Follow-up analyses on the effects of long-term use of high fat diet on hippocampal metabolite concentrations in Wistar rats: Comparing Tarquin quantification of 7.0T rat metabolites to LCModel

Abstract

We have recently observed that one-year use of high fat diet (HFD) that induced mild ketonemia lead to better learning and memory, larger hippocampi volumes without any changes to cortical volumes, as well as higher concentrations of total NAA (tNAA: N-acetylaspartate and N-acetylaspartateglutame; marker of neuronal viability), total Cho (tCho: Glycerophosphocholine +Phosphocholine, which are believed to be primarily involved in cell membrane breakdown and synthesis) and total Cr (tCr: creatine + phospo-creatine -involved in cell bioenergetics). However, the spectroscopic results may have been driven by specific processing procedures used by LC Model, thus we needed to use a different software to assure the obtained results are independent of processing procedure. TARQUIN (Wilson et al, 2011) [1] is an open source alternative that was demonstrated to work comparably well to LCModelTM with wide range of 1.5T and 3.0T proton spectra. However, it has not been used to process proton, animal spectra acquired at 7.0T. Here, we 1) created basis sets for TARQUIN to work with spectra obtained at 7T, 2) reanalyzed the data, and finally 3) compared performance of TARQUIN and LCModelTM for single voxel hippocampal and anterior cingulate cortex spectra obtained from 50 onemonth Wistar rats at 7T, and later, when they were one year old (n=47). Two different basis sets were proposed: one based on basis set used by LCModel TM , and the other one adapted from a basis set established for 16.1T. Given the intrinsic differences in processing between LCModel TM and TARQUIN, we evaluated quality of fit (Q) and performed Bland-Altman analysis to estimate the agreement between the methods. Moreover, we calculated mean baseline and mean fit for 50 one-month old rats to identify potential systematic errors in fits. Finally, results from an exemplary experiment obtained with LCModel TM were reproduced with TARQUIN. Bland Altman plots indicate that there is an acceptable agreement between LCModel TM and Tarquin with adjacent basis set for total N-Acetylo-aspartate (tNAA), total-choline (tCho), total-creatine (tCr) and glutamine/glutamate (Glx) (95% confidence interval of agreement below 20%). However, for both basis sets, Tarquin gave significantly more variable results in myo-Inositol comparing to LCModel. In conclusion, despite some potential biases to the results, spectra were successfully processed with Tarquin and they yielded similar results to those obtained with LCModel.

Comparison of different linear-combination modelling algorithms for short-TE proton spectra

“…The strong agreement of group means and CVs for metabolites with prominent singlets (tNAA/tCho) and inconsistency for lower-intensity coupled signals (mI/Glx) are in line with previous two-tool comparisons of simulated data 7,15 and in-vivo studies with smaller sample sizes 7,14,16 .…”

“…This is exacerbated by the substantial variability of correlation strengths at vendor- or even site-level, and is even more likely to be the case for ‘real-life’ clinical data, given the relatively high quality of the dataset ‘best case scenario’ in this study (standardized pre-processing; large sample size; high SNR; low linewidth; young, healthy, cooperative subjects). While two previous studies found that some differences between clinical groups remained significant independent of the LCM algorithm 14,16 , this is questionable as a default assumption. The lack of comparability arising from the additional variability originating in the choice of analysis tool is rarely recognized or acknowledged.…”

“…In-vivo comparisons are often limited to small sample sizes, whether analyzing spectra from animal models 7,12,13 or human subjects 7,8,12 . To the best of our knowledge, two exceptions compared the LCM performance of different algorithms in rat brain 14 and human body 15 , respectively. Most studies report good agreement between results from different algorithms, inferring this from group-mean comparisons, or observing that differences between clinical groups are consistent regardless of the algorithm applied 14,16 .…”

“…To the best of our knowledge, two exceptions compared the LCM performance of different algorithms in rat brain 14 and human body 15 , respectively. Most studies report good agreement between results from different algorithms, inferring this from group-mean comparisons, or observing that differences between clinical groups are consistent regardless of the algorithm applied 14,16 . Correlations of estimates from different algorithms are rarely reported; however, a high correlation between LCModel and Tarquin results was found in the rat brain at ultra-high field 14 .…”

“…Most studies report good agreement between results from different algorithms, inferring this from group-mean comparisons, or observing that differences between clinical groups are consistent regardless of the algorithm applied 14,16 . Correlations of estimates from different algorithms are rarely reported; however, a high correlation between LCModel and Tarquin results was found in the rat brain at ultra-high field 14 . Despite the fact that LCM has been used to analyze thousands of studies ( Table 1 ), a comprehensive assessment of the agreement between the algorithms is lacking, and the relationship between the choice of model parameters and quantitative outcomes is poorly understood.…”