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While conventional magnetic resonance imaging (MRI) is central to the evaluation of patients with multiple sclerosis, its role in detecting the pathophysiology underlying neurodegeneration is more limited. One of the common outcome measures for progressive multiple sclerosis trials, atrophy on brain MRI, is non-specific and reflects end-stage changes after considerable neurodegeneration has occurred. Identifying biomarkers that identify processes underlying neurodegeneration before it is irreversible and that reflect relevant neurodegenerative pathophysiology is an area of significant need. Accumulating evidence suggests that oxidative stress plays a major role in the pathogenesis of multiple neurodegenerative diseases, including multiple sclerosis. Imaging markers related to inflammation, myelination, and neuronal integrity have been areas of advancement in recent years but oxidative stress has remained an area of unrealized potential. In this article we will begin by reviewing the role of oxidative stress in the pathogenesis of multiple sclerosis. Chronic inflammation appears to be directly related to the increased production of reactive oxygen species and the effects of subsequent oxidative stress appear to be amplified by aging and accumulating disease. We will then discuss techniques in development used in the assessment of MS as well as other models of neurodegenerative disease in which oxidative stress is implicated. Multiple blood and CSF markers of oxidative stress have been evaluated in subjects with MS, but non-invasive imaging offers major upside in that it provides real-time assessment within the brain.

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

The combination of electromyography (EMG) and transcranial magnetic stimulation (TMS) offers a powerful non-invasive approach for investigating corticospinal excitability in both humans and animals. Acquiring and analyzing the data produced with this combination of tools requires overcoming multiple technical hurdles. Due in part to these technical hurdles, the field lacks standard routines for EMG data collection and analysis. This poses a problem for study replication and direct comparisons. Although software toolboxes already exist that perform either online EMG data visualization or offline analysis, there currently are no openly available toolboxes that flexibly perform both and also interface directly with peripheral EMG and TMS equipment. Here, we introduce Visualize EMG TMS Analyze (VETA), a MATLAB-based toolbox that supports simultaneous EMG data collection and visualization as well as automated offline processing and is specially tailored for use with motor TMS. The VETA toolbox enables the simultaneous recording of EMG, timed administration of TMS, and presentation of behavioral stimuli from a single computer. These tools also provide a streamlined analysis pipeline with interactive data visualization. Finally, VETA offers a standard EMG data format to facilitate data sharing and open science.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

Much research has focused on how people stop initiated response tendencies when instructed by a signal. Stopping of this kind appears to have global effects on the motor system. For example, by delivering transcranial magnetic stimulation (TMS) over the leg area of the primary motor cortex, it is possible to detect suppression in the leg when the hand is being stopped (Badry R et al. Suppression of human cortico-motoneuronal excitability during the stop-signal task. Clin Neurophysiol 120: 1717–1723, 2009). Here, we asked if such "global suppression" can be observed proactively, i.e., when people anticipate they might have to stop. We used a conditional stop signal task, which allows the measurement of both an "anticipation phase" (i.e., where proactive control is applied) and a "stopping" phase. TMS was delivered during the anticipation phase (experiment 1) and also during the stopping phase (experiments 1 and 2) to measure leg excitability. During the anticipation phase, we did not observe leg suppression, but we did during the stopping phase, consistent with Badry et al. (2009). Moreover, when we split the subject groups into those who slowed down behaviorally (i.e., exercised proactive control) and those who did not, we found that subjects who slowed did not show leg suppression when they stopped, whereas those who did not slow did show leg suppression when they stopped. These results suggest that if subjects prepare to stop, then they do so without global effects on the motor system. Thus, preparation allows them to stop more selectively. [ABSTRACT FROM AUTHOR]