Biology subjects

Zöllner, H. J.

Publications and source records attributed to Zöllner, H. J..

4 recordsLinked to original sources

bioRxiv2023

Metabolite T1 relaxation times differ across the adult lifespan

PurposeTo investigate the age-dependence of metabolite T1 relaxation times at 3T in both gray- and white-matter-rich voxels. MethodsThis manuscript analyzes publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3T using PRESS localization. Voxels were placed in posterior cingulate cortex and centrum semiovale in 102 healthy volunteers across 5 decades of life (20s to 60s). All spectra were analyzed in Osprey v2.4.0. To estimate T1 relaxation times for tNAA2.0 and tCr3.0, the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single inversion recovery signal equation. Correlations between T1 and subject age were evaluated. ResultsSpearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.43; p < 0.001) and tCr3.0 (rs = -0.23; p = 0.021) decreased significantly with age in white-matter-rich CSO, and less steeply (and not significantly) for tNAA2.0 (rs = -0.15; p = 0.136) and tCr3.0 (rs = -0.10; p = 0.319) in gray-matter-rich PCC. ConclusionThe analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.

neuroscience↗

bioRxiv2022

More than half of the variance in in-vivo 1H-MRS metabolite estimates is common to all metabolites

The present study characterized associations among brain metabolite levels, applying bivariate and multivariate (i.e., factor analysis) statistical methods to tCr-referenced estimates of the major PRESS 1H-MRS metabolites (i.e., tNAA/tCr, tCho/tCr, mI/tCr, Glx/tCr), acquired from medial parietal lobe in a large (n=299), well-characterized international cohort of healthy volunteers (Povazan et al., 2020). Results supported the hypothesis that 1H-MRS-measured metabolite estimates are moderately intercorrelated (Mr = 0.42, SDr = 0.11, ps < 0.001), with more than half (i.e., 57%) of the total variability in metabolite estimates common to (i.e., shared by) all metabolites. Older age was significantly associated with lower levels of common metabolite variance (CMV; {beta} = -0.09, p = 0.048), despite not being associated with levels of any individual metabolite. Holding CMV levels constant, females had significantly lower levels of total choline (i.e., unique metabolite variance or UMV; {beta} = -0.19, p < 0.001), mirroring significant bivariate correlations between sex and total choline reported previously. If replicated, these results would suggest that applied 1H-MRS researchers should shift their analytical framework from examining bivariate associations between individual metabolites and specialty-dependent (e.g., clinical, research) variables of interest (e.g., using t-tests) to examining multi-variable (i.e., covariate) associations between multiple metabolites and specialty-dependent variables of interest (e.g., using multiple regression). Without this shift, clear interpretation of associations of 1H-MRS metabolites with specialty-dependent variables of interest may not be possible.

neuroscience↗

bioRxiv2022

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

Expert consensus recommends linear-combination modeling (LCM) of 1H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions have been derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the macromolecular baseline. Fully tissue- and relaxation-corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort-mean measured MM. Metabolite-age associations were consistently found for two major singlet signals (tCr, tCho) for all MM strategies, however, findings for highly J-coupled metabolites it was depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite-age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high-SNR MM basis function (cohort-mean) leads to more robust metabolite estimation (lower model residuals and AIC values) compared to MM strategies with more degrees of freedom (Gaussian parametrization) or subject-specific MM information. Integration of multiple LCM analyses into a single statistical model potentially improves the robustness in the detection of underlying effects (e.g. metabolite vs age), reduces algorithm-based bias, and estimates algorithm-related variance.

neuroscience↗

bioRxiv2022

Impact of gradient scheme and shimming on out-of-voxel echo artifacts in edited MRS

Out-of-voxel (OOV) signals are common spurious echo artifacts in magnetic resonance spectroscopy (MRS). These signals often manifest in the spectrum as very strong ripples which interfere with spectral quantification by overlapping with targeted metabolite resonances. Dephasing Optimization Through Coherence Order Pathway Selection (DOTCOPS) gradient schemes are algorithmically optimized to suppress all potential alternative coherence transfer pathways (CTPs), and should suppress unwanted OOV echoes. In addition, second-order shimming uses non-linear gradient fields to maximize field homogeneity inside the voxel, which unfortunately increases the diversity of local gradient fields outside of the voxel. Given that strong local spatial B0 gradients can refocus unintended CTPs, it is possible that OOVs are less prevalent when only linear first-order shimming is applied. Here we compare the size of unwanted OOV signals in Hadamard-edited (HERMES) data acquired with either a local gradient scheme (which we refer to here as "Shared") or DOTCOPS, and with first or second-order shimming. We collected data from 15 healthy volunteers in two brain regions (voxel size 30x26x26mm3) from which it is challenging to acquire MRS data: medial prefrontal cortex (MPFC) and left temporal cortex (LTC). Characteristic OOV echoes were seen in both GABA- and GSH-edited spectra for both brain regions, gradient schemes, and shimming approaches. A linear mixed-effect (LME) model revealed a statistically significant difference in the average residual based on gradient scheme in both GABA-(p < 0.001) and GSH-edited (p < 0.001) spectra; that is, the DOTCOPS gradient scheme resulted in smaller OOV artifacts compared with the Shared scheme. There were no significant differences in OOV artifacts associated with shimming method. Thus, these results suggest that the DOTCOPS gradient scheme for J-difference-edited PRESS acquisitions yields spectra with smaller OOV echo artifacts than the Shared gradient scheme implemented in a widely disseminated editing sequence.

biophysics↗