Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles.

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.

In vivo evidence of differential frontal cortex metabolic abnormalities in progressive and relapsing-remitting multiple sclerosis.

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.

Seizure detection devices for use in antiseizure medication clinical trials: A systematic review.

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.