Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study

The analysis of neurophysiological data using the two most widely used open-source MATLAB toolboxes, FieldTrip and Brainstorm, validates our hypothesis about the correlation between event-related coherence in the visual cortex and neuronal noise. The analyzed data were obtained from magnetoencephalography (MEG) experiments based on visual perception of flickering stimuli, in which fifteen subjects effectively participated. Before coherence and brain noise calculations, MEG data were first transformed from recorded channel data to brain source waveforms by solving the inverse problem. The inverse solution was obtained for a 2D cortical shape in Brainstorm and a 3D volume in FieldTrip. We found that stronger brain entrainment to the visual stimuli concurred with higher brain noise in both studies.

Functional Connectivity Pattern Analysis Underlying Neural Oscillation Synchronization during Deception

To characterize system cognitive processes during deception, event-related coherence was computed to investigate the functional connectivity among brain regions underlying neural oscillation synchronization. In this study, 15 participants were randomly assigned to honesty or deception groups and were instructed to tell the truth or lie when facing certain stimuli. Meanwhile, event-related potential signals were recorded using a 64-channel electroencephalography cap. Event-related coherence was computed separately in four frequency bands (delta (1-3.5 Hz), theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 HZ)) for the long-range intrahemispheric electrode pairs (F3P3, F4P4, F3T7, F4T8, F3O1, and F4O2). The results indicated that deceptive responses elicited greater connectivities in the frontoparietal and frontotemporal networks than in the frontooccipital network. Furthermore, the deception group displayed lower values of coherence in the frontoparietal electrode pairs in the alpha and beta bands than the honesty group. In particular, increased coherence in the delta and theta bands on specific left frontoparietal electrode pairs was observed. Additionally, the deception group exhibited higher values of coherence in the delta band and lower values of coherence in the beta band on the frontotemporal electrode pairs than did the honesty group. These data indicated that the active cognitive processes during deception include changes in ensemble activities between the frontal and parietal/temporal regions.

Electroencephalographic connectivity measures predict learning of a motor sequencing task

Individuals vary significantly with respect to rate and degree of improvement with motor practice. While the regions that underlie motor learning have been well described, neurophysiological factors underlying differences in response to motor practice are less well understood. The present study examined both resting-state and event-related EEG coherence measures of connectivity as predictors of response to motor practice on a motor sequencing task using the dominant hand. Thirty-two healthy young right-handed participants underwent resting EEG before motor practice. Response to practice was evaluated both across the single session of motor practice and 24 h later at a retention test of short-term motor learning. Behaviorally, the group demonstrated statistically significant gains both in single-session “motor improvement” and across-session “motor learning.” A resting-state measure of whole brain coherence with primary motor cortex (M1) at baseline robustly predicted subsequent motor improvement (validated R2 = 0.55) and motor learning (validated R2 = 0.68) in separate partial least-squares regression models. Specifically, greater M1 coherence with left frontal-premotor cortex (PMC) at baseline was characteristic of individuals likely to demonstrate greater gains in both motor improvement and motor learning. Analysis of event-related coherence with respect to movement found the largest changes occurring in areas implicated in planning and preparation of movement, including PMC and frontal cortices. While event-related coherence provided a stronger prediction of practice-induced motor improvement (validated R2 = 0.73), it did not predict the degree of motor learning (validated R2 = 0.16). These results indicate that connectivity in the resting state is a better predictor of consolidated learning of motor skills. NEW & NOTEWORTHY Differences in response to motor training have significant societal implications across a lifetime of motor skill practice. By evaluating both resting-state and event-related measures of brain function, our findings highlight interindividual differences in brain connectivity providing unique insights into differences in response to motor training. These findings have wide-ranging implications in settings ranging from advanced professional motor training to rehabilitation after brain injury.

A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a Brain-Computer Interface

We study the relationship between electroencephalographic (EEG) coherence and accuracy in operating a brain-computer interface (BCI). In our case, the BCI is controlled through motor imagery. Hence, a number of volunteers were trained using different training paradigms: classical visual feedback, auditory stimulation, and functional electrical stimulation (FES). After each training session, the volunteers’ accuracy in operating the BCI was assessed, and the event-related coherence (ErCoh) was calculated for all possible combinations of pairs of EEG sensors. After at least four training sessions, we searched for significant differences in accuracy and ErCoh using one-way analysis of variance (ANOVA) and multiple comparison tests. Our results show that there exists a high correlation between an increase in ErCoh and performance improvement, and this effect is mainly localized in the centrofrontal and centroparietal brain regions for the case of our motor imagery task. This result has a direct implication with the development of new techniques to evaluate BCI performance and the process of selecting a feedback modality that better enhances the volunteer’s capacity to operate a BCI system.