Decreased glutamate in the PCC during nocturnal sleep: A simultaneous EEG and 7T MRS study
Poster No:
1949
Submission Type:
Abstract Submission
Authors:
Lingyan Ma1, Yushan Liu1, Xi Chen2, Qihong Zou1, Jia-hong Gao1
Institutions:
1Center for MRl Research, Peking University, Beijing, China, 2McLean Hospital/Harvard Medical School, Belmont, MA
First Author:
Co-Author(s):
Introduction:
Sleep is an important and complex physiological process, with brain networks reconfigured in different sleep stages. Specifically, the default mode network (DMN) shows reduced functional connectivity after the onset of sleep. However, the underlying neurometabolic mechanism remains unclear. Proton magnetic resonance spectroscopy (MRS) is a non-invasive, in vivo technique that quantifies neurometabolite levels in the brain. Electroencephalography (EEG) tracks brain electrical activity and classifies different sleep stages. By combining EEG with MRS, it becomes possible to dynamically monitor changes in metabolic levels during different sleep stages (Tamaki et al., 2020, 2021). Here, we present preliminary results of a simultaneous EEG and 7T MRS study exploring metabolite changes in the posterior cingulate cortex (PCC, a key node of the DMN) during sleep.
Methods:
We recruited 5 healthy adults (2 males, 3 females, aged 18-35 years) and acquired data during their regular nocturnal sleep. Participants wore an MRI-compatible EEG cap (Greentek, Wuhan, China) and were scanned in a 7T MRI system (MAGNETOM Terra, Siemens Healthineers, Erlangen, Germany) with a Nova 8Tx/32Rx head coil. Voxels (20 mm × 20 mm × 20 mm) were placed in the PCC (Fig. 1a). The simultaneous EEG-MRS acquisition commenced with a 5-minute eyes-open wakefulness (W) session, then followed by repetitive MRS acquisition sessions during sleep (20-minute each, with 6-16 sessions) until they could not sleep anymore.
The metabolite spectra were acquired using a semi-LASER sequence (TE = 26 ms, TR = 5 s), processed using MRspa and analyzed using LCModel (Fig. 1b). We fit the concentrations of 25 metabolites, and focused on γ- Aminobutyric Acid (GABA), glutamate (Glu) and glutamine (Gln). EEG data were preprocessed with Brain Products Analyzer 2.1, including gradient artifact removal (Allen et al., 2000), ballistocardiogram removal (Allen et al., 1998), down-sampling to 500Hz, re-referencing, and band-pass filtering to 0.3 - 20Hz. Sleep stage scoring was accomplished leveraging the preprocessed EEG data, including non-rapid eye movement (NREM) sleep stages 1, 2 and 3 (N1, N2 and N3), as well as rapid eye movement (REM). The MRS data were segmented according to sleep stages, resulting in a total of 136 data segments (24 in N1, 35 in N2, 46 in N3, 4 in REM, and 27 in W), with each segment lasting 5 minutes in a same sleep stage. To evaluate the main effect of sleep stages, a linear mixed-effects model was conducted.
The metabolite spectra were acquired using a semi-LASER sequence (TE = 26 ms, TR = 5 s), processed using MRspa and analyzed using LCModel (Fig. 1b). We fit the concentrations of 25 metabolites, and focused on γ- Aminobutyric Acid (GABA), glutamate (Glu) and glutamine (Gln). EEG data were preprocessed with Brain Products Analyzer 2.1, including gradient artifact removal (Allen et al., 2000), ballistocardiogram removal (Allen et al., 1998), down-sampling to 500Hz, re-referencing, and band-pass filtering to 0.3 - 20Hz. Sleep stage scoring was accomplished leveraging the preprocessed EEG data, including non-rapid eye movement (NREM) sleep stages 1, 2 and 3 (N1, N2 and N3), as well as rapid eye movement (REM). The MRS data were segmented according to sleep stages, resulting in a total of 136 data segments (24 in N1, 35 in N2, 46 in N3, 4 in REM, and 27 in W), with each segment lasting 5 minutes in a same sleep stage. To evaluate the main effect of sleep stages, a linear mixed-effects model was conducted.
Results:
Consistent with previous studies (Near et al., 2021), the concentration of total creatine (tCr) remained relatively stable (F = 0.7603, p = 0.5538) among the five stages. Thereafter we reported metabolite levels using the tCr signal as the reference, presented as their ratio to tCr. A significant main effect of stage on Glu/tCr was observed (F = 3.4524, p = 0.0108). Post-hoc tests revealed a trend of decrease in Glu/tCr from wakefulness to NREM sleep, with significant reductions observed between W and N2 (p = 0.0104) as well as between W and N3 (p = 0.0172) (Fig. 2). The GABA/tCr shows a decreasing trend from wakefulness to NREM sleep, but the main effect is not significant (F = 0.8903, p = 0.4722). In contrast, the Gln/tCr showed a trend of increase from W to NREM sleep (F = 1.0770, p = 0.3734).
Conclusions:
This is the first study that demonstrated the feasibility of simultaneous EEG-MRS acquisition at 7T during nocturnal sleep. It uncovers the dynamics in neural metabolites related to physiological stages. Our preliminary results indicate a decrease in Glu levels during NREM sleep compared to wakefulness. This may provide the metabolic basis for the decreased information processing, reduced functional connectivity, and lower levels of consciousness during NREM sleep. We will expand the sample size to discern if there are possible changes in other metabolites, and to further explore the dynamics during REM sleep, which included only one participant's data at the current stage.
Brain Stimulation:
Non-Invasive Stimulation Methods Other
Novel Imaging Acquisition Methods:
MR Spectroscopy 1
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 2
Keywords:
Cortex
Electroencephaolography (EEG)
GABA
Glutamate
HIGH FIELD MR
Magnetic Resonance Spectroscopy (MRS)
MRI
Neurotransmitter
Sleep
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Allen, P. J. (1998). Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction. NeuroImage, 8(3), 229–239. https://doi.org/10.1006/nimg.1998.0361
Near, J. (2021). Preprocessing, analysis and quantification in single‐voxel magnetic resonance spectroscopy: Experts’ consensus recommendations. NMR in Biomedicine, 34(5), e4257. https://doi.org/10.1002/nbm.4257
Tamaki, M. (2020). Complementary contributions of non-REM and REM sleep to visual learning. Nature Neuroscience, 23(9), 1150–1156. https://doi.org/10.1038/s41593-020-0666-y
Tamaki, M. (2021). Coregistration of magnetic resonance spectroscopy and polysomnography for sleep analysis in human subjects. STAR Protocols, 2(4), 100974. https://doi.org/10.1016/j.xpro.2021.100974