Improvements in precision and accuracy of complex- relative to real-domain linear combination model spectral fitting not necessarily recovered by zero filling.

Although the information obtained from in vivo proton magnetic resonance spectroscopy (1H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Medial prefrontal cortex neurotransmitter abnormalities in posttraumatic stress disorder with and without comorbidity to major depression.

Posttraumatic stress disorder (PTSD) is a chronic psychiatric condition that follows exposure to a traumatic stressor. Though previous in vivo proton (1H) MRS) research conducted at 4 T or lower has identified alterations in glutamate metabolism associated with PTSD predisposition and/or progression, no prior investigations have been conducted at higher field strength. In addition, earlier studies have not extensively addressed the impact of psychiatric comorbidities such as major depressive disorder (MDD) on PTSD-associated 1H-MRS-visible brain metabolite abnormalities. Here we employ 7 T 1H MRS to examine concentrations of glutamate, glutamine, GABA, and glutathione in the medial prefrontal cortex (mPFC) of PTSD patients with MDD (PTSD+MDD+; N = 6) or without MDD (PTSD+MDD-; N = 5), as well as trauma-unmatched controls without PTSD but with MDD (PTSD-MDD+; N = 9) or without MDD (PTSD-MDD-; N = 18). Participants with PTSD demonstrated decreased ratios of GABA to glutamine relative to healthy PTSD-MDD- controls but no single-metabolite abnormalities. When comorbid MDD was considered, however, MDD but not PTSD diagnosis was significantly associated with increased mPFC glutamine concentration and decreased glutamate:glutamine ratio. In addition, all participants with PTSD and/or MDD collectively demonstrated decreased glutathione relative to healthy PTSD-MDD- controls. Despite limited findings in single metabolites, patterns of abnormality in prefrontal metabolite concentrations among individuals with PTSD and/or MDD enabled supervised classification to separate them from healthy controls with 80+% sensitivity and specificity, with glutathione, glutamine, and myoinositol consistently among the most informative metabolites for this classification. Our findings indicate that MDD can be an important factor in mPFC glutamate metabolism abnormalities observed using 1H MRS in cohorts with PTSD.