Brain event-related potentials predict individual differences in inhibitory control.

Stop-signal reaction time (SSRT), the time needed to cancel an already-initiated motor response, quantifies individual differences in inhibitory control. Electrophysiological correlates of SSRT have primarily focused on late event-related potential (ERP) components over midline scalp regions from successfully inhibited stop trials. SSRT is robustly associated with the P300, there is mixed evidence for N200 involvement, and there is little information on the role of early ERP components. Here, machine learning was first used to interrogate ERPs during both successful and failed stop trials from 64 scalp electrodes at 4 ms resolution (n = 148). The most predictive model included data from both successful and failed stop trials, with a cross-validated Pearson's r of 0.32 between measured and predicted SSRT, significantly higher than null models. From successful stop trials, spatio-temporal features overlapping the N200 in right frontal areas and the P300 in frontocentral areas predicted SSRT, as did early ERP activity (<200 ms). As a demonstration of the reproducibility of these findings, the application of this model to a separate dataset of 97 participants was also significant (r = 0.29). These results show that ERPs during failed stops are relevant to SSRT, and that both early and late ERP activity contribute to individual differences in SSRT. Notably, the right lateralized N200, which predicted SSRT here, is not often observed in neurotypical adults. Both the ascending slope and peak of the P300 component predicted SSRT. These results were replicable, both within the training sample and when applied to ERPs from a separate dataset.

Upregulating excitability of corticospinal pathways in stroke patients using TMS neurofeedback; A pilot study.

Upper limb weakness following a stroke affects 80% of survivors and is a key factor in preventing their return to independence. State-of-the art approaches to rehabilitation often require that the patient can generate some activity in the paretic limb, which is not possible for many patients in the early period following stroke. Approaches that enable more patients to engage with upper limb therapy earlier are urgently needed. Motor imagery has shown promise as a potential means to maintain activity in the brain's motor network, when the patient is incapable of generating functional movement. However, as imagery is a hidden mental process, it is impossible for individuals to gauge what impact this is having upon their neural activity. Here we used a novel brain-computer interface (BCI) approach allowing patients to gain an insight into the effect of motor imagery on their brain-muscle pathways, in real-time. Seven patients 2-26 weeks post stroke were provided with neurofeedback (NF) of their corticospinal excitability measured by the size of motor evoked potentials (MEP) in response to transcranial magnetic stimulation (TMS). The aim was to train patients to use motor imagery to increase the size of MEPs, using the BCI with a computer game displaying neurofeedback. Patients training finger muscles learned to elevate MEP amplitudes above their resting baseline values for the first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles. By day 3 for ADM and day 4 for FDI, MEP amplitudes were sustained above baseline in all three NF blocks. Here we have described the first clinical implementation of TMS NF in a population of sub-acute stroke patients. The results show that in the context of severe upper limb paralysis, patients are capable of using neurofeedback to elevate corticospinal excitability in the affected muscles. This may provide a new training modality for early intervention following stroke.

Stop-signal reaction time correlates with a compensatory balance response.

BACKGROUND: Response inhibition involves suppressing automatic, but unwanted action, which allows for behavioral flexibility. This capacity could theoretically contribute to fall prevention, especially in the cluttered environments we face daily. Although much has been learned from cognitive psychology regarding response inhibition, it is unclear if such findings translate to the intensified challenge of coordinating balance recovery reactions. RESEARCH QUESTION: Is the ability to stop a prepotent response preserved when comparing performance on a standard test of response inhibition versus a reactive balance test where compensatory steps must be occasionally suppressed? METHODS: Twelve young adults completed a stop signal task and reactive balance test separately. The stop signal task evaluates an individual's ability to quickly suppress a visually-cued button press upon hearing a 'stop' tone, and provides a measure of the speed of response inhibition called the Stop Signal Reaction Time (SSRT). Reactive balance was tested by releasing participants from a supported lean position, in situations where the environment was changed during visual occlusion. Upon receiving vision, participants were required to either step to regain balance following cable release (70% of trials), or suppress a step if an obstacle was present (30% of trials). The early muscle response of the stepping leg was compared between the 'step blocked' and 'step allowed' trials to quantify step suppression. RESULTS: SSRT was correlated with muscle activation of the stepping leg when sufficient time was provided to view the response environment (400 ms). Individuals with faster SSRTs exhibited comparably less leg muscle activity when a step was blocked, signifying a superior ability to inhibit an unwanted step. SIGNIFICANCE: Performance on a standardized test of response inhibition is related to performance on a reactive balance test where automated stepping responses must occasionally be inhibited. This highlights a generalizable neural mechanism for stopping action across different behavioral contexts.

Improving the quality of combined EEG-TMS neural recordings: Introducing the coil spacer.

BACKGROUND: In the last decade, interest in combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) approaches has grown substantially. Aside from the obvious artifacts induced by the magnetic pulses themselves, separate and more sinister signal disturbances arise as a result of contact between the TMS coil and EEG electrodes. NEW METHOD: Here we profile the characteristics of these artifacts and introduce a simple device - the coil spacer - to provide a platform allowing physical separation between the coil and electrodes during stimulation. RESULTS: EEG data revealed high amplitude signal disturbances when the TMS coil was in direct contact with the EEG electrodes, well within the physiological range of viable EEG signals. The largest artifacts were located in the Delta and Theta frequency range, and standard data cleanup using independent components analysis (ICA) was ineffective due to the artifact's similarity to real brain oscillations. COMPARISON WITH EXISTING METHOD: While the current best practice is to use a large coil holding apparatus to fixate the coil 'hovering' over the head with an air gap, the spacer provides a simpler solution that ensures this distance is kept constant throughout testing. CONCLUSIONS: The results strongly suggest that data collected from combined TMS-EEG studies with the coil in direct contact with the EEG cap are polluted with low frequency artifacts that are indiscernible from physiological brain signals. The coil spacer provides a cheap and simple solution to this problem and is recommended for use in future simultaneous TMS-EEG recordings.