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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.

RESUMEN

Multiple sclerosis (MS) is a heterogeneous autoimmune disease for which diagnosis continues to rely on subjective clinical judgment over a battery of tests. Proton magnetic resonance spectroscopy (1H MRS) enables the noninvasive in vivo detection of multiple small-molecule metabolites and is therefore in principle a promising means of gathering information sufficient for multiple sclerosis diagnosis and subtype classification. Here we show that supervised classification using 1H-MRS-visible normal-appearing frontal cortex small-molecule metabolites alone can indeed differentiate individuals with progressive MS from control (held-out validation sensitivity 79% and specificity 68%), as well as between relapsing and progressive MS phenotypes (held-out validation sensitivity 84% and specificity 74%). Post hoc assessment demonstrated the disproportionate contributions of glutamate and glutamine to identifying MS status and phenotype, respectively. Our finding establishes 1H MRS as a viable means of characterizing progressive multiple sclerosis disease status and paves the way for continued refinement of this method as an auxiliary or mainstay of multiple sclerosis diagnostics.

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The pathophysiology of progressive multiple sclerosis remains elusive, significantly limiting available disease-modifying therapies. Proton MRS (1 H-MRS) enables in vivo measurement of small molecules implicated in multiple sclerosis, but its application to key metabolites glutamate, γ-aminobutyric acid (GABA), and glutathione has been sparse. We employed, at 7 T, a previously validated 1 H-MRS protocol to measure glutamate, GABA, and glutathione, as well as glutamine, N-acetyl aspartate, choline, and myoinositol, in the frontal cortex of individuals with relapsing-remitting (N = 26) or progressive (N = 21) multiple sclerosis or healthy control adults (N = 25) in a cross-sectional analysis. Only individuals with progressive multiple sclerosis demonstrated reduced glutamate (F2,65 = 3.424, p = 0.04; 12.40 ± 0.62 mM versus control 13.17 ± 0.95 mM, p = 0.03) but not glutamine (F2,65 = 0.352, p = 0.7; 4.71 ± 0.35 mM versus control 4.84 ± 0.42 mM), reduced GABA (F2,65 = 3.89, p = 0.03; 1.29 ± 0.23 mM versus control 1.47 ± 0.25 mM, p = 0.05), and possibly reduced glutathione (F2,65 = 0.352, p = 0.056; 2.23 ± 0.46 mM versus control 2.51 ± 0.48 mM, p < 0.1). As a group, multiple sclerosis patients demonstrated significant negative correlations between disease duration and glutamate or GABA (ρ = -0.4, p = 0.02) but not glutamine or glutathione. Alone, only relapsing-remitting multiple sclerosis patients exhibited a significant negative correlation between disease duration and GABA (ρ = -0.5, p = 0.03). Taken together, these results indicate that frontal cortex metabolism is differentially disturbed in progressive and relapsing-remitting multiple sclerosis.

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OBJECTIVE: This study characterizes the current capabilities of seizure detection device (SDD) technology and evaluates the fitness of these devices for use in anti-seizure medication (ASM) clinical trials. METHODS: Through a systematic literature review, 36 wireless SDDs featured in published device validation studies were identified. Each device's seizure detection capabilities that addressed ASM clinical trial primary endpoint measurement needs were cataloged. RESULTS: The two most common types of seizures targeted by ASMs in clinical trials are generalized tonic-clonic (GTC) seizures and focal with impaired awareness (FIA) seizures. The Brain Sentinel SPEAC achieved the highest performance for the detection of GTC seizures (F1-score = 0.95). A non-commercial wireless EEG device achieved the highest performance for the detection of FIA seizures (F1-score = 0.88). DISCUSSION: A preliminary assessment of device capabilities for measuring selected ASM clinical trial secondary endpoints was performed. The need to address key limitations in validation studies is highlighted in order to support future assessments of SDD fitness for ASM clinical trial use. In tandem, a stepwise framework to streamline device testing is put forth. These suggestions provide a starting point for establishing SDD reporting requirements before device integration into ASM clinical trials.

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