motor Behavior:
Sleep and Wakefulness
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
Autonomics
Cerebro Spinal Fluid (CSF)
FUNCTIONAL MRI
Other - Pupillometry
1|2Indicates the priority used for review
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2-Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers in Biomedical Research, 29(3), 162–173.
3-Frederick, B. (2016–2024). rapidtide [Computer software]. Available from https://github.com/bbfrederick/rapidtide
4-Glover, G. H. (2000). Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 44(1), 162-167.
5-Handwerker, D. A. (2012). Periodic changes in fMRI connectivity. NeuroImage, 63(3), 1712–1719.
6-Lee, K. (2022). Yale resting state fMRI/pupillometry: Arousal study [Dataset]. OpenNeuro.
7-Leonardi, N. (2015). On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage, 104, 430–436.
8-Liu, X. (2013). Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proceedings of the National Academy of Sciences, 110(11), 4392–4397.
9-Lüdtke, H. (1998). Mathematical procedures in data recording and processing of pupillary fatigue waves. Vision Research, 38(22), 2889–2896.
10-Rolls, E. T. (2020). Automated anatomical labelling atlas 3. NeuroImage, 206, 116189.