Self-regulation of the brain's right frontal Beta rhythm using a brain-computer interface.

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.

Volume of ß-Bursts, But Not Their Rate, Predicts Successful Response Inhibition.

In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesized that the right inferior frontal cortex (rIFC) plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalography (EEG)-derived ß-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal ß-power often observed before successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of ß-activity. There is evidence that the rate of ß-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of ß-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) EEG Stop Signal task (SST) dataset (n = 218) to examine averaged normalized ß-power, ß-burst rate, and ß-burst "volume" (which we defined as burst duration × frequency span × amplitude). We first sought to optimize the ß-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 versus failed stopping and to (2) predict individual stop signal reaction time (SSRT). ß-burst volume was significantly more predictive of successful and fast stopping than ß-burst rate and normalized ß-power. The classification model generalized to an external dataset (n = 201). We suggest ß-burst volume is a sensitive and reliable measure for investigation of human response inhibition.SIGNIFICANCE STATEMENT The electroencephalography (EEG)-derived ß-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of ß-activity that contribute to successful and fast inhibition. In order to search for the most relevant features of ß-activity, across the whole scalp and with high temporal precision, we employed machine learning on two large datasets. Spatial and temporal features of ß-burst "volume" (duration × frequency span × amplitude) predicted response inhibition outcomes in our data significantly better than ß-burst rate and normalized ß-power. These findings suggest that multidimensional measures of ß-bursts, such as burst volume, can add to our understanding of human response inhibition.

Neural activity related to volitional regulation of cortical excitability.

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.