The macromolecular MR spectrum does not change with healthy aging

Abstract

Purpose To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. Methods 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm3, pre‐inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite‐nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a “clean” MM spectrum using the Osprey software. Pearson’s correlation coefficient was calculated between integrals and age for the 14 MM signals. One‐way ANOVA was performed to determine differences between age groups. An independent t‐test was carried out to determine differences between sexes. Results MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One‐way ANOVA also suggested no age‐group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM‐predominant CSO (R = –0.29) and GM‐predominant PCC regions (R = –0.57) respectively while CSF increased significantly with age in both regions. Conclusion Our findings suggest that a pre‐defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.

Neurometabolic timecourse of healthy aging

“…Metabolites included in the model are as follows: ascorbate, Asp; creatine, Cr; negative creatine methylene, CrCH2; gamma-aminobutyric acid, GABA; glycerophosphocholine, GPC; glutathione, GSH; glutamine, Gln; glutamate, Glu; myo-inositol, mI; lactate, Lac; N-acetylaspartate, NAA; N-acetylaspartylglutamate, NAAG; phosphocholine, PCh; phosphocreatine, PCr; phosphoethanolamine, PE; scyllo-inositol, sI; and taurine, Tau. Osprey analysis procedures match those previously described in, with the exception that experimentally derived in vivo macromolecular (MM) basis spectra derived from a previous study (Hui et al, 2022b) are incorporated into the basis set instead of 8 parameterized Gaussian basis functions. To create the MM basis function, individual-subject 'clean' MM spectra (separate for PCC and CSO) were modeled with a flexible spline (0.1 ppm knot spacing) across the full spectral range.…”

“…Exclusion criteria included contraindications for MRI and a history of neurological and psychiatric illness. Metabolite-nulled data from the same cohort of subjects was recently published (Hui et al, 2022a) to investigate the age trajectory of macromolecular signals in the spectrum. Since this analysis revealed no significant age- or sex-related changes to the macromolecular spectrum, a cohort-mean macromolecule spectrum was incorporated into the modeling (see Analysis ).…”

“…To create the MM basis function, individual-subject ‘clean’ MM spectra (separate for PCC and CSO) were modeled with a flexible spline (0.1 ppm knot spacing) across the full spectral range. The mean of these splines was taken across all subjects (since no significant MM-age relationships were observed before (Hui et al, 2022b) to generate the cohort-mean MM basis function. Water reference spectra were modeled with a simulated water basis function in the frequency domain with a 6-parameter model (amplitude, zero- and first-order phase, Gaussian and Lorentzian line-broadening, and frequency shift).…”

Cohort-mean measured macromolecules lead to more robust linear-combination modeling than parameterized and subject-specific ones

“…Interestingly, it has not been investigated whether including subject-specific measured MMs (mMM) in the LCM has advantages over cohort-mean mMMs, possibly because prohibitively long acquisition times have disincentivized such a study, and current MRS analysis software does not provide easily accessible workflows to do so. A recent large-scale lifespan cohort 9,10 now provides sufficient publicly available data to compare the two strategies. This dataset further allows investigation of other important issues of MM modeling in short-TE spectra to be investigated:…”

“…Interestingly, it has not been investigated whether including subject-specific measured MMs (mMM) in the LCM has advantages over cohort-mean mMMs, possibly because prohibitively long acquisition times have disincentivized such a study, and current MRS analysis software does not provide easily accessible workflows to do so. A recent large-scale lifespan cohort 9,10 now provides sufficient publicly available data to compare the two strategies. This dataset further allows investigation of other important issues of MM modeling in short-TE spectra to be investigated: Parameterized vs. measured MM: Only a few low-n studies have systematically compared metabolite estimation between parameterized MM (pMM) and mMM modeling, or studied updated MM parameterizations 11–13 . Other studies focused on individual differences between measured MM spectra, but did not investigate the impact on the metabolite estimation 9,14 . LCM algorithms apply modeling parameters to the basis functions, including a single Gaussian linebroadening parameter to account for B 0 inhomogeneities in the MRS volume 7,15,16 .…”