Introduction

Blood oxygenation level-dependent (BOLD) fMRI provides information about hemodynamic response, which is related to the oxygen demand of the neurons and is predominantly driven by the balance between excitation and inhibition in microcircuits¹. Previous studies have shown that about 85% of the energy consumed by the brain is used to support glutamatergic signalling^2,3. Consequently, generation of the BOLD signal is primarily influenced by the regional excitation⁴. Research using single voxel magnetic resonance spectroscopy (MRS) and fMRI has shown links between regional neurotransmitter levels, BOLD signals, and functional connectivity^5–8. Previous research has primarily explored these associations either regionally or at a whole-brain level, encompassing the entire cortical ribbon. However, since the distribution of neurotransmitter receptors can vary between different cortical regions and layers^9,10, studying the association of BOLD signal at different cortical layers with absolute regional neurotransmitter concentrations will improve understanding of the neurochemical basis of brain activity and potentially uncover layer-specific functional relationships. Thus, this exploratory study aims to examine the association between the cortical depth-dependent BOLD signal and absolute in-vivo neurometabolite concentrations (glutamate, glutamine, GABA, excitation-inhibition ratio glutamate/GABA, glutathione, and lactate) in the the posterior cingulate cortex (PCC) during resting state (RS). The PCC is a central hub within the default mode network, exhibiting increased metabolic activity¹¹, receptor binding availabilities¹² and structural connections¹³ to various brain regions. Thus, the PCC is considered to be the region-of-interest in this study.

Methods

Data Acquisition:
The MR data were acquired from nine healthy subjects (six males, age: 34±14) using a 7T MAGNETOM Terra scanner (Siemens Healthineers). The structural MRI, high-resolution fMRI and MRS data were acquired in the same session.
Structural MRI: MP2RAGE sequence - TR/TE 4500ms/1.99ms, voxel-size 0.75 mm³ isotropic resolution.
RS-fMRI: GE-EPIK sequence with TR-external phase-correction^14,15 (TR/TE = 3500/22ms, FA = 85°, partial Fourier = 5/8, 3-fold in-plane/3-fold inter-plane (multi-band) acceleration, voxel-size 0.63×0.63×0.63 mm³).
Single-voxel MRS: STEAM sequence^16–18 with ultra-short echo-time: TE = 4.6ms; TM = 28 ms; TR = 8200ms; 64 averages; voxel-size 20×20×20mm³. B0 shimming was performed using FASTESTMAP¹⁸. The sequence included water suppression (VAPOR) and outer-volume suppression modules¹⁹.
MRS and fMRI Data Analysis:
MR-spectra were pre-processed (motion, frequency and phase drift corrections) and fitted using the FID-A package²⁰ and LCModel (6.3-0I),²¹ respectively. The neurometabolite concentrations with a Cramer-Rao lower bound above 20% were excluded and absolute concentration was calculated²².
fMRI pre-processing included slice timing correction, realignment, temporal filtering, and regression (motion, CSF/WM, physiological and vein signal biases)²³ using SPM12²⁴, FSL²⁵ and AFNI²⁶. Whole-brain voxel-level RS-fMRI-metrics, such as amplitude of low-frequency fluctuations (ALFF)²⁷, eigenvector centrality mapping (ECM)²⁸, and regional homogeneity (ReHo)²⁹ were calculated and projected to six cortical depth-dependent surfaces and normalised using Z-score transformation. The mean RS-fMRI metrics for each equidistant cortical depth were extracted from the PCC volume included in single-voxel MRS.
Statistical Analysis:
Spearman's rank correlation was performed to find the associations. The family-wise error rate due to multiple comparisons was controlled for using a permutation approach³⁰.

Results

The concentration of glutamate was found to have a significant (p <0.05) positive association with the fMRI ECM-metric at depths 2 (r_s = 0.71), 3 (r_s = 0.78) and 4 (r_s = 0.83) of PCC (Fig. 3), with depth territories numbered from the GM-CSF boundary to the WM-GM boundary. Lactate concentration was found to have a significant (p <0.05) negative association with the fMRI ALFF-metric in superficial depths, specifically depths 1 (r_s = -0.77) and 3 (r_s = -0.72) of PCC (Fig. 3).

Discussions and Conclusions

The positive correlations between glutamate concentration and the fMRI ECM-metric indicate a strong relationship between the excitatory neurotransmitter glutamate and highly functionally connected voxels in the whole brain. This finding suggests that higher glutamate levels are associated with increased long-range neural excitability and synchronisation in the more superficial layers, as reflected by the enhanced fMRI ECM-metric.
PCC is one of the regions which show elevated aerobic glycolysis in the brain³¹. The negative correlation observed between lactate and the fMRI ALFF-metric (representing the intensity of BOLD signal) in the superficial layers of the PCC during RS, coupled with the consistent levels of lactate in the PCC during tasks³², suggests that there may be distinct metabolic mechanisms at play that support neurotransmission within the superficial cortical layers at rest. This could reflect a potential metabolic or functional difference in the layers of the PCC, highlighting the layer-specific complexities of brain function.

Limitations

The study focused on a relatively small sample size, which limits generalizability. Extending the method to a larger number of subjects and to other disease conditions may help in finding neurobiological mechanisms behind BOLD signals and functional connectivity.

Acknowledgements

*Authors Ravichandran Rajkumar and Patricia Pais-Roldán contributed equally to this work. N. Jon Shah and Irene Neuner shared equally senior authorship. The authors would like to thank Petra Engels, Elke Bechholz, and Anita Köth for their technical assistance during the scans, and Claire Rick for proofreading the abstract. We would like to acknowledge E.J. Auerbach and M. Marjanska (Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, USA) for the development of the STEAM sequence for the Siemens platform, which was provided by the University of Minnesota under a C2P agreement.

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