Low- and High-resolution Dynamic Analyses for Magnetic Resonance Spectroscopy Data

Equipment

1. 3-tesla MRI scanner unit (Siemens PRISMA, Siemens Healthcare)

2. 32-channel head coil for MRI signal reception (Siemens PRISMA, Siemens Healthcare)

Software

1. MATLAB R2018B (The MathWorks, www.mathworks.com)

Procedure

1. Set up the subject in the MRI scanner using a 32-channel head coil.

   
   

2. Perform magnetic resonance imaging (MRI) anatomical scan with the following parameters:

   Whole-brain T1-Magnetization Prepared Rapid Acquisition of Gradient Echoes (MPRAGE, sagittal 3D turbo-fast low angle shot sequence, acquisition matrix 256 × 256, isotropic resolution 1 mm, 176 slices, repetition time (TR) = 2250 ms, echo time (TE) = 3.02 ms, inversion time (TI) = 900 ms, flip angle 9°, acquisition time 5 min 38 s). Parameters were reproduced from Rideaux and Welchman (2019).

   
   

3. Perform magnetic resonance spectroscopy (MRS) scan, using the MRI anatomical scan to position the voxel, with the following parameters:

   MEGA-PRESS sequence (Mescher et al., 1996 and 1998): TE = 68 ms, TR = 3000 ms; 256 transients of 2048 data points, acquisition time 12 min 48 s; 14.28 ms Gaussian editing pulse at 1.9 (ON) and 7.5 (OFF) ppm. Apply water suppression using variable power with optimized relaxation delays (VAPOR; Tkáč and Gruetter, 2005) and outer volume suppression. Position voxel (3 × 3 × 2 cm) medially in the occipital lobe; with the lower face aligned with the cerebellar tentorium and positioned so to avoid including the sagittal sinus and to ensure it remains within the occipital lobe (Figure 1). Perform automated shimming followed by manual shimming to achieve approximately 12 Hz water linewidth.

   
   

   
   Figure 1. MRS voxel placement. Screenshot taken from Siemens MRI scanner terminal showing an example of voxel placement (yellow bounding box) in the (A) sagittal, (B) coronal, and (C) axial plane.

Data analysis

To preprocess the data, for all transients, fit a Lorentzian model to the creatine (Cr) signal at 3.0 ppm to obtain frequency, phase, area, and full width at half maximum (FWHM) parameters (Figure 2A). Remove outlier transients with any parameter estimates > 3 standard deviations from the mean within the scan. Align the frequency of the all transients using the “circshift” MATLAB function, shifting each spectrum by the difference between the estimated frequency of the Cr signal and the target frequency at 3.0 ppm (Figure 2B). Align the phase of each spectra with the following:

where spectrum and spectrum_corr denote the uncorrected and phase corrected spectra, respectively, and phase denotes the phase parameter estimate in radians of the Lorentzian model.

Follow the following steps to perform the sliding window analysis. For each scan, calculate the average of all i) OFF transients and ii) OFF – ON (i.e., difference spectrum) transients between the first acquisition and the size of the sliding window (i.e., 128; Figure 2C). Quantify the total creatine (tCr) and total N-acetylaspartate (tNAA) signal intensity by fitting a single Lorentzian model to the average OFF spectra at 3.0 ppm and 2.0 ppm, respectively. Quantify GABA+ (γ-aminobutyric acid and co-edited macromolecules; 3 ppm) and Glx (a complex comprising Glu and Glutamine (Gln); 3.8 ppm) signal intensity by fitting single- and double- Gaussian models, respectively (Figure 2D-2E). Calculate neurometabolite signal intensities as the area of the fitted peak/s. Repeat this process, starting at the second ON/OFF transient, rather than the first, until the last transient in the scan is reached. That is, slide the window across from the point where one edge is aligned to the first transient in the scan to the point where the other edge is aligned to the last transient in the scan. Omit data from further analysis for which the FWHM of any of the quantified neurometabolites (tCr, tNAA, GABA+, or Glx) is > 3 standard deviations from the mean across the scan. This analysis provides a temporal trace of metabolite concentration for each metabolite, for each scan (Figure 2F). Compare traces between subjects, or conditions, to test for differences in neurometabolite temporal dynamics. Additionally, calculate difference of each estimate from first measurement to assess significant changes over time. A cluster correction, with time as the clustered dimension, can be applied to avoid possible spurious differences.

Follow the following steps to perform the intersubject combination analysis. Calculate the average of the first i) OFF transients and ii) OFF-ON transients across all subjects. Quantify tCR, tNAA, GABA+, and Glx signal intensity in the same manner as described above. Move to next ON/OFF transient pair in the scan and repeat these steps. Continue to repeat for the entire scan. This analysis provides a temporal a single trace for each metabolite concentration with the highest possible temporal resolution achievable with the data (i.e., 6 s). Compare high temporal resolution trace between conditions to test for differences in neurometabolite temporal dynamics. Additionally, use cross correlation between dynamic neurometabolite estimates to assess high frequency interdependencies (Rideaux, 2020).

Figure 2. Analysis. A. Example Lorentzian model fit (green) to Cr signal (black). B. Spectra (n = 256) from representative scan before and after alignment. C. Average edit OFF (red), ON (blue), and difference (yellow) spectra. D. Example double-Gaussian model fit (green) to Glx signal (black). E. Example Gaussian model fit (green) to GABA signal (black). F. Example change in Glx/GABA concentration as a function of time using the sliding window method.

Notes

1. We aligned to the Cr signal, but other alignment methods can alternatively be used.

2. We used a window size of 128 transients, equal to half the length of the scan, but different window sizes may be used, with the constraint that the window must be suitably large to yield sufficient signal-to-noise ratio.

Acknowledgments

This research was supported by Leverhulme Trust Grant ECF-2017-573 and Isaac Newton Trust Grant 17.08(o). The protocol was derived from Rideaux (2020).

Competing interests

The authors declare no financial or non-financial competing interests.

Ethics

All experiments were conducted in accordance with the ethical guidelines of the Declaration of Helsinki and were approved by the University of Cambridge ethics committee, and all participants provided informed consent.

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