2.2. MRS Acquisition and Analysis

A Siemens MAGNETOM 7T head-only MRI scanner (Siemens, Erlangen, Germany) was used for all MRS acquisition along with a site-built head coil (8-channel transmit, 32-channel receive coil array) at the Centre for Functional and Metabolic Mapping of Western University (London, ON, Canada). A two-dimensional sagittal anatomical image (37 slices, TR = 8000 ms, TE = 70 ms, flip-angle (α) = 120°, thickness = 3.5 mm, field of view = 240 × 191 mm²) was used as reference to prescribe a 2.0 × 2.0 × 2.0 cm³ (8 cm³) ¹H-MRS voxel on the bilateral dorsal ACC (Figure 1). The voxel position was prescribed by setting the posterior face of the voxel to coincide with the precentral gyrus and setting the position of the inferior face of the voxel to the most caudal point not part of the corpus callosum. The voxel angle was set to be tangential to the corpus callosum. A semi-LASER ¹H-MRS sequence (TR = 7500 ms, TE = 100 ms, bandwidth = 6000 Hz, N = 2048) was used to acquire 32 channel-combined, VAPOR [41] water-suppressed spectra as well as a water-unsuppressed spectrum to be used for spectral editing and quantification. All the participants were asked to fix their gaze on a white cross (50% gray background) during MRS acquisition.

MRS voxel and spectra. (A) Sagittal, (B) axial and (C) coronal views of voxel positioning on the dorsal anterior cingulate cortex. (D) Sample spectra obtained from a single healthy participant. The bolded black line represents the fitted spectra with the residuals above and each individual metabolite contributions below.

Using the techniques outlined by Near et al. [42], the 32 spectra were phase- and frequency-corrected before being averaged into a single spectrum to be used for all subsequent analyses. QUECC [43] and HSVD [44] were applied to the spectrum for lineshape deconvolution and removal of the residual water signal, respectively. Spectral fitting was done using fitMAN [45], a time-domain fitting algorithm that uses a nonlinear iterative Levenberg–Marquardt minimization algorithm to echo time-specific prior knowledge templates. The metabolite fitting template included 17 brain metabolites: alanine, aspartate, choline, creatine, γ-aminobutyric acid (GABA), glucose, glutamate, glutamine, glutathione, glycine, lactate, myo-inositol, N-acetylaspartate, N-acetylaspartylglutamate, phosphorylethanolamine, scyllo-inositol and taurine. Due to the long echo time used, no significant macromolecular contribution was expected. Metabolite quantification was then performed using Barstool [46] with corrections made for tissue-specific (gray matter, white matter, CSF) T₁ and T₂ relaxation through partial volume segmentation calculations of voxels mapped onto T₁-weighted images acquired using a 0.75-mm isotropic MP2RAGE sequence (TR = 6000 ms, TI₁ = 800 ms, TI₂ = 2700 ms, flip-angle 1 (α₁) = 4°, flip-angle 2 (α₂) = 5°, FOV = 350 mm × 263 mm × 350 mm, T_acq = 9 min 38 s, iPAT_PE = 3 and 6/8 partial k-space). All spectral fit underwent visual quality inspection as well as the Cramer–Rao lower bounds (CRLB) assessment for each metabolite.

The quality of metabolite quantification was measured using CRLB percentages for both groups using a CRLB threshold < 30% for glutathione to determine inclusion toward further analyses, in line with our prior study [18]. Notably, the mean CRLB for these metabolites were over two times lower than the individual threshold percentages. There was no significant difference in CRLB between the clinical high-risk group and the healthy controls for both metabolites reported in this study. We present the concentration and CRLB of other metabolites in our fitting template, along with the two presently mentioned, in the Table S1. A sample of fitted spectrum for a single participant is presented in Figure 1.