Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data

Neural correlates of motor learning: Network communication versus local oscillations

Learning new motor skills through training, also termed motor learning, is central for everyday life. Current training strategies recommend intensive task-repetitions aimed at inducing local activation of motor areas, associated with changes in oscillation amplitudes ("event-related power") during training. More recently, another neural mechanism was suggested to influence motor learning: modulation of functional connectivity (FC), that is, how much spatially separated brain regions communicate with each other before and during training. The goal of the present study was to compare the impact of these two neural processing types on motor learning. We measured EEG before, during, and after a finger-tapping task (FTT) in 20 healthy subjects. The results showed that training gain, long-term expertise (i.e., average motor performance), and consolidation were all predicted by whole-brain alpha- and beta-band FC at motor areas, striatum, and mediotemporal lobe (MTL). Local power changes during training did not predict any dependent variable. Thus, network dynamics seem more crucial than local activity for motor sequence learning, and training techniques should attempt to facilitate network interactions rather than local cortical activation.

Using a linear dynamic system to measure functional connectivity from M/EEG

Objective.Measures of functional connectivity (FC) can elucidate which cortical regions work together in order to complete a variety of behavioral tasks. This study's primary objective was to expand a previously published model of measuring FC to include multiple subjects and several regions of interest. While FC has been more extensively investigated in vision and other sensorimotor tasks, it is not as well understood in audition. The secondary objective of this study was to investigate how auditory regions are functionally connected to other cortical regions when attention is directed to different distinct auditory stimuli.Approach.This study implements a linear dynamic system (LDS) to measure the structured time-lagged dependence across several cortical regions in order to estimate their FC during a dual-stream auditory attention task.Results.The model's output shows consistent functionally connected regions across different listening conditions, indicative of an auditory attention network that engages regardless of endogenous switching of attention or different auditory cues being attended.Significance.The LDS implemented in this study implements a multivariate autoregression to infer FC across cortical regions during an auditory attention task. This study shows how a first-order autoregressive function can reliably measure functional connectivity from M/EEG data. Additionally, the study shows how auditory regions engage with the supramodal attention network outlined in the visual attention literature.

EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio

EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.

Targeted rhythmic visual stimulation at individual participants' intrinsic alpha frequency causes selective increase of occipitoparietal BOLD-fMRI and EEG functional connectivity

Neural oscillations in distinct frequency bands are ubiquitous in the brain and play a role in many cognitive processes. The "communication by coherence" hypothesis, poses that the synchronization through phase coupling of frequency-specific neural oscillations regulate information flow across distribute brain regions. Specifically, the posterior alpha frequency band (7-12 Hz) is thought to gate bottom-up visual information flow by inhibition during visual processing. Evidence shows that increased alpha phase coherency positively correlates with functional connectivity in resting state connectivity networks, supporting alpha mediates neural communication through coherency. However, these findings have mainly been derived from spontaneous changes in the ongoing alpha rhythm. In this study, we experimentally modulate the alpha rhythm by targeting individuals' intrinsic alpha frequency with sustained rhythmic light to investigate alpha-mediated synchronous cortical activity in both EEG and fMRI. We hypothesize increased alpha coherency and fMRI connectivity should arise from modulation of the intrinsic alpha frequency (IAF) as opposed to control frequencies in the alpha range. Sustained rhythmic and arrhythmic stimulation at the IAF and at neighboring frequencies within the alpha band range (7-12 Hz) was implemented and assessed in a separate EEG and fMRI study. We observed increased cortical alpha phase coherency in the visual cortex during rhythmic stimulation at the IAF as in comparison to rhythmic stimulation of control frequencies. In the fMRI, we found increased functional connectivity for stimulation at the IAF in visual and parietal areas as compared to other rhythmic control frequencies by correlating time courses from a set of regions of interest for the different stimulation conditions and applying network-based statistics. This suggests that rhythmic stimulation at the IAF frequency induces a higher degree of synchronicity of neural activity across the occipital and parietal cortex, which supports the role of the alpha oscillation in gating information flow during visual processing.

Data on a novel approach examining the role of the cerebellum in gait performance improvement in patients with Parkinson disease receiving neurologic music therapy

Individuals with idiopathic Parkinson's disease (PD) benefit from Rhythmic Auditory Stimulation (RAS) concerning gait impairment recovery. In PD, RAS may help eliciting rhythmic and automatized motor responses, including gait, by bypassing the deteriorated internal "clock" within basal ganglia for automatic and rhythmic motricity. We aimed at exploring the contribution of the cerebellum to this "bypass effect" in response to RAS. To this end, we examined the cerebellum-cerebral connectivity indices using conventional EEG recording to assess whether the cerebellum contributes to RAS-based post-training effects in persons with PD. Fifty PD patients were randomly assigned to an 8-week training program using Gait-Trainer3 with or without RAS. We measured the Functional Gait Assessment, the Unified Parkinson's Disease Rating Scale, the Berg Balance Scale, the Tinetti Falls Efficacy Scale, the 10-meter walking test, the timed up-and-go test, and the gait quality index derived from gait analysis before and after the end of the training. A standard EEG during gait on the GT3 was also recorded and submitted to eLORETA analysis. Particularly, we focused on the time course of the gait-related activities (which were characterized using the maximum amplitude vertex across the gait cycles) within each brain region of interest. These clinical and electrophysiological measures could be used to monitor the improvement in gait performance in standard clinical settings and to develop new rehabilitation protocols focusing on a holistic functional recovery approach.

How Functional Connectivity Measures Affect the Outcomes of Global Neuronal Network Characteristics in Patients with Schizophrenia Compared to Healthy Controls

Modern computational solutions used in the reconstruction of the global neuronal network arrangement seem to be particularly valuable for research on neuronal disconnection in schizophrenia. However, the vast number of algorithms used in these analyses may be an uncontrolled source of result inconsistency. Our study aimed to verify to what extent the characteristics of the global network organization in schizophrenia depend on the inclusion of a given type of functional connectivity measure. Resting-state EEG recordings from schizophrenia patients and healthy controls were collected. Based on these data, two identical procedures of graph-theory-based network arrangements were computed twice using two different functional connectivity measures (phase lag index, PLI, and phase locking value, PLV). Two series of between-group comparisons regarding global network parameters calculated on the basis of PLI or PLV gave contradictory results. In many cases, the values of a given network index based on PLI were higher in the patients, and the results based on PLV were lower in the patients than in the controls. Additionally, selected network measures were significantly different within the patient group when calculated from PLI or PLV. Our analysis shows that the selection of FC measures significantly affects the parameters of graph-theory-based neuronal network organization and might be an important source of disagreement in network studies on schizophrenia.

Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography

Virtual reality (VR), a rapidly evolving technology that simulates three-dimensional virtual environments for users, has been proven to activate brain functions. However, the continuous alteration pattern of the functional small-world network in response to comprehensive three-dimensional stimulation rather than realistic two-dimensional media stimuli requires further exploration. Here, we aimed to validate the effect of VR on the pathways and network parameters of a small-world organization and interpret its mechanism of action. Fourteen healthy volunteers were selected to complete missions in an immersive VR game. The changes in the functional network in six different frequency categories were analyzed using graph theory with electroencephalography data measured during the pre-, VR, and post-VR stages. The mutual information matrix revealed that interactions between the frontal and posterior areas and those within the frontal and occipital lobes were strengthened. Subsequently, the betweenness centrality (BC) analysis indicated more robust and extensive pathways among hubs. Furthermore, a specific lateralized channel (O1 or O2) increment in the BC was observed. Moreover, the network parameters improved simultaneously in local segregation, global segregation, and global integration. The overall topological improvements of small-world organizations were in high-frequency bands and exhibited some degree of sustainability.

Neurophysiological and Behavioral Differences in Human-Multiagent Tasks: An EEG Network Perspective

Effective human-multiagent teams will incorporate the cognitive skills of the human with the autonomous capabilities of the multiagent group to maximize task performance. However, producing a seamless fusion requires a greater understanding of the human’s cognitive state as it reacts to uncertainties in both the task environment and agent dynamics. This study examines external behaviors in concert with neurophysiological measures acquired via electroencephalography (EEG) to probe the interactions between cognitive processes, behaviors, and performance in a human-multiagent team task. We show that changes in the α (8-12Hz) and θ (4-8Hz) bands of EEG indicate a higher burden on the cognitive resources associated with visual-spatial reasoning required to estimate a more complex kinematic state of robotic agents. These results are reinforced by complementary behavioral shifts in gaze and pilot inputs. Additionally, higher performing participants tend to engage more actively in the task by utilizing greater amounts of visual-spatial reasoning. Finally, we show that features based on EEG dynamic-network-metrics provide discriminative information that distinguish gaze behaviors associated with the attention process.

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

A Novel Technique to Detect False Data Injection Attacks on Phasor Measurement Units

The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these technologies enable more reliable and efficient operation, the risk of cyber threats has increased, as evidenced by the recent blackouts in Ukraine and New York. One of these threats is false data injection attacks (FDIAs). Most of the FDIA literature focuses on the vulnerability of DC estimators and AC estimators to such attacks. This paper investigates FDIAs for PMU-based state estimation, where the PMUs are comparable. Several states can be manipulated by compromising one PMU through the channels of that PMU. A Phase Locking Value (PLV) technique was developed to detect FDIAs. The proposed approach is tested on the IEEE 14-bus and the IEEE 30-bus test systems under different scenarios using a Monte Carlo simulation where the PLV demonstrated an efficient performance.