INTRODUCTION

A stressful event can significantly influence sleep-wake behavior in humans.^1,2 Furthermore, sleep disturbances can cause a significant, abnormal secretion of neurological metabolites in the brain.^1,3,4 In particular, the hippocampus is important for regulation of stress and adaptation, and is highly vulnerable to neuronal activity in cerebral neuro-metabolism.^5-7 Here, we quantitatively assessed cerebral glutamate changes in the hippocampal region in a rat model of stress-induced sleep perturbation. To detect, visualize, and evaluate glutamate changes, we conducted chemical exchange-dependent saturation transfer imaging of glutamate (GluCEST), and assessed the relationship between glutamate signal intensities and concentrations, quantified with proton magnetic resonance spectroscopy (¹H-MRS).

METHODS

Fourteen Sprague-Dawley rats were divided into two groups [stress-induced sleep perturbation group (SPG): n = 7 and control (CTRL) group: n = 7] to evaluate and compare the cerebral glutamate signal changes. SPG rats were placed in individual cages for 1 week without cage/bedding cleaning, and were then placed into a dirty cage (a clean cage for CTRL rats) previously occupied by another male rat for 1 week (cage exchange).⁸ About 5.5 hours after cage exchange, GluCEST imaging was performed and ¹H-MRS data were analyzed using a 7-T Bruker MRI scanner. CEST data were obtained using a fat-suppressed, Turbo-RARE sequence [TR/TE=4200/36.4 ms; single-slice; image-matrix=96×96; FOV=30×30 mm²; slice thickness=1.5 mm; and continuous wave saturation RF pulse (power/length=3.6µT/1s)]. Z-spectra with 25 frequency offsets (-6 to +6 ppm, at 0.5 ppm intervals) and the reference image (S₀ image) were obtained from single-slice MR images. To correct B₀ and B₁ inhomogeneities, water saturation shift referencing (WASSR)^9,10 data with 33 frequency offsets (-0.8 to +0.8 ppm, at 0.05-ppm intervals, using 0.05-µT RF saturation power and 200 ms saturation length) and a B₁ map using the double flip-angle method (30° and 60°) were acquired. Moreover, multi-parametric images were acquired as follows: T₁ maps [RAREVTR sequence; six TRs (600, 900, 1500, 2500, 4000, and 7000-ms); 12.2-ms TE]; T₂ maps [MSME sequence; fifteen TEs (10–150 ms with 10-ms increments); 3-s TR]; ADC maps [DTI-EPI-sequence; seven b-values (0, 166.7, 333.3, 500, 666.7, 833.3, and 1000 s/mm²); and TR/TE=3000/18.7 ms]; and CBF maps [FAIR sequence; 36.36-ms TE with multi-inversion times (35, 100–1400ms with 100ms increment, and 1600-ms)]. The ¹H-MRS data were acquired from a volume of interest region of the hippocampus, with a point-resolved spectroscopy sequence (TR/TE=5000/16.3-ms, spectral-width=5 kHz, average=256, data-points=2,048). The GluCEST map shows relative changes expressed as percentages: GluCEST(%)=100×(S_−ω–S_+ω)/S_−ω. Where S_−ω and S_+ω in the equation are the B₀ and B₁ corrected signals at –3 and +3 ppm from bulk water, respectively. To evaluate the signal values on the GluCEST map, four ROIs were drawn in the two hemispheres of the hippocampus and cortex. ¹H-MRS spectra were analyzed using the LCModel, with a simulated basis set containing 18 metabolites. Neurochemical signals from proton spectra were processed with water referencing for quantifying metabolic concentrations and eddy current correction. Raw spectra were fitted in a chemical shift range from 4.3 to 0.3 ppm. For statistical analyses, the Mann-Whitney U test was used to compare mean values between the two groups. Moreover, the relationship between GluCEST signal and glutamate concentrations (¹H-MRS) in individual animals was tested by Spearman’s correlation (r) and simple linear regression analysis (R²).

RESULTS AND DISCUSSION

The CEST signal revealed significant differences in the GluCEST contrast values between the two groups in both hippocampus (Fig.1a) and cortex (Fig.1b). GluCEST contrast levels significantly lower in SPG rats than in CTRL rats, in all regions [hippocampus (left: **P=0.002; right: *P=0.035); and cortex (left: **P=0.004, right: **P=0.003)]. Glutamate concentrations (¹H-MRS) revealed the same significant difference (*P=0.018; data not shown). Clusters of individual glutamate levels from GluCEST and ¹H-MRS data (Fig.2) were significantly correlated in both groups (F=5.445; R²=0.312; *P=0.038). Quantified multi-parametric values (T₁/T₂/ADC/CBF) in SPG and CTRL rats did not exhibit significant differences in any region (Fig.3a-h). Note that these multi-parametric values do not affect the formation of the GluCEST signal in this experiment, as the temperature and pH were maintained at a constant level for both groups. The signal changes can therefore be solely attributed to the difference in glutamate concentrations.¹⁰ Fig.4 shows quantitative MR multi-parametric and GluCEST maps, overlaid on the corresponding T₂-weighted image from representative SPG and CTRL rats. A visual inspection of the CEST signals in the hippocampus and cortex reveals remarkable contrasts between the two groups.

CONCLUSION

This study indicates that GluCEST can detect and visualize cerebral glutamate changes in the hippocampus and cortex of rats subjected to stress-induced sleep disturbance. Furthermore, GluCEST and ¹H-MRS data may yield valuable insights for interpreting alterations in cerebral glutamate signals in sleep disorders.

Acknowledgements

This study was supported by grants from the Basic Science Research Program through the National Research Foundation of Korea [NRF-2018R1C1B6004521; NRF-2017R1A6A3A03012461; and NRF-2018R1A2B2007694] and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute [HI14C1090], funded by the Ministry of Health & Welfare, Republic of Korea.