Increased Intra-Thalamic and Thalamo-Cortical Functional Connections during fMRI of human REM Sleep
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M1 & M2 (Mezzanine Level)
Poster No:
2085
Submission Type:
Abstract Submission
Authors:
Nils Yang1, Dante Picchioni1, Jacco de Zwart1, Peter van Gelderen1, Jeff Duyn1
Institutions:
1National Institutes of Health, Bethesda, MD
First Author:
Co-Author(s):
Introduction:
Rapid Eye Movement (REM) sleep, characterized by vivid dreaming and muscle atonia, raises fascinating questions about how such immersive experiences occur without external sensory input. An fMRI study has shown that REM-time-locked activations were found in multiple non-visual primary sensory cortices, along with the visual cortex, anterior cingulate cortex, and thalamus (Hong et al., 2009). These findings suggest that the thalamus is simultaneously functionally connected to multiple sensory cortices during REM sleep, a pattern not observed during wakefulness. For instance, Seitzman et al. (2019) identified five distinct thalamic subnetworks based on their unique functional connectivity to cortical networks. Given that pontine cholinergic neurons discharge in bursts just before each ponto-geniculo-occipital (PGO) wave-a process associated with eye movements-it is plausible that these neurons release acetylcholine to the thalamus (Datta & Siwek, 2002; Steriade & McCarley, 2013), which in turn activates the limbic system and sensory cortices. Taken together, we hypothesize that these five thalamic subnetworks function collectively as a sensory relay station during REM sleep, simultaneously connecting to sensory-related cortical networks (See Figure 1).
Methods:
We analyzed data from a state-of-the-art whole-night EEG-fMRI concurrent recording dataset collected in our lab (Moehlman et al., 2019). Twelve non-sleep-deprived participants completed two 8-hour whole-night experiments, reaching all sleep stages during the second night. The first night served as an adaptation night, so only data from the second night were included in the analysis. Dynamic functional connectivity was calculated using a sliding window approach (step size: 1 TR or 3 seconds, window length: 30 TR) for sleep episodes with a consistent sleep score lasting at least three epochs (90 seconds). The Seitzman 300-region brain atlas was used to define 14 cortical networks and five thalamic subnetworks (Seitzman et al., 2019).
Results:
Supporting our hypothesis, REM sleep engages all five thalamic subnetworks to simultaneously connect, i.e. functionally correlated, with multiple sensory-related cortical networks, including visual, motor, auditory, and action-planning networks-connections that are not observed during wakefulness (See Figure 2). For example, thalamus-default mode subnetwork (THALDMN) was not connected to auditory and motor networks during wakefulness but established connections with these networks during REM sleep. Additionally, we found significantly higher intra-thalamic functional connectivity during REM sleep compared to wakefulness across all 11 participants with high-quality fMRI data during REM (p < 0.001). Building on these findings, we further hypothesized that increased thalamus-related connectivity is specifically associated with phasic REM compared to tonic REM, as phasic REM is often linked to vivid dreaming. Using the Hidden Markov Model from our previous study to differentiate phasic and tonic REM states (Yang et al., 2024), we found that phasic REM indeed exhibited significantly greater intra-thalamic and thalamo-cortical connectivity, specifically between thalamic subnetworks and sensory-related networks, than tonic REM.
Conclusions:
These findings may reflect the neural mechanisms underlying vivid dreaming, suggesting that thalamic subnetworks simultaneously process and distribute internally generated sensory information to sensory-related cortical networks.
Novel Imaging Acquisition Methods:
BOLD fMRI 2
EEG
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
Electroencephaolography (EEG)
FUNCTIONAL MRI
Sleep
Thalamus
1|2Indicates the priority used for review
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Hong, C. C.-H., Harris, J. C., Pearlson, G. D., Kim, J.-S., Calhoun, V. D., Fallon, J. H., Golay, X., Gillen, J. S., Simmonds, D. J., van Zijl, P. C. M., Zee, D. S., & Pekar, J. J. (2009). fMRI evidence for multisensory recruitment associated with rapid eye movements during sleep. Human Brain Mapping, 30(5), 1705–1722. https://doi.org/10.1002/hbm.20635
Moehlman, T. M., de Zwart, J. A., Chappel-Farley, M. G., Liu, X., McClain, I. B., Chang, C., Mandelkow, H., Özbay, P. S., Johnson, N. L., Bieber, R. E., Fernandez, K. A., King, K. A., Zalewski, C. K., Brewer, C. C., van Gelderen, P., Duyn, J. H., & Picchioni, D. (2019). All-night functional magnetic resonance imaging sleep studies. Journal of Neuroscience Methods, 316, 83–98. https://doi.org/10.1016/j.jneumeth.2018.09.019
Seitzman, B., Gratton, C., Marek, S., Raut, R., Dosenbach, N., Schlaggar, B., Petersen, S., & Greene, D. (2019). A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum. NeuroImage, 206, 116290. https://doi.org/10.1016/j.neuroimage.2019.116290
Steriade, M. M., & McCarley, R. W. (2013). Brainstem Control of Wakefulness and Sleep. Springer Science & Business Media.
Yang, F. N., Picchioni, D., de Zwart, J. A., Wang, Y., van Gelderen, P., & Duyn, J. H. (2024). Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI. eLife, 13, RP98739. https://doi.org/10.7554/eLife.98739