Back to Pupillometry: How Cortical Network State Fluctuations Tracked by Pupil Dynamics Could Explain Neural Signal Variability in Human Cognitive Neuroscience

Schwalm, Miriam; Jubal, Eduardo Rosales

doi:10.1523/eneuro.0293-16.2017

Cited by 27 publications

(33 citation statements)

References 75 publications

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“…In conclusion, we used steady-state fMRI to study the relation between pupil dynamics and brain networks. Our results advance a potential important link between changes in pupil and cortical state (Schwalm and Rosales Jubal, 2017). It would be interesting to study how the pupil-related network evolves during altered states of consciousness.…”

Section: Resultssupporting

confidence: 79%

Brain Networks Underlying Eye’s Pupil Dynamics

et al. 2019

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Phasic changes in eye’s pupil diameter have been repeatedly observed during cognitive, emotional and behavioral activity in mammals. Although pupil diameter is known to be associated with noradrenergic firing in the pontine Locus Coeruleus (LC), thus far the causal chain coupling spontaneous pupil dynamics to specific cortical brain networks remains unknown. In the present study, we acquired steady-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data combined with eye-tracking pupillometry from fifteen healthy subjects that were trained to maintain a constant attentional load. Regression analysis revealed widespread visual and sensorimotor BOLD-fMRI deactivations correlated with pupil diameter. Furthermore, we found BOLD-fMRI activations correlated with pupil diameter change rate within a set of brain regions known to be implicated in selective attention, salience, error-detection and decision-making. These regions included LC, thalamus, posterior cingulate cortex (PCC), dorsal anterior cingulate and paracingulate cortex (dACC/PaCC), orbitofrontal cortex (OFC), and right anterior insular cortex (rAIC). Granger-causality analysis performed on these regions yielded a complex pattern of interdependence, wherein LC and pupil dynamics were far apart in the network and separated by several cortical stages. Functional connectivity (FC) analysis revealed the ubiquitous presence of the superior frontal gyrus (SFG) in the networks identified by the brain regions correlated to the pupil diameter change rate. No significant correlations were observed between pupil dynamics, regional activation and behavioral performance. Based on the involved brain regions, we speculate that pupil dynamics reflects brain processing implicated in changes between self- and environment-directed awareness.

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Section: Resultssupporting

confidence: 79%

Brain Networks Underlying Eye’s Pupil Dynamics

et al. 2019

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“…a dynamic visual attention task) significantly increased integration with arousal, which may hint that these effects may be in part related to task-related activity. Taken together, our results support the view that pupil size is a marker of ongoing network states (Schwalm & Jubal, 2017), and point towards arousal as one mechanism affecting brain coordination between specific areas in tasks involving different cognitive states.…”

Section: Discussionsupporting

confidence: 86%

Pupil-based states of brain integration across cognitive states

Mäki-Marttunen

2020

Preprint

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Arousal is a potent mechanism that provides the brain with functional flexibility and adaptability to external conditions. Within the wake state, arousal levels driven by activity in the neuromodulatory systems are related to specific signatures of neural activation and brain synchrony. However, direct evidence is still lacking on the varying effects of arousal on macroscopic brain characteristics and across a variety of cognitive states in humans. Using a concurrent fMRI-pupillometry approach, we used pupil size as a proxy for arousal and obtained patterns of brain integration associated with increasing arousal levels. We carried out this analysis on resting-state data and data from two attentional tasks implicating different cognitive processes. We found that an increasing level of arousal was related to a non-linear pattern of brain integration, with increasing brain integration from intermediate to larger arousal levels. This effect was prominent in the salience network in all tasks, while other regions showed task-specificity. Furthermore, task performance was also related to arousal level, with accuracy being highest at intermediate levels of arousal across tasks. Taken together, our study provides evidence in humans for pupil size as an index of brain network state, and supports the role of arousal as a switch that drives brain coordination in specific brain regions according to the cognitive state.

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“…Electrophysiological studies have revealed that pupil constriction is associated to slow and synchronous cortical responses and inattentive behavior. Conversely, the cortical activation during task engagement or locomotion shows a persistent desynchronized neuronal activity associated to the dilatation of the pupil (Reimer et al, 2014, 2016; McGinley et al, 2015b; Schwalm and Jubal, 2017). Pupil size fluctuations and cortical state variations are modulated by the central noradrenergic and cholinergic pathways.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, monitoring pupil dynamics could be a reliable proxy of the changes in cortical states (Reimer et al, 2014, 2016; McGinley et al, 2015a,b). More specifically, the release of acetylcholine (Ach) from the basal forebrain and noradrenaline (NA) from LC have been shown to drive both the state of cortical connectivity and the pattern of the pupil size oscillations also in resting conditions (Reimer et al, 2016; Schwalm and Jubal, 2017). At the peripheral level, both Ach and NA are neurotransmitters of the ANS (parasympathetic and sympathetic systems, respectively) also controlling the pupil diameter.…”

Section: Discussionmentioning

confidence: 99%

“…Other forebrain nuclei and cortical areas connected to LC are activated during spontaneous and event driven pupil size changes (Wang and Munoz, 2015; Joshi et al, 2016) suggesting a circuit for pupil response, linked to arousal, attention and perception systems (Jones, 2004; Naber et al, 2013; Wang and Munoz, 2015; Fazlali et al, 2016; Reimer et al, 2016; Larsen and Waters, 2018). Overall, these studies outline a new role for the pupil size monitoring as a reliable and non-invasive peripheral marker of rapid brain state changes (Hartmann and Fischer, 2014; Schwalm and Jubal, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A Cross-Recurrence Analysis of the Pupil Size Fluctuations in Steady Scotopic Conditions

et al. 2019

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Pupil size fluctuations during stationary scotopic conditions may convey information about the cortical state activity at rest. An important link between neuronal network state modulation and pupil fluctuations is the cholinergic and noradrenergic neuromodulatory tone, which is active at cortical level and in the peripheral terminals of the autonomic nervous system (ANS). This work aimed at studying the low- and high-frequency coupled oscillators in the autonomic spectrum (0–0.45 Hz) which, reportedly, drive the spontaneous pupillary fluctuations. To assess the interaction between the oscillators, we focused on the patterns of their trajectories in the phase-space. Firstly, the frequency spectrum of the pupil signal was determined by empirical mode decomposition. Secondly, cross-recurrence quantification analysis was used to unfold the non-linear dynamics. The global and local patterns of recurrence of the trajectories were estimated by two parameters: determinism and entropy. An elliptic region in the entropy-determinism plane (95% prediction area) yielded health-related values of entropy and determinism. We hypothesize that the data points inside the ellipse would likely represent balanced activity in the ANS. Interestingly, the Epworth Sleepiness Scale scores scaled up along with the entropy and determinism parameters. Although other non-linear methods like Short Time Fourier Transform and wavelets are usually applied for analyzing the pupillary oscillations, they rely on strong assumptions like the stationarity of the signal or the a priori knowledge of the shape of the single basis wave. Instead, the cross-recurrence analysis of the non-linear dynamics of the pupil size oscillations is an adaptable diagnostic tool for identifying the different weight of the autonomic nervous system components in the modulation of pupil size changes at rest in non-luminance conditions.

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Back to Pupillometry: How Cortical Network State Fluctuations Tracked by Pupil Dynamics Could Explain Neural Signal Variability in Human Cognitive Neuroscience

Abstract: Visual Abstract

Cited by 27 publications

References 75 publications

Brain Networks Underlying Eye’s Pupil Dynamics

Brain Networks Underlying Eye’s Pupil Dynamics

Pupil-based states of brain integration across cognitive states

A Cross-Recurrence Analysis of the Pupil Size Fluctuations in Steady Scotopic Conditions

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