Decreased glutamate levels in both the PCC and thalamus after nocturnal sleep: A 7T MRS study
Poster No:
1956
Submission Type:
Abstract Submission
Authors:
Yushan Liu1, Lingyan Ma1, Qihong Zou1, Xi Chen2, Jia-hong Gao1
Institutions:
1Center for MRl Research, Peking University, Beijing, China, 2McLean Hospital/Harvard Medical School, Belmont, USA
First Author:
Co-Author(s):
Introduction:
Diurnal fluctuations in brain metabolites provide critical insights into the mechanisms underlying sleep and circadian rhythms. Previous studies using 3T magnetic resonance spectroscopy (MRS) have reported variations in cortical metabolite concentrations across different times of the day (Al-Iedani, 2018; Volk, 2018; Ye, 2024). 7T MRS facilitates higher signal-to-noise ratio and enables reliable quantification of metabolites in the subcortical nuclei (Younis, 2020). Here, we present preliminary results from a 7T MRS study exploring changes in neuronal metabolites in the posterior cingulate cortex (PCC), a key default-mode network node, and the thalamus, critical for sleep-wake regulation, after nocturnal sleep.
Methods:
Six healthy participants (3 males and 3 females, aged 18-25 years) with regular sleep-wake routines and good sleep quality participated in this study. Two MRI sessions were conducted: the first session was performed 90 minutes before nocturnal sleep (mean scanning onset time = 22:29 ± 9 minutes), and the second session 30 minutes after sleep (mean scanning onset time = 08:06 ± 11 minutes).
All MRI scans were performed using a 7T MRI system (MAGNETOM Terra, Siemens Healthineers, Erlangen, Germany) equipped with a Nova 8Tx/32Rx head coil. Each session began with a T1-weighted image for voxel placement. As shown in Fig. 1, voxels were positioned in the PCC (20 × 20 × 20 mm³) and thalamus (36 × 21 × 14 mm³) using AutoVOI (Park, 2018). Each session lasted approximately 1 hour.
Metabolite spectra were acquired using a semi-LASER sequence (TE = 26 ms, TR = 5 s, Deelchand, 2021), with 64 averages for the PCC and 128 averages for the thalamus. Water reference spectra (16 averages) were also acquired. Spectra were processed using MRspa and analyzed with LCModel (Provencher, 2001), with the fitting parameter DKNTMN set to 0.5. T1-weighted images were co-registered and segmented using Osprey (Oeltzschner, 2020) for partial volume correction. Water-scaled concentrations were reported.
Paired t-tests were conducted to evaluate differences in metabolite levels after nocturnal sleep compared to those before sleep.
All MRI scans were performed using a 7T MRI system (MAGNETOM Terra, Siemens Healthineers, Erlangen, Germany) equipped with a Nova 8Tx/32Rx head coil. Each session began with a T1-weighted image for voxel placement. As shown in Fig. 1, voxels were positioned in the PCC (20 × 20 × 20 mm³) and thalamus (36 × 21 × 14 mm³) using AutoVOI (Park, 2018). Each session lasted approximately 1 hour.
Metabolite spectra were acquired using a semi-LASER sequence (TE = 26 ms, TR = 5 s, Deelchand, 2021), with 64 averages for the PCC and 128 averages for the thalamus. Water reference spectra (16 averages) were also acquired. Spectra were processed using MRspa and analyzed with LCModel (Provencher, 2001), with the fitting parameter DKNTMN set to 0.5. T1-weighted images were co-registered and segmented using Osprey (Oeltzschner, 2020) for partial volume correction. Water-scaled concentrations were reported.
Paired t-tests were conducted to evaluate differences in metabolite levels after nocturnal sleep compared to those before sleep.
Results:
We reported γ-aminobutyric acid (GABA) and glutamate (Glu) as key inhibitory and excitatory neurotransmitters. N-ace-tylaspartate (NAA) was also reported as a negative control.
As shown in Fig. 2, Glu levels in the PCC were significantly lower after sleep (-5.79 ± 0.80%, p = 7.44 × 10-4) compared to before sleep. In contrast, no significant differences were observed in GABA (p = 9.02 × 10-1) or NAA levels (p = 7.18 × 10-1).
In the thalamus, Glu levels (-6.41 ± 1.14%, p = 3.52 × 10-3) were significantly reduced after sleep. Meanwhile, GABA (p = 6.94 × 10-1) and NAA (p = 2.14 × 10-1) levels did not show significant changes.
Additionally, we found that GABA concentrations in the thalamus were significantly higher than those in the PCC (p = 7.12 × 10-5), indicating that 7T can detect differences in GABA levels to some extent.
The signal quality showed no significant differences before and after sleep. In the PCC, the mean SNR was 68.3 ± 1.4, and the mean FWHM was 7.31 ± 0.22 Hz. The CRLB values for GABA were within 20%, and those for Glu and NAA did not exceed 2%. In the thalamus, the mean SNR was 58.0 ± 1.8, and the mean FWHM was 11.61 ± 0.87 Hz. The CRLB values for GABA were within 10%, and those for Glu and NAA did not exceed 2%.
As shown in Fig. 2, Glu levels in the PCC were significantly lower after sleep (-5.79 ± 0.80%, p = 7.44 × 10-4) compared to before sleep. In contrast, no significant differences were observed in GABA (p = 9.02 × 10-1) or NAA levels (p = 7.18 × 10-1).
In the thalamus, Glu levels (-6.41 ± 1.14%, p = 3.52 × 10-3) were significantly reduced after sleep. Meanwhile, GABA (p = 6.94 × 10-1) and NAA (p = 2.14 × 10-1) levels did not show significant changes.
Additionally, we found that GABA concentrations in the thalamus were significantly higher than those in the PCC (p = 7.12 × 10-5), indicating that 7T can detect differences in GABA levels to some extent.
The signal quality showed no significant differences before and after sleep. In the PCC, the mean SNR was 68.3 ± 1.4, and the mean FWHM was 7.31 ± 0.22 Hz. The CRLB values for GABA were within 20%, and those for Glu and NAA did not exceed 2%. In the thalamus, the mean SNR was 58.0 ± 1.8, and the mean FWHM was 11.61 ± 0.87 Hz. The CRLB values for GABA were within 10%, and those for Glu and NAA did not exceed 2%.
Conclusions:
Our findings show significant decreases in Glu levels after sleep in the PCC, aligning with prior literature (Volk, 2018), despite using a much smaller sample size. This highlights the superior sensitivity, accuracy, and statistical power of 7T MRS. Additionally, 7T MRS enables precise measurements in subcortical regions, such as the thalamus, where Glu levels also decreased. As Glu is an excitatory neurotransmitter, our results may reflect reduced neuronal activity during sleep. These findings underscore the importance of considering the time-of-day effect in future studies.
Novel Imaging Acquisition Methods:
MR Spectroscopy 1
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 2
Keywords:
Cortex
GABA
Glutamate
HIGH FIELD MR
Magnetic Resonance Spectroscopy (MRS)
MRI
Neurotransmitter
Sleep
Thalamus
1|2Indicates the priority used for review
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Provide references using APA citation style.
Deelchand, D. K. (2021). Across‐vendor standardization of semi‐LASER for single‐voxel MRS at 3T. NMR in Biomedicine, 34(5), e4218.
Oeltzschner, G. (2020). Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data. Journal of neuroscience methods, 343, 108827.
Park, Y. W. (2018). AutoVOI: real‐time automatic prescription of volume‐of‐interest for single voxel spectroscopy. Magnetic resonance in medicine, 80(5), 1787-1798.
Provencher, S. W. (2001). Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo, 14(4), 260-264.
Volk, C. (2018). Diurnal changes in glutamate+ glutamine levels of healthy young adults assessed by proton magnetic resonance spectroscopy. Human brain mapping, 39(10), 3984-3992.
Ye, Y. (2024). MR Spectroscopy Assessment of Daily Variations of GABA Levels within the Parietal Lobe and Anterior Cingulate Gyrus Regions of Healthy Young Adults. Journal of Magnetic Resonance Imaging.
Younis, S. (2020). Feasibility of glutamate and GABA detection in pons and thalamus at 3T and 7T by proton magnetic resonance spectroscopy. Frontiers in Neuroscience, 14, 559314.