Electrical Impedance Myography And Quantitative Ultrasound As Biomarkers In Duchenne Muscular Dystrophy

Electrical impedance myography (EIM) and quantitative ultrasound (QUS) represent two rapid, non-invasive, quantitative, and child-friendly approaches to measure disease outcomes in clinical trials that may surpass functional tests currently in use. In this thesis, we present a comparison of EIM and QUS data in six muscles from 25 DMD and 25 healthy subjects, aged 2-14 years. Quantitative ultrasound data can be analyzed by measuring the brightness, or grayscale level (GSL), of the ultrasound image. To circumvent post-processing variability across manufacturers, for the first time, we introduce a new means of evaluating QUS--a quantitative backscatter analysis (QBA) on the raw radio frequency (RF) signal received by the ultrasound probe in DMD and control subjects. For EIM, either the phase at 50 kHz or the phase ratio (50 kHz/200 kHz) was used for measuring disease status. We found that taking the phase ratio substantially reduces the confounding effect of subcutaneous fat thickness on EIM (r=0.16, p=0.45) compared with the standard phase at 50 kHz (r=-0.72, p\u3c0.0001) in averaged muscles. Using age as a surrogate for disease severity, we found that 50 kHz phase decreased with age in select muscles while GSL did not. The averaged phase correlated strongly with the North Star Ambulatory Assessment (NSAA) (rho=0.735, p=0.004), while GSL did not (rho=-0.442, p=0.115). Although the standard phase at 50 kHz averaged values did not correlate with GSL, the phase ratio did (rho=-0.48, p=0.017). In DMD, age showed comparable correlation with superficial region of interest (ROI) QBA and GSL (average: rho=0.67, p=0.0004 vs. rho=0.47, p=0.020), in contrast to whole muscle ROI, and the difference in rho values did not reach significance (average: z=1.00, p=0.16). Here, we show that whole muscle GSL detects disease pathology from an early age and remains stable over time; in contrast, DMD and control subjects begin with relatively similar EIM values and diverge with age. The moderate correlation between averaged EIM and QUS parameters indicate that the measures provide both overlapping and divergent information about dystrophic muscle. Our data confirm that EIM and QUS show promise as outcome measures that may aid the rapid identification of successful therapeutics currently in clinical pipelines

Impaired consciousness in epilepsy investigated by a prospective responsiveness in epilepsy scale (RES)

Purpose: Impaired consciousness in epileptic seizures has a major negative impact on patient quality of life. Prior work on epileptic unconsciousness has mainly used retrospective and nonstandardized methods. Our goal was to validate and to obtain initial data using a standardized prospective testing battery. Methods: The responsiveness in epilepsy scale (RES) was used on 52 patients during continuous video-electroencephalography (EEG) monitoring. RES begins with higher-level questions and commands, and switches adaptively to more basic sensorimotor responses depending on patient performance. RES continues after seizures and includes postictal memory testing. Scoring was conducted based on video review. Key Findings: Testing on standardized seizure simulations yielded good intrarater and interrater reliability. We captured 59 seizures from 18 patients (35% of participants) during 1,420 h of RES monitoring. RES impairment was greatest during and after tonic-clonic seizures, less in partial seizures, and minimal in auras and subclinical seizures. In partial seizures, ictal RES impairment was significantly greater if EEG changes were present. Maximum RES impairment (lowest ictal score) was also significantly correlated with long postictal recovery time, and poor postictal memory. Significance: We found that prospective testing of responsiveness during seizures is feasible and reliable. RES impairment was related to EEG changes during seizures, as well as to postictal memory deficits and recovery time. With a larger patient sample it is hoped that this approach can identify brain networks underlying specific components of impaired consciousness in seizures. This may allow the development of improved treatments targeted at preventing dysfunction in these networks