Alpha coherence predicts accuracy during a visuomotor tracking task

High gamma coherence between task-responsive sensory-motor cortical regions in a motor reaction-time task

Electrical activity at high gamma frequencies (70-170 Hz) is thought to reflect the activity of small cortical ensembles. For example, high gamma activity (often quantified by spectral power) can increase in sensory-motor cortex in response to sensory stimuli or movement. On the other hand, synchrony of neural activity between cortical areas (often quantified by coherence) has been hypothesized as an important mechanism for inter-areal communication, thereby serving functional roles in cognition and behavior. Currently, high gamma activity has primarily been studied as a local amplitude phenomenon. We investigated the synchronization of high gamma activity within sensory-motor cortex and the extent to which underlying high gamma activity can explain coherence during motor tasks. We characterized high gamma coherence in sensory-motor networks and the relationship between coherence and power by analyzing electrocorticography (ECoG) data from human subjects as they performed a motor response to sensory cues. We found greatly increased high gamma coherence during the motor response compared with the sensory cue. High gamma power poorly predicted high gamma coherence, but the two shared a similar time course. However, high gamma coherence persisted longer than high gamma power. The results of this study suggest that high gamma coherence is a physiologically distinct phenomenon during a sensory-motor task, the emergence of which may require active task participation.NEW & NOTEWORTHY Motor action after auditory stimulus elicits high gamma responses in sensory-motor and auditory cortex, respectively. We show that high gamma coherence reliably and greatly increased during motor response, but not after auditory stimulus. Underlying high gamma power could not explain high gamma coherence. Our results indicate that high gamma coherence is a physiologically distinct sensory-motor phenomenon that may serve as an indicator of increased synaptic communication on short timescales (∼1 s).

Stereoptic serious games as a visual rehabilitation tool for individuals with a residual amblyopia (AMBER trial): a protocol for a crossover randomized controlled trial

BACKGROUND

Amblyopia is the most common developmental vision disorder in children. The initial treatment consists of refractive correction. When insufficient, occlusion therapy may further improve visual acuity. However, the challenges and compliance issues associated with occlusion therapy may result in treatment failure and residual amblyopia. Virtual reality (VR) games developed to improve visual function have shown positive preliminary results. The aim of this study is to determine the efficacy of these games to improve vision, attention, and motor skills in patients with residual amblyopia and identify brain-related changes. We hypothesize that a VR-based training with the suggested ingredients (3D cues and rich feedback), combined with increasing the difficulty level and the use of various games in a home-based environment is crucial for treatment efficacy of vision recovery, and may be particularly effective in children.

METHODS

The AMBER study is a randomized, cross-over, controlled trial designed to assess the effect of binocular stimulation (VR-based stereoptic serious games) in individuals with residual amblyopia (n = 30, 6-35 years of age), compared to refractive correction on vision, selective attention and motor control skills. Additionally, they will be compared to a control group of age-matched healthy individuals (n = 30) to account for the unique benefit of VR-based serious games. All participants will play serious games 30 min per day, 5 days per week, for 8 weeks. The games are delivered with the Vivid Vision Home software. The amblyopic cohort will receive both treatments in a randomized order according to the type of amblyopia, while the control group will only receive the VR-based stereoscopic serious games. The primary outcome is visual acuity in the amblyopic eye. Secondary outcomes include stereoacuity, functional vision, cortical visual responses, selective attention, and motor control. The outcomes will be measured before and after each treatment with 8-week follow-up.

DISCUSSION

The VR-based games used in this study have been conceived to deliver binocular visual stimulation tailored to the individual visual needs of the patient, which will potentially result in improved basic and functional vision skills as well as visual attention and motor control skills.

TRIAL REGISTRATION

This protocol is registered on ClinicalTrials.gov (identifier: NCT05114252) and in the Swiss National Clinical Trials Portal (identifier: SNCTP000005024).

Binary Controller Based on the Electrical Activity Related to Head Yaw Rotation

A human machine interface (HMI) is presented to switch on/off lights according to the head left/right yaw rotation. The HMI consists of a cap, which can acquire the brain’s electrical activity (i.e., an electroencephalogram, EEG) sampled at 500 Hz on 8 channels with electrodes that are positioned according to the standard 10–20 system. In addition, the HMI includes a controller based on an input–output function that can compute the head position (defined as left, right, and forward position with respect to yaw angle) considering short intervals (10 samples) of the signals coming from three electrodes positioned in O1, O2, and Cz. An artificial neural network (ANN) training based on a Levenberg–Marquardt backpropagation algorithm was used to identify the input–output function. The HMI controller was tested on 22 participants. The proposed classifier achieved an average accuracy of 88% with the best value of 96.85%. After calibration for each specific subject, the HMI was used as a binary controller to verify its ability to switch on/off lamps according to head turning movement. The correct prediction of the head movements was greater than 75% in 90% of the participants when performing the test with open eyes. If the subjects carried out the experiments with closed eyes, the prediction accuracy reached 75% of correctness in 11 participants out of 22. One participant controlled the light system in both experiments, open and closed eyes, with 100% success. The control results achieved in this work can be considered as an important milestone towards humanoid neck systems.

Using EEG to study sensorimotor adaptation

Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.

Cortical Stimulation Paired With Volitional Unimanual Movement Affects Interhemispheric Communication

Cortical stimulation (CS) of the motor cortex can cause excitability changes in both hemispheres, showing potential to be a technique for clinical rehabilitation of motor function. However, previous studies that have investigated the effects of delivering CS during movement typically focus on a single hemisphere. On the other hand, studies exploring interhemispheric interactions typically deliver CS at rest. We sought to bridge these two approaches by documenting the consequences of delivering CS to a single motor cortex during different phases of contralateral and ipsilateral limb movement, and simultaneously assessing changes in interactions within and between the hemispheres via local field potential (LFP) recordings. Three macaques were trained in a unimanual reaction time (RT) task and implanted with epidural or intracortical electrodes over bilateral motor cortices. During a given session CS was delivered to one hemisphere with respect to movements of either the contralateral or ipsilateral limb. Stimulation delivered before contralateral limb movement onset shortened the contralateral limb RT. In contrast, stimulation delivered after the end of contralateral movement increased contralateral RT but decreased ipsilateral RT. Stimulation delivered before ipsilateral limb movement decreased ipsilateral RT. All other stimulus conditions as well as random stimulation and periodic stimulation did not have consistently significant effects on either limb. Simultaneous LFP recordings from one animal revealed correlations between changes in interhemispheric alpha band coherence and changes in RT, suggesting that alpha activity may be indicative of interhemispheric communication. These results show that changes caused by CS to the functional coupling within and between precentral cortices is contingent on the timing of CS relative to movement.

Identification of Brain Electrical Activity Related to Head Yaw Rotations

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

Midfrontal theta as moderator between beta oscillations and precision control

Control of movements using visual information is crucial for many daily activities, and such visuomotor control has been revealed to be supported by alpha and beta cortical oscillations. However, it has been remained to be unclear how midfrontal theta and occipital gamma oscillations, which are associated with high-level cognitive functions, would be involved in this process to facilitate performance. Here we addressed this fundamental open question in healthy young adults by measuring high-density cortical activity during a precision force-matching task. We manipulated the amount of error by changing visual feedback gain (low, medium, and high visual gains) and analyzed event-related spectral perturbations. Increasing the visual feedback gain resulted in a decrease in force error and variability. There was an increase in theta synchronization in the midfrontal area and also in beta desynchronization in the sensorimotor and posterior parietal areas with higher visual feedback gains. Gamma de/synchronization was not evident during the task. In addition, we found a moderation effect of midfrontal theta on the positive relationship between the beta oscillations and force error. Subsequent simple slope analysis indicated that the effect of beta oscillations on force error was weaker when midfrontal theta was high. Our findings suggest that the midfrontal area signals the increased need of cognitive control to refine behavior by modulating the visuomotor processing at theta frequencies.

EEG dynamical network analysis method reveals the neural signature of visual-motor coordination

Human visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is still largely unknown. In this study, we applied an eigenvector-based dynamical network analysis method to investigate the functional connectivity calculated from electroencephalography (EEG) signals under visual-motor coordination task and to identify its meta-stable states dynamics. We first tested this signal processing on a simulated network to evaluate it in comparison with other dynamical methods, demonstrating that the eigenvector-based dynamical network analysis was able to correctly extract the dynamical features of the evolving networks. Subsequently, the eigenvector-based analysis was applied to EEG data collected under a visual-motor coordination experiment. In the EEG study with participants, the results of both topological analysis and the eigenvector-based dynamical analysis were able to distinguish different experimental conditions of visual tracking task. With the dynamical analysis, we showed that different visual-motor coordination states can be distinguished by investigating the meta-stable states dynamics of the functional connectivity.

Acute Exercise as an Intervention to Trigger Motor Performance and EEG Beta Activity in Older Adults

Acute bouts of exercise have been shown to improve fine motor control performance and to facilitate motor memory consolidation processes in young adults. Exercise effects might be reflected in EEG task-related power (TRPow) decreases in the beta band (13-30 Hz) as an indicator of active motor processing. This study aimed to investigate those effects in healthy older adults. Thirty-eight participants (65-74 years of age) were assigned to an experimental (EG, acute exercise) or a control group (CG, rest). Fine motor control was assessed using a precision grip force modulation (FM) task. FM performance and EEG were measured at (1) baseline (immediately before acute exercise/rest), (2) during practice sessions immediately after, (3) 30 minutes, and (4) 24 hours (FM only) after exercise/rest. A marginal significant effect indicated that EG revealed more improvement in fine motor performance immediately after exercise than CG after resting. EG showed enhanced consolidation of short-term and long-term motor memory, whereas CG revealed only a tendency for short-term motor memory consolidation. Stronger TRPow decreases were revealed immediately after exercise in the contralateral frontal brain area as compared to the control condition. This finding indicates that acute exercise might enhance cortical activation and thus, improves fine motor control by enabling healthy older adults to better utilize existing frontal brain capacities during fine motor control tasks after exercise. Furthermore, acute exercise can act as a possible intervention to enhance motor memory consolidation in older adults.

Increased Alpha Band Functional Connectivity Following the Quadrato Motor Training: A Longitudinal Study

Quadrato Motor Training (QMT) is a new training paradigm, which was found to increase cognitive flexibility, creativity and spatial cognition. In addition, QMT was reported to enhance inter- and intra-hemispheric alpha coherence as well as Fractional Anisotropy (FA) in a number of white matter pathways including corpus callosum. Taken together, these results seem to suggest that electrophysiological and structural changes induced by QMT may be due to an enhanced interplay and communication of the different brain areas within and between the right and the left hemisphere. In order to test this hypothesis using the exact low-resolution brain electromagnetic tomography (eLORETA), we estimated the current neural density and lagged linear connectivity (LLC) of the alpha band in the resting state electroencephalography (rsEEG) recorded with open (OE) and closed eyes (CE) at three different time points, following 6 and 12 weeks of daily QMT. Significant changes were observed for the functional connectivity. In particular, we found that limbic and fronto-temporal alpha connectivity in the OE condition increased after 6 weeks, while it enhanced at the CE condition in occipital network following 12-weeks of daily training. These findings seem to show that the QMT may have dissociable long-term effects on the functional connectivity depending on the different ways of recording rsEEG. OE recording pointed out a faster onset of Linear Lag Connectivity modulations that tend to decay as quickly, while CE recording showed sensible effect only after the complete 3-months training.

A multivariate method for estimating cross-frequency neuronal interactions and correcting linear mixing in MEG data, using canonical correlations

BACKGROUND

Cross-frequency interactions between distinct brain areas have been observed in connection with a variety of cognitive tasks. With electro- and magnetoencephalography (EEG/MEG) data, typical connectivity measures between two brain regions analyze a single quantity from each region within a specific frequency band; given the wideband nature of EEG/MEG signals, many statistical tests may be required to identify true coupling. Furthermore, because of the poor spatial resolution of activity reconstructed from EEG/MEG, some interactions may actually be due to the linear mixing of brain sources.

NEW METHOD

In the present work, a method for the detection of cross-frequency functional connectivity in MEG data using canonical correlation analysis (CCA) is described. We demonstrate that CCA identifies correlated signals and also the frequencies that cause the correlation. We also implement a procedure to deal with linear mixing based on symmetry properties of cross-covariance matrices.

RESULTS

Our tests with both simulated and real MEG data demonstrate that CCA is able to detect interacting locations and the frequencies that cause them, while accurately discarding spurious coupling.

COMPARISON WITH EXISTING METHODS

Recent techniques look at time delays in the activity between two locations to discard spurious interactions, while we propose a linear mixing model and demonstrate its relationship with symmetry aspects of cross-covariance matrices.

CONCLUSIONS

Our tests indicate the benefits of the CCA approach in connectivity studies, as it allows the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single statistical test.

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.

Characterization of Cortical Networks and Corticocortical Functional Connectivity Mediating Arbitrary Visuomotor Mapping

Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Although the sensorimotor and associative frontostriatal circuits are known to mediate arbitrary visuomotor mappings, the underlying corticocortico dynamics remain elusive. Here, we take a novel approach exploiting gamma-band neural activity to study the human cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping. Single-trial gamma-power time courses were estimated for all Brodmann areas by combing magnetoencephalographic and MRI data with spectral analysis and beam-forming techniques. Linear correlation and Granger causality analyses were performed to investigate functional connectivity between cortical regions. The performance of visuomotor associations was characterized by an increase in gamma-power and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal area played a driving role in the network, exerting Granger causality on the dorsal premotor area. Premotor areas acted as relay from parietal to medial prefrontal cortices, which played a receiving role in the network. Link community analysis further revealed that visuomotor mappings reflect the coordination of multiple subnetworks with strong overlap over motor and frontoparietal areas. We put forward an associative account of the underlying cognitive processes and corticocortical functional connectivity. Overall, our approach and results provide novel perspectives toward a better understanding of how distributed brain activity coordinates adaptive behaviors.

SIGNIFICANCE STATEMENT

In everyday life, most of our behaviors are based on the arbitrary linkage of sensory information to actions and goals, such as stopping at a red traffic light. Despite their automaticity, such behaviors rely on the activity of a large brain network and elusive interareal functional connectivity. We take a novel approach exploiting noninvasive recordings of human brain activity to reveal the cortical networks and corticocortical functional connectivity mediating visuomotor mappings. Parietal areas were found to play a driving role in the network, whereas premotor areas acted as relays from parietal to medial prefrontal cortices, which played a receiving role. Overall, our approach and results provide novel perspectives toward a better understanding of how distributed brain activity coordinates adaptive behaviors.

Brain Networks Responsible for Sense of Agency: An EEG Study

BACKGROUND

Self-agency (SA) is a person's feeling that his action was generated by himself. The neural substrates of SA have been investigated in many neuroimaging studies, but the functional connectivity of identified regions has rarely been investigated. The goal of this study is to investigate the neural network related to SA.

METHODS

SA of hand movements was modulated with virtual reality. We examined the cortical network relating to SA modulation with electroencephalography (EEG) power spectrum and phase coherence of alpha, beta, and gamma frequency bands in 16 right-handed, healthy volunteers.

RESULTS

In the alpha band, significant relative power changes and phase coherence of alpha band were associated with SA modulation. The relative power decrease over the central, bilateral parietal, and right temporal regions (C4, Pz, P3, P4, T6) became larger as participants more effectively controlled the virtual hand movements. The phase coherence of the alpha band within frontal areas (F7-FP2, F7-Fz) was directly related to changes in SA. The functional connectivity was lower as the participants felt that they could control their virtual hand. In the other frequency bands, significant phase coherences were observed in the frontal (or central) to parietal, temporal, and occipital regions during SA modulation (Fz-O1, F3-O1, Cz-O1, C3-T4L in beta band; FP1-T6, FP1-O2, F7-T4L, F8-Cz in gamma band).

CONCLUSIONS

Our study suggests that alpha band activity may be the main neural oscillation of SA, which suggests that the neural network within the anterior frontal area may be important in the generation of SA.

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.

Rate control and quality assurance during rhythmic force tracking

Movement characteristics can be coded in the single neurons or in the summed activity of neural populations. However, whether neural oscillations are conditional to the frequency demand and task quality of rhythmic force regulation is still unclear. This study was undertaken to investigate EEG dynamics and behavior correlates during force-tracking at different target rates. Fourteen healthy volunteers conducted load-varying isometric abduction of the index finger by coupling the force output to sinusoidal targets at 0.5 Hz, 1.0 Hz, and 2.0 Hz. Our results showed that frequency demand significantly affected EEG delta oscillation (1-4 Hz) in the C3, CP3, CPz, and CP4 electrodes, with the greatest delta power and lowest delta peak around 1.5 Hz for slower tracking at 0.5 Hz. Those who had superior tracking congruency also manifested enhanced alpha oscillation (8-12 Hz). Alpha rhythms of the skilled performers during slow tracking spread through the whole target cycle, except for the phase of direction changes. However, the alpha rhythms centered at the mid phase of a target cycle with increasing target rate. In conclusion, our findings clearly suggest two advanced roles of cortical oscillation in rhythmic force regulation. Rate-dependent delta oscillation involves a paradigm shift in force control under different time scales. Phasic organization of alpha rhythms during rhythmic force tracking is related to behavioral success underlying the selective use of bimodal controls (feedback and feedforward processes) and the timing of attentional focus on the target's peak velocity.

Subthalamic nucleus stimulation restores the efferent cortical drive to muscle in parallel to functional motor improvement

Pathological synchronization in large-scale motor networks constitutes a pathophysiological hallmark of Parkinson's disease (PD). Corticomuscular synchronization in PD is pronounced in lower frequency bands (< 10 Hz), whereas efficient cortical motor integration in healthy persons is driven in the beta frequency range. Electroencephalogram and electromyogram recordings at rest and during an isometric precision grip task were performed in four perioperative sessions in 10 patients with PD undergoing subthalamic nucleus deep-brain stimulation: (i) 1 day before (D0); (ii) 1 day after (D1); (iii) 8 days after implantation of macroelectrodes with stimulation off (D8StimOff); and (iv) on (D8StimOn). Analyses of coherence and phase delays were performed in order to challenge the effects of microlesion and stimulation on corticomuscular coherence (CMC). Additionally, local field potentials recorded from the subthalamic nucleus on D1 allowed comprehensive mapping of motor-related synchronization in subthalamocortical and cerebromuscular networks. Motor performance improved at D8StimOn compared with D0 and D8StimOff paralleled by a reduction of muscular activity and CMC in the theta band (3.9-7.8 Hz) and by an increase of CMC in the low-beta band (13.7-19.5 Hz). Efferent motor cortical drives to muscle presented mainly below 10 Hz on D8StimOff that were suppressed on D8StimOn and occurred on higher frequencies from 13 to 45 Hz. On D1, coherence of the high-beta band (20.5-30.2 Hz) increased during movement compared with rest in subthalamomuscular and corticomuscular projections, whereas it was attenuated in subcorticocortical projections. The present findings lend further support to the concept of pathological network synchronization in PD that is beneficially modulated by stimulation.