Biology subjects

Prisciandaro, J. J.

Publications and source records attributed to Prisciandaro, J. J..

3 recordsLinked to original sources

bioRxiv2023

Impact of acquisition and modeling parameters on test-retest reproducibility of edited GABA+

Literature values for within-subject test-retest reproducibility of gamma-aminobutyric acid (GABA), measured with edited magnetic resonance spectroscopy (MRS), vary widely. Reasons for this variation remain unclear. Here we tested whether sequence complexity (two-experiment MEGA-PRESS versus four-experiment HERMES), editing pulse duration (14 versus 20 ms), scanner frequency drift (interleaved water referencing (IWR) turned ON versus OFF), and linear combination modeling variations (three different co-edited macromolecule models and 0.55 versus 0.4 ppm spline baseline knot spacing) affected the within-subject coefficient of variation of GABA + macromolecules (GABA+). We collected edited MRS data from the dorsal anterior cingulate cortex from 20 participants (30.8 {+/-} 9.5 years; 10 males). Test and retest scans were separated by removing the participant from the scanner for 5-10 minutes. Each acquisition consisted of two MEGA-PRESS and two HERMES sequences with editing pulse durations of 14 and 20 ms (referred to here as: MEGA-14, MEGA-20, HERMES-14, and HERMES-20; all TE = 80 ms, 224 averages). Reproducibility did not consistently differ for MEGA-PRESS compared with HERMES or for 14 compared with 20 ms editing pulses. A composite model of the 0.9 and 3 ppm macromolecules (particularly for HERMES) and sparser (0.55 compared with 0.4 ppm) spline baseline knot spacing yielded generally better test-retest reproducibility for GABA+. Replicating our prior results, linear combination modeling in Osprey compared with simple peak fitting in Gannet resulted in substantially better test-retest reproducibility. These results highlight the importance of model selection for edited MRS studies of GABA+, particularly for clinical studies which focus on individual patient differences in GABA+ or changes following an intervention.

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↗

bioRxiv2021

Importance of linear combination modeling for quantification of Glutathione and GABA levels using Hadamard-edited MRS

PurposeTwo main approaches are used for spectral analysis of edited data: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data. The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach will improve quantification of HERMES data compared with simple peak fitting. MethodsA test-retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE 80 ms for HERMES and TE 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variance (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate. ResultsThe reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0% to 9.9%. For MEGA-PRESS data, the GSH reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3% and 8.8% respectively. ConclusionLinear combination modeling with simulated basis functions substantially improves the reproducibility of GSH quantification for HERMES data.

biophysics↗