In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesised that the right inferior frontal cortex plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalographyderived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed prior to successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) electroencephalography Stop Signal Task dataset (n=218) to examine averaged normalised β-power, β-burst rate and β-burst 'volume' (which we defined as burst duration x frequency span x amplitude). We first sought to optimise the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful vs. failed stopping and to (2) predict individual Stop Signal Reaction Time. β-Burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalised β-power. The classification model generalised to an external dataset (n=201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition. 3 Significance StatementThe electroencephalography-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of βactivity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activityacross the whole scalp and with high temporal precisionwe employed machine learning on two large datasets. Spatial and temporal features of β-burst 'volume' (duration x frequency span x amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalised β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

To date there exists no reliable method to non-invasively upregulate or downregulate the state of the resting human motor system over a large dynamic range. Here we show that an operant conditioning paradigm which provides neurofeedback of the size of motor evoked potentials (MEPs) in response to transcranial magnetic stimulation (TMS), enables participants to self-modulate their own brain state. Following training, participants were able to robustly increase (by 83.8%) and decrease (by 30.6%) their MEP amplitudes. This volitional up-versus down-regulation of corticomotor excitability caused an increase of late-cortical disinhibition (LCD), a TMS derived read-out of presynaptic GABAB disinhibition, which was accompanied by an increase of gamma and a decrease of alpha oscillations in the trained hemisphere. This approach paves the way for future investigations into how altered brain state influences motor neurophysiology and recovery of function in a neurorehabilitation context.

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.

No abstract

Despite substantial health benefits, smoking cessation attempts have high relapse rates. Neuroimaging measures can sometimes predict individual differences in substance use phenotypes – including relapse – better than behavioral metrics alone. No study to date has compared the relative prediction ability of changes in psychological processes across prolonged abstinence with corresponding changes in brain activity. Here, in a longitudinal design, measurements were made one day prior to smoking cessation, and at 1 and 4 weeks post-cessation (total n=120). Next, we tested the relative role of changes in psychosocial variables vs. task-based functional brain measures predicting time to nicotine relapse up to 12 months. Abstinence was bioverified 4-5 times during the first month. Data were analyzed with a novel machine learning approach to predict relapse. Results showed that increased electrophysiological brain activity during inhibitory control predicted longer time-to-relapse (c-index=0.56). However, reward-related brain activity was not predictive (c-index=0.45). Psychological variables, notably an increase during abstinence in psychological flexibility when experiencing negative smoking-related sensations, predicted longer time-to-relapse (c-index=0.63). A model combining psychosocial and brain data was predictive (c-index=0.68). Using a best-practice approach, we demonstrated generalizability of the combined model on a previously unseen holdout validation dataset (c-index=0.59 vs. 0.42 for a null model). These results show that changes during abstinence – increased smoking-specific psychological flexibility and increased inhibitory control brain function – are important in predicting time to relapse from smoking cessation. In the future, monitoring and augmenting changes in these variables could help improve the chances of successful nicotine smoking abstinence.

Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using noninvasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain-computer interface that human participants (n = 44) learned over multiple sessions across a 6-day period to self-regulate their Beta rhythm (13-20 Hz), either up or down, over the right inferior frontal cortex. Training to downregulate Beta was more effective than training to upregulate Beta. The modulation was evident only during neurofeedback task performance but did not lead to offline alteration of Beta rhythm characteristics at rest, nor to changes in subsequent cognitive behavior. Likewise, a control group (n = 38) who underwent training to up or downregulate the Alpha rhythm (8-12 Hz) did not exhibit behavioral changes. Although the right frontal Beta rhythm has been repeatedly implicated as a key component of the brain's inhibitory control system, the present data suggest that its manipulation offline prior to cognitive task performance does not result in behavioral change in healthy individuals. Whether this form of neurofeedback training could serve as a useful therapeutic target for disorders with dysfunctional inhibitory control as their basis remains to be tested in a context where performance is abnormally poor and neural dynamics are different. K E Y W O R D S alpha oscillations, beta oscillations, brain-computer interface, inhibitory control, neurofeedback, stop signal task [Correction added on June 11, 2022, after first online publication: DOI number was updated in 2.5 section instead of the text '[URL to be inserted after acceptance]'].

No abstract

Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using non-invasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain-computer interface that human participants (n=44) learned over multiple sessions across a 6-day period to self-regulate their Beta rhythm (13-20 Hz) over the right inferior frontal cortex (rIFC). This Beta modulation had observable consequences on cognitive behavior: changes in an individual's inhibitory control performance before and after training were predicted by the magnitude of their training-related change in Beta over rIFC. This was not the case for a control group (n=38) who underwent training of their Alpha rhythm (8-12 Hz). The present data support the view that the right frontal Beta rhythm is a key component of the brain's inhibitory control system. Most importantly, we introduce causality to this relationship for the first time, as experimental modulation of rIFC Beta activity had a measurable impact upon behavior.