Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction

The metacontrol of event segmentation-A neurophysiological and behavioral perspective

During our everyday life, the constant flow of information is divided into discrete events, a process conceptualized in Event Segmentation Theory (EST). How people perform event segmentation and the resulting granularity of encapsulated segments likely depends on their metacontrol style. Yet, the underlying neural mechanisms remain undetermined. The current study examines how the metacontrol style affects event segmentation through the analysis of EEG data using multivariate pattern analysis (MVPA) and source localization analysis. We instructed two groups of healthy participants to either segment a movie as fine-grained as possible (fine-grain group) or provided no such instruction (free-segmentation group). The fine-grain group showed more segments and a higher likelihood to set event boundaries upon scene changes, which supports the notion that cognitive control influences segmentation granularity. On a neural level, representational dynamics were decodable 400 ms prior to the decision to close a segment and open a new one, and especially fronto-polar regions (BA10) were associated with this representational dynamic. Groups differed in their use of this representational dynamics to guide behavior and there was a higher sensitivity to incoming information in the Fine-grain group. Moreover, a higher likelihood to set event boundaries was reflected by activity increases in the insular cortex suggesting an increased monitoring of potentially relevant upcoming events. The study connects the EST with the metacontrol framework and relates these to overarching neural concepts of prefrontal cortex function.

Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Simultaneous MEG and EEG source imaging of electrophysiological activity in response to acute transcranial photobiomodulation

Introduction

Transcranial photobiomodulation (tPBM) is a non-invasive neuromodulation technique that improves human cognition. The effects of tPBM of the right forehead on neurophysiological activity have been previously investigated using EEG in sensor space. However, the spatial resolution of these studies is limited. Magnetoencephalography (MEG) is known to facilitate a higher spatial resolution of brain source images. This study aimed to image post-tPBM effects in brain space based on both MEG and EEG measurements across the entire human brain.

Methods

MEG and EEG scans were concurrently acquired for 6 min before and after 8-min of tPBM delivered using a 1,064-nm laser on the right forehead of 25 healthy participants. Group-level changes in both the MEG and EEG power spectral density with respect to the baseline (pre-tPBM) were quantified and averaged within each frequency band in the sensor space. Constrained modeling was used to generate MEG and EEG source images of post-tPBM, followed by cluster-based permutation analysis for family wise error correction (p < 0.05).

Results

The 8-min tPBM enabled significant increases in alpha (8-12 Hz) and beta (13-30 Hz) powers across multiple cortical regions, as confirmed by MEG and EEG source images. Moreover, tPBM-enhanced oscillations in the beta band were located not only near the stimulation site but also in remote cerebral regions, including the frontal, parietal, and occipital regions, particularly on the ipsilateral side.

Discussion

MEG and EEG results shown in this study demonstrated that tPBM modulates neurophysiological activity locally and in distant cortical areas. The EEG topographies reported in this study were consistent with previous observations. This study is the first to present MEG and EEG evidence of the electrophysiological effects of tPBM in the brain space, supporting the potential utility of tPBM in treating neurological diseases through the modulation of brain oscillations.

Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex

Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.

Comparing the performance of beamformer algorithms in estimating orientations of neural sources

The efficacy of transcranial electric stimulation (tES) to effectively modulate neuronal activity depends critically on the spatial orientation of the targeted neuronal population. Therefore, precise estimation of target orientation is of utmost importance. Different beamforming algorithms provide orientation estimates; however, a systematic analysis of their performance is still lacking. For fixed brain locations, EEG and MEG data from sources with randomized orientations were simulated. The orientation was then estimated (1) with an EEG and (2) with a combined EEG-MEG approach. Three commonly used beamformer algorithms were evaluated with respect to their abilities to estimate the correct orientation: Unit-Gain (UG), Unit-Noise-Gain (UNG), and Array-Gain (AG) beamformer. Performance depends on the signal-to-noise ratios for the modalities and on the chosen beamformer. Overall, the UNG and AG beamformers appear as the most reliable. With increasing noise, the UG estimate converges to a vector determined by the leadfield, thus leading to insufficient orientation estimates.

Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions

OBJECTIVE

To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy.

METHODS

Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation.

RESULTS

The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients.

CONCLUSIONS

Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data.

SIGNIFICANCE

The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.

Functional connectivity via the dorsolateral prefrontal cortex in the late phase of rest periods predicts offline learning

The relationship between offline learning gains and functional connectivity (FC) has been investigated in several studies. They have focused on average motor task performance and resting-state FC across subjects. Generally, individual differences are seen in both offline learning gain and neurophysiological profiles in resting-state FC. However, few studies have focused on the relationship between individual differences in offline learning gain and temporal characteristics of resting-state FC. The present study aimed to clarify this relationship between the two profiles. Thirty-four healthy right-handed participants performed a force-controlled motor task. Electroencephalography was performed during the 15-minute wakeful rest period between tasks. The results revealed a significant correlation between offline learning gain and FC between the contralateral dorsolateral prefrontal cortex (DLPFC) and contralateral primary motor cortex (M1), and ipsilateral primary somatosensory cortex (S1) during late phase of the rest interval. These results are consistent with the findings of previous studies showing the FC between M1, which is necessary for awake offline learning, and DLPFC, which is related to motor control. Additionally, sensory feedback related to force control may be caused by the interaction between contralateral DLPFC and ipsilateral S1. Our study shed light on the temporal profiles of resting-state FC associated with individual differences in offline learning.

Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition

Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best predict whether an individual is confronted with a situation requiring the inhibition of a response. This is particularly the case when considering the complex spatio-temporal nature of neural processes captured by EEG data. In the current study, we ask whether independent spatial activity profiles in the EEG data are useful to predict whether an individual is confronted with a situation requiring response inhibition. We combine independent component analysis (ICA) with explainable artificial intelligence approaches (EEG-based deep learning) using data from a Go/Nogo task (N = 255 participants). We show that there are four dissociable spatial activity profiles important to classify Go and Nogo trials as revealed by deep learning. Of note, for all of these four independent activity profiles, neural activity in the time period between 300 and 550 ms after stimulus presentation was most informative. Source localization analyses further revealed regions in the pre-central gyrus (BA6), the middle frontal gyrus (BA10), the inferior frontal gyrus (BA46), and the insular cortex (BA13) were associated with the isolated spatial activity profiles. The data suggest concomitant processes being reflected in the identified time window. This has implications for the ongoing debate on the functional significance of event-related potential correlates of inhibitory control.

The neurophysiology of continuous action monitoring

Monitoring actions is essential for goal-directed behavior. However, as opposed to short-lasting, and regularly reinstating monitoring functions, the neural processes underlying continuous action monitoring are poorly understood. We investigate this using a pursuit-tracking paradigm. We show that beta band activity likely maintains the sensorimotor program, while theta and alpha bands probably support attentional sampling and information gating, respectively. Alpha and beta band activity are most relevant during the initial tracking period, when sensorimotor calibrations are most intense. Theta band shifts from parietal to frontal cortices throughout tracking, likely reflecting a shift in the functional relevance from attentional sampling to action monitoring. This study shows that resource allocation mechanisms in prefrontal areas and stimulus-response mapping processes in the parietal cortex are crucial for adapting sensorimotor processes. It fills a knowledge gap in understanding the neural processes underlying action monitoring and suggests new directions for examining sensorimotor integration in more naturalistic experiments.

Neurophysiological mechanisms underlying the differential effect of reward prospect on response selection and inhibition

Reward and cognitive control play crucial roles in shaping goal-directed behavior. Yet, the behavioral and neural underpinnings of interactive effects of both processes in driving our actions towards a particular goal have remained rather unclear. Given the importance of inhibitory control, we investigated the effect of reward prospect on the modulatory influence of automatic versus controlled processes during response inhibition. For this, a performance-contingent monetary reward for both correct response selection and response inhibition was added to a Simon NoGo task, which manipulates the relationship of automatic and controlled processes in Go and NoGo trials. A neurophysiological approach was used by combining EEG temporal signal decomposition and source localization methods. Compared to a non-rewarded control group, rewarded participants showed faster response execution, as well as overall lower response selection and inhibition accuracy (shifted speed-accuracy tradeoff). Interestingly, the reward group displayed a larger interference of the interactive effects of automatic versus controlled processes during response inhibition (i.e., a larger Simon NoGo effect), but not during response selection. The reward-specific behavioral effect was mirrored by the P3 amplitude, underlining the importance of stimulus-response association processes in explaining variability in response inhibition performance. The selective reward-induced neurophysiological modulation was associated with lower activation differences in relevant structures spanning the inferior frontal and parietal cortex, as well as higher activation differences in the somatosensory cortex. Taken together, this study highlights relevant neuroanatomical structures underlying selective reward effects on response inhibition and extends previous reports on the possible detrimental effect of reward-triggered performance trade-offs on cognitive control processes.

Sensor array design of optically pumped magnetometers for accurately estimating source currents

An optically pumped magnetometer (OPM) is a new generation of magnetoencephalography (MEG) devices that is small, light, and works at room temperature. Due to these characteristics, OPMs enable flexible and wearable MEG systems. On the other hand, if we have a limited number of OPM sensors, we need to carefully design their sensor arrays depending on our purposes and regions of interests (ROIs). In this study, we propose a method that designs OPM sensor arrays for accurately estimating the cortical currents at the ROIs. Based on the resolution matrix of minimum norm estimate (MNE), our method sequentially determines the position of each sensor to optimize its inverse filter pointing to the ROIs and suppressing the signal leakage from the other areas. We call this method the Sensor array Optimization based on Resolution Matrix (SORM). We conducted simple and realistic simulation tests to evaluate its characteristics and efficacy for real OPM-MEG data. SORM designed the sensor arrays so that their leadfield matrices had high effective ranks as well as high sensitivities to ROIs. Although SORM is based on MNE, the sensor arrays designed by SORM were effective not only when we estimated the cortical currents by MNE but also when we did so by other methods. With real OPM-MEG data we confirmed its validity for real data. These analyses suggest that SORM is especially useful when we want to accurately estimate ROIs' activities with a limited number of OPM sensors, such as brain-machine interfaces and diagnosing brain diseases.

Visualization of axonal and volume currents in median nerve compound action potential using magnetoneurography

OBJECTIVE

Reconstruct compound median nerve action currents using magnetoneurography to clarify the physiological characteristics of axonal and volume currents and their relationship to potentials.

METHODS

The median nerves of both upper arms of five healthy individuals were investigated. The propagating magnetic field of the action potential was recorded using magnetoneurography, reconstructed into a current, and analyzed. The currents were compared with the potentials recorded from multipolar surface electrodes.

RESULTS

Reconstructed currents could be clearly visualized. Axonal currents flowed forward or backward in the axon, arcing away from the depolarization zone, turning about the subcutaneous volume conductor, and returning to the depolarization zone. The zero-crossing latency of the axonal current was approximately the same as the peak of its volume current and the negative peak of the surface electrode potential. Volume current waveforms were proportional to the derivative of axonal ones.

CONCLUSIONS

Magnetoneurography allows the visualization and quantitative evaluation of action currents. The currents in axons and in volume conductors could be clearly discriminated with good quality. Their properties were consistent with previous neurophysiological findings.

SIGNIFICANCE

Magnetoneurography could be a novel tool for elucidating nerve physiology and pathophysiology.

Exploring the limits of MEG spatial resolution with multipolar expansions

The advent of scalp magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) may represent a step change in the field of human electrophysiology. Compared to cryogenic MEG based on superconducting quantum interference devices (SQUIDs, placed 2-4 cm above scalp), scalp MEG promises significantly higher spatial resolution imaging but it also comes with numerous challenges regarding how to optimally design OPM arrays. In this context, we sought to provide a systematic description of MEG spatial resolution as a function of the number of sensors (allowing comparison of low- vs. high-density MEG), sensor-to-brain distance (cryogenic SQUIDs vs. scalp OPM), sensor type (magnetometers vs. gradiometers; single- vs. multi-component sensors), and signal-to-noise ratio. To that aim, we present an analytical theory based on MEG multipolar expansions that enables, once supplemented with experimental input and simulations, quantitative assessment of the limits of MEG spatial resolution in terms of two qualitatively distinct regimes. In the regime of asymptotically high-density MEG, we provide a mathematically rigorous description of how magnetic field smoothness constraints spatial resolution to a slow, logarithmic divergence. In the opposite regime of low-density MEG, it is sensor density that constraints spatial resolution to a faster increase following a square-root law. The transition between these two regimes controls how MEG spatial resolution saturates as sensors approach sources of neural activity. This two-regime model of MEG spatial resolution integrates known observations (e.g., the difficulty of improving spatial resolution by increasing sensor density, the gain brought by moving sensors on scalp, or the usefulness of multi-component sensors) and gathers them under a unifying theoretical framework that highlights the underlying physics and reveals properties inaccessible to simulations. We propose that this framework may find useful applications to benchmark the design of future OPM-based scalp MEG systems.

In silicostudy of the effects of cerebral circulation on source localization using a dynamical anatomical atlas of the human head

Objective.This study focuses on the effects of dynamical vascular modeling on source localization errors in electroencephalography (EEG). Our aim of thisin silicostudy is to (a) find out the effects of cerebral circulation on the accuracy of EEG source localization estimates, and (b) evaluate its relevance with respect to measurement noise and interpatient variation.Approach.We employ a four-dimensional (3D + T) statistical atlas of the electrical properties of the human head with a cerebral circulation model to generate virtual patients with different cerebral circulatory conditions for EEG source localization analysis. As source reconstruction techniques, we use the linearly constraint minimum variance (LCMV) beamformer, standardized low-resolution brain electromagnetic tomography (sLORETA), and the dipole scan (DS).Main results.Results indicate that arterial blood flow affects source localization at different depths and with varying significance. The average flow rate plays an important role in source localization performance, while the pulsatility effects are very small. In cases where a personalized model of the head is available, blood circulation mismodeling causes localization errors, especially in the deep structures of the brain where the main cerebral arteries are located. When interpatient variations are considered, the results show differences up to 15 mm for sLORETA and LCMV beamformer and 10 mm for DS in the brainstem and entorhinal cortices regions. In regions far from the main arteries vessels, the discrepancies are smaller than 3 mm. When measurement noise is added and interpatient differences are considered in a deep dipolar source, the results indicate that the effects of conductivity mismatch are detectable even for moderate measurement noise. The signal-to-noise ratio limit for sLORETA and LCMV beamformer is 15 dB, while the limit is under 30 dB for DS.Significance.Localization of the brain activity via EEG constitutes an ill-posed inverse problem, where any modeling uncertainty, e.g. a slight amount of noise in the data or material parameter discrepancies, can lead to a significant deviation of the estimated activity, especially in the deep structures of the brain. Proper modeling of the conductivity distribution is necessary in order to obtain an appropriate source localization. In this study, we show that the conductivity of the deep brain structures is particularly impacted by blood flow-induced changes in conductivity because large arteries and veins access the brain through that region.

Source Localization of Somatosensory Neural Generators in Adults with Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, where differences are often present relating to the performance of motor skills. Our previous work elucidated unique event-related potential patterns of neural activity in those with ADHD when performing visuomotor and force-matching motor paradigms. The purpose of the current study was to identify whether there were unique neural sources related to somatosensory function and motor performance in those with ADHD. Source localization (sLORETA) software identified areas where neural activity differed between those with ADHD and neurotypical controls when performing a visuomotor tracing task and force-matching task. Median nerve somatosensory evoked potentials (SEPs) were elicited, while whole-head electroencephalography (EEG) was performed. sLORETA localized greater neural activity post-FMT in those with ADHD, when compared with their baseline activity (p < 0.05). Specifically, greater activity was exhibited in BA 31, precuneus, parietal lobe (MNI coordinates: X = -5, Y = -75, and Z = 20) at 156 ms post stimulation. No significant differences were found for any other comparisons. Increased activity within BA 31 in those with ADHD at post-FMT measures may reflect increased activation within the default mode network (DMN) or attentional changes, suggesting a unique neural response to the sensory processing of force and proprioceptive afferent input in those with ADHD when performing motor skills. This may have important functional implications for motor tasks dependent on similar proprioceptive afferent input.

Perception-action integration during inhibitory control is reflected in a concomitant multi-region processing of specific codes in the neurophysiological signal

The integration of perception and action has long been studied in psychological science using overarching cognitive frameworks. Despite these being very successful in explaining perception-action integration, little is known about its neurophysiological and especially the functional neuroanatomical foundations. It is unknown whether distinct brain structures are simultaneously involved in the processing of perception-action integration codes and also to what extent demands on perception-action integration modulate activities in these structures. We investigate these questions in an EEG study integrating temporal and ICA-based EEG signal decomposition with source localization. For this purpose, we used data from 32 healthy participants who performed a 'TEC Go/Nogo' task. We show that the EEG signal can be decomposed into components carrying different informational aspects or processing codes relevant for perception-action integration. Importantly, these specific codes are processed independently in different brain structures, and their specific roles during the processing of perception-action integration differ. Some regions (i.e., the anterior cingulate and insular cortex) take a 'default role' because these are not modulated in their activity by demands or the complexity of event file coding processes. In contrast, regions in the motor cortex, middle frontal, temporal, and superior parietal cortices were not activated by 'default' but revealed modulations depending on the complexity of perception-action integration (i.e., whether an event file has to be reconfigured). Perception-action integration thus reflects a multi-region processing of specific fractions of information in the neurophysiological signal. This needs to be taken into account when further developing a cognitive science framework detailing perception-action integration.

The role of visual association cortices during response selection processes in interference-modulated response stopping

Response inhibition and the ability to navigate distracting information are both integral parts of cognitive control and are imperative to adaptive behavior in everyday life. Thus far, research has only inconclusively been able to draw inferences regarding the association between response stopping and the effects of interfering information. Using a novel combination of the Simon task and a stop signal task, the current study set out to investigate the behavioral as well as the neurophysiological underpinnings of the relationship between response stopping and interference processing. We tested n = 27 healthy individuals and combined temporal EEG signal decomposition with source localization methods to delineate the precise neurophysiological dynamics and functional neuroanatomical structures associated with conflict effects on response stopping. The results showed that stopping performance was compromised by conflicts. Importantly, these behavioral effects were reflected by specific aspects of information coded in the neurophysiological signal, indicating that conflict effects during response stopping are not mediated via purely perceptual processes. Rather, it is the processing of specific, stop-relevant stimulus features in the sensory regions during response selection, which underlies the emergence of conflict effects in response stopping. The findings connect research regarding response stopping with overarching theoretical frameworks of perception-action integration.

Parietofrontal oscillations show hand-specific interactions with top-down movement plans

To generate a hand-specific reach plan, the brain must integrate hand-specific signals with the desired movement strategy. Although various neurophysiology/imaging studies have investigated hand-target interactions in simple reach-to-target tasks, the whole brain timing and distribution of this process remain unclear, especially for more complex, instruction-dependent motor strategies. Previously, we showed that a pro/anti pointing instruction influences magnetoencephalographic (MEG) signals in frontal cortex that then propagate recurrently through parietal cortex (Blohm G, Alikhanian H, Gaetz W, Goltz HC, DeSouza JF, Cheyne DO, Crawford JD. NeuroImage 197: 306-319, 2019). Here, we contrasted left versus right hand pointing in the same task to investigate 1) which cortical regions of interest show hand specificity and 2) which of those areas interact with the instructed motor plan. Eight bilateral areas, the parietooccipital junction (POJ), superior parietooccipital cortex (SPOC), supramarginal gyrus (SMG), medial/anterior interparietal sulcus (mIPS/aIPS), primary somatosensory/motor cortex (S1/M1), and dorsal premotor cortex (PMd), showed hand-specific changes in beta band power, with four of these (M1, S1, SMG, aIPS) showing robust activation before movement onset. M1, SMG, SPOC, and aIPS showed significant interactions between contralateral hand specificity and the instructed motor plan but not with bottom-up target signals. Separate hand/motor signals emerged relatively early and lasted through execution, whereas hand-motor interactions only occurred close to movement onset. Taken together with our previous results, these findings show that instruction-dependent motor plans emerge in frontal cortex and interact recurrently with hand-specific parietofrontal signals before movement onset to produce hand-specific motor behaviors.NEW & NOTEWORTHY The brain must generate different motor signals depending on which hand is used. The distribution and timing of hand use/instructed motor plan integration are not understood at the whole brain level. Using MEG we show that different action planning subnetworks code for hand usage and integrating hand use into a hand-specific motor plan. The timing indicates that frontal cortex first creates a general motor plan and then integrates hand specificity to produce a hand-specific motor plan.

On the Role of Stimulus-Response Context in Inhibitory Control in Alcohol Use Disorder

The behavioral and neural dynamics of response inhibition deficits in alcohol use disorder (AUD) are still largely unclear, despite them possibly being key to the mechanistic understanding of the disorder. Our study investigated the effect of automatic vs. controlled processing during response inhibition in participants with mild-to-moderate AUD and matched healthy controls. For this, a Simon Nogo task was combined with EEG signal decomposition, multivariate pattern analysis (MVPA), and source localization methods. The final sample comprised n = 59 (32♂) AUD participants and n = 64 (28♂) control participants. Compared with the control group, AUD participants showed overall better response inhibition performance. Furthermore, the AUD group was less influenced by the modulatory effect of automatic vs. controlled processes during response inhibition (i.e., had a smaller Simon Nogo effect). The neurophysiological data revealed that the reduced Simon Nogo effect in the AUD group was associated with reduced activation differences between congruent and incongruent Nogo trials in the inferior and middle frontal gyrus. Notably, the drinking frequency (but not the number of AUD criteria we had used to distinguish groups) predicted the extent of the Simon Nogo effect. We suggest that the counterintuitive advantage of participants with mild-to-moderate AUD over those in the control group could be explained by the allostatic model of drinking effects.

Evidence for independent representational contents in inhibitory control subprocesses associated with frontoparietal cortices

Inhibitory control processes have intensively been studied in cognitive science for the past decades. Even though the neural dynamics underlying these processes are increasingly better understood, a critical open question is how the representational dynamics of the inhibitory control processes are modulated when engaging in response inhibition in a relatively automatic or a controlled mode. Against the background of an overarching theory of perception-action integration, we combine temporal and spatial EEG signal decomposition methods with multivariate pattern analysis and source localization to obtain fine-grained insights into the neural dynamics of the representational content of response inhibition. For this purpose, we used a sample of N = 40 healthy adult participants. The behavioural data suggest that response inhibition was better in a more controlled than a more automated response execution mode. Regarding neural dynamics, effects of response inhibition modes relied on a concomitant coding of stimulus-related information and rules of how stimulus information is related to the appropriate motor programme. Crucially, these fractions of information, which are encoded at the same time in the neurophysiological signal, are based on two independent spatial neurophysiological activity patterns, also showing differences in the temporal stability of the representational content. Source localizations revealed that the precuneus and inferior parietal cortex regions are more relevant than prefrontal areas for the representation of stimulus-response selection codes. We provide a blueprint how a concatenation of EEG signal analysis methods, capturing distinct aspects of neural dynamics, can be connected to cognitive science theory on the importance of representations in action control.

A ventral stream-prefrontal cortex processing cascade enables working memory gating dynamics

The representation of incoming information, goals and the flexible processing of these are required for cognitive control. Efficient mechanisms are needed to decide when it is important that novel information enters working memory (WM) and when these WM 'gates' have to be closed. Compared to neural foundations of maintaining information in WM, considerably less is known about what neural mechanisms underlie the representational dynamics during WM gating. Using different EEG analysis methods, we trace the path of mental representations along the human cortex during WM gate opening and closing. We show temporally nested representational dynamics during WM gate opening and closing depending on multiple independent neural activity profiles. These activity profiles are attributable to a ventral stream-prefrontal cortex processing cascade. The representational dynamics start in the ventral stream during WM gate opening and WM gate closing before prefrontal cortical regions are modulated. A regional specific activity profile is shown within the prefrontal cortex depending on whether WM gates are opened or closed, matching overarching concepts of prefrontal cortex functions. The study closes an essential conceptual gap detailing the neural dynamics underlying how mental representations drive the WM gate to open or close to enable WM functions such as updating and maintenance.

Cognitive science theory-driven pharmacology elucidates the neurobiological basis of perception-motor integration

An efficient integration of sensory and motor processes is crucial to goal-directed behavior. Despite this high relevance, and although cognitive theories provide clear conceptual frameworks, the neurobiological basis of these processes remains insufficiently understood. In a double-blind, randomized placebo-controlled pharmacological study, we examine the relevance of catecholamines for perception-motor integration processes. Using EEG data, we perform an in-depth analysis of the underlying neurophysiological mechanisms, focusing on sensorimotor integration processes during response inhibition. We show that the catecholaminergic system affects sensorimotor integration during response inhibition by modulating the stability of the representational content. Importantly, catecholamine levels do not affect the stability of all aspects of information processing during sensorimotor integration, but rather-as suggested by cognitive theory-of specific codes in the neurophysiological signal. Particularly fronto-parietal cortical regions are associated with the identified mechanisms. The study shows how cognitive science theory-driven pharmacology can shed light on the neurobiological basis of perception-motor integration and how catecholamines affect specific information codes relevant to cognitive control.

The neural stability of perception-motor representations affects action outcomes and behavioral adaptation

Actions can fail - even though this is well known, little is known about what distinguishes neurophysiological processes preceding errors and correct actions. In this study, relying on the Theory of Event Coding, we test the assumption that only specific aspects of information coded in EEG activity are relevant for understanding processes leading to response errors. We examined N = 69 healthy participants who performed a mental rotation task and combined temporal EEG signal decomposition with multivariate pattern analysis (MVPA) and source localization analyses. We show that fractions of the EEG signal, primarily representing stimulus-response translation (event file) processes and motor response representations, are essential. Stimulus representations were less critical. The source localization results revealed widespread activity modulations in structures including the frontopolar, the middle and superior frontal, the anterior cingulate cortex, the cuneus, the inferior parietal cortex, and the ventral stream regions. These are associated with differential effects of the neural dynamics preceding correct/erroneous responses. The temporal-generalization MVPA showed that event file representations and representations of the motor response were already distinct 200 ms after stimulus presentation and this lasted till around 700 ms. The stability of this representational content was predictive for the magnitude of posterror slowing, which was particularly strong when there was no clear distinction between the neural activity profile of event file representations associated with a correct or an erroneous response. The study provides a detailed analysis of the dynamics leading to an error/correct response in connection to an overarching framework on action control.

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization

High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single- and multiple-source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes (1) the localization error for each source and (2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases in synthetic signals and 63% in real signals, and in more than 88% and 73% of cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.

Comparative analysis of background EEG activity based on MRI findings in neonatal hypoxic-ischemic encephalopathy: a standardized, low-resolution, brain electromagnetic tomography (sLORETA) study

Background

It is important to assess the degree of brain injury and predict long-term outcomes in neonates diagnosed with hypoxic-ischemic encephalopathy (HIE). However, routine studies, including magnetic resonance imaging (MRI) and conventional encephalography (EEG) or amplitude-integrated EEG (aEEG), have their own limitations in terms of availability and accuracy of evaluation. Recently, quantitative EEG (qEEG) has been shown to improve the predictive reliability of neonatal HIE and has been further refined with brain mapping techniques.

Methods

We investigated background EEG activities in 29 neonates with HIE who experienced therapeutic hypothermia, via qEEG using a distributed source model. MRI images were evaluated and classified into two groups (normal-to-mild injury vs moderate-to-severe injury), based on a scoring system. Non-parametric statistical analysis using standardized low-resolution brain electromagnetic tomography was performed to compare the current density distribution of four frequency bands (delta, theta, alpha, and beta) between the two groups.

Results

Electrical neuronal activities were significantly lower in the moderate-to-severe injury group compared with the normal-to-mild injury group. Background EEG activities in moderate-to-severe HIE were most significantly reduced in the temporal and parietal lobes. Quantitative EEG also revealed a decrease in background activity at all frequency bands, with a maximum in decrease in the delta component. The maximum difference in current density was found in the inferior parietal lobule of the right parietal lobe for the delta frequency band.

Conclusions

Our study demonstrated quantitative and topographical changes in EEG in moderate-to-severe neonatal HIE. They also suggest possible implementation and evaluation of conventional EEG and aEEG in neonatal HIE. The findings have implications as biomarkers in the assessment of neonatal HIE.

A review of MEG dynamic brain network research

The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.

Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

BACKGROUND

Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed.

METHODS

We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split).

RESULTS

In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset.

LIMITATIONS

The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects.

CONCLUSIONS

This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.

Neurofeedback training: Decreases in Mu rhythm lead to improved motor performance in complex visuomotor skills

The physiological function of the Mu rhythm (8-13 Hz in the central region) is still unclear, particularly its role in visuomotor performance in sports (shooting vs. golf putting), as both the complexity of the motor skills (i.e., simple vs. complex visuomotor skills) and the skill level (e.g., novices vs. experts or low-skilled vs. highly skilled) may modulate Mu rhythm. To gain a broader understanding of the association between Mu rhythm and visuomotor skill performance, a study design that considers both a control moderator (the difference in skill level) and the ability to manipulate Mu rhythm (i.e., either increase or decrease Mu rhythm) is required. To achieve this, we recruited 30 novice golfers who were randomly assigned to either the increased Mu rhythm group (IMG), decreased Mu rhythm group (DMG), or sham group (SG) and used electroencephalographic-neurofeedback training (EEG-NFT) to manipulate Mu rhythm during a golf putting task (complex visuomotor skill). The aim was to determine whether the complexity of the motor skill was a potential moderator of Mu rhythm. We mainly found that Mu power was significantly decreased in the DMG following EEG-NFT, which lead to increased motor control and improved performance. We suggest that (1) the complexity of the motor skill, rather than the difference in skill level, may be a potential moderator of Mu rhythm and visuomotor performance, as our results were not consistent with a previous study that reported that increased Mu rhythm improved shooting performance (a simple visuomotor task) in novices.

Resting-state theta activity is linked to information content-specific coding levels during response inhibition

The neurophysiological processes underlying the inhibition of impulsive responses have been studied extensively. While also the role of theta oscillations during response inhibition is well examined, the relevance of resting-state theta activity for inhibitory control processes is largely unknown. We test the hypothesis that there are specific relationships between resting-state theta activity and sensory/motor coding levels during response inhibition using EEG methods. We show that resting theta activity is specifically linked to the stimulus-related fraction of neurophysiological activity in specific time windows during motor inhibition. In contrast, concomitantly coded processes related to decision-making or response selection as well as the behavioral inhibition performance were not associated with resting theta activity. Even at the peak of task-related theta power, where task-related theta activity and resting theta activity differed the most, there was still predominantly a significant correlation between both types of theta activity. This suggests that aspects similar to resting dynamics are evident in the proportion of inhibition-related neurophysiological activity that reflects an “alarm” signal, whose function is to process and indicate the need for cognitive control. Thus, specific aspects of task-related theta power may build upon resting theta activity when cognitive control is necessary.

Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level

Goal-directed actions frequently require a balance between antagonistic processes (e.g., executing and inhibiting a response), often showing an interdependency concerning what constitutes goal-directed behavior. While an inter-dependency of antagonistic actions is well described at a behavioral level, a possible inter-dependency of underlying processes at a neuronal level is still enigmatic. However, if there is an interdependency, it should be possible to predict the neurophysiological processes underlying inhibitory control based on the neural processes underlying speeded automatic responses. Based on that rationale, we applied artificial intelligence and source localization methods to human EEG recordings from N = 255 participants undergoing a response inhibition experiment (Go/Nogo task). We show that the amplitude and timing of scalp potentials and their functional neuroanatomical sources during inhibitory control can be inferred by conditional generative adversarial networks (cGANs) using neurophysiological data recorded during response execution. We provide insights into possible limitations in the use of cGANs to delineate the interdependency of antagonistic actions on a neurophysiological level. Nevertheless, artificial intelligence methods can provide information about interdependencies between opposing cognitive processes on a neurophysiological level with relevance for cognitive theory.

Altered Processing of Visual Stimuli in Vestibular Migraine Patients Between Attacks: A Combined VEP and sLORETA Study

Objective: Vestibular migraine (VM) is one of the most common causes of recurrent vertigo, but the neural mechanisms that mediate such symptoms remain unknown. Since visual symptoms and photophobia are common clinical features of VM patients, we hypothesized that VM patients have abnormally sensitive low-level visual processing capabilities. This study aimed to investigate cortex abnormalities in VM patients using visual evoked potential (VEP) and standardized low-resolution brain electromagnetic tomography (sLORETA) analysis.

Methods: We employed visual stimuli consisting of reversing displays of circular checkerboard patterns to examine “low-level” visual processes. Thirty-three females with VM and 20 healthy control (HC) females underwent VEP testing. VEP components and sLORETA were analyzed.

Results: Patients with VM showed significantly lower amplitude and decreased latency of P1 activation compared with HC subjects. Further topographic mapping analysis revealed a group difference in the occipital area around P1 latency. sLORETA analysis was performed in the time frame of the P1 component and showed significantly less activity (deactivation) in VM patients in the frontal, parietal, temporal, limbic, and occipital lobes, as well as sub-lobar regions. The maximum current density difference was in the postcentral gyrus of the parietal lobe. P1 source density differences between HC subjects and VM patients overlapped with the vestibular cortical fields.

Conclusion: The significantly abnormal response to visual stimuli indicates altered processing in VM patients. These findings suggest that abnormalities in vestibular cortical fields might be a pathophysiological mechanism of VM.

Multi-level decoding of task sets in neurophysiological data during cognitive flexibility

Cognitive flexibility is essential to achieve higher level goals. Cognitive theories assume that the activation/deactivation of goals and task rules is central to understand cognitive flexibility. However, how this activation/deactivation dynamic is implemented on a neurophysiological level is unclear. Using EEG-based multivariate pattern analysis (MVPA) methods, we show that activation of relevant information occurs parallel in time at multiple levels in the neurophysiological signal containing aspects of stimulus-related processing, response selection, and motor response execution, and relates to different brain regions. The intensity with which task sets are activated and processed dynamically decreases and increases. The temporal stability of these activations could, however, hardly explain behavioral performance. Instead, task set deactivation processes associated with left orbitofrontal regions and inferior parietal regions selectively acting on motor response task sets are relevant. The study shows how propositions from cognitive theories stressing the importance task set activation/deactivation during cognitive flexibility are implemented on a neurophysiological level.

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Stimulus-related, motor, and response selection aspects of task set were decoded

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Activation of task rule information occurs at multiple neurophysiological levels

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Activation and deactivation of rule sets contributes to cognitive flexibility

Cognitive Decline Risk Stratification in People with Late-Onset Epilepsy of Unknown Etiology: An Electroencephalographic Connectivity and Graph Theory Pilot Study

BACKGROUND

Although people with late onset epilepsy of unknown etiology (LOEU) are at higher risk of cognitive decline compared to the general population, we still lack affordable tools to predict and stratify their risk of dementia.

OBJECTIVE

This pilot-study investigates the potential application of electroencephalography (EEG) network small-world (SW) properties in predicting cognitive decline among patients with LOEU.

METHODS

People diagnosed with LOEU and normal cognitive examination at the time of epilepsy diagnosis were included. Cerebrospinal fluid biomarkers, brain imaging, and neuropsychological assessment were performed at the time of epilepsy diagnosis. Baseline EEG was analyzed for SW properties. Patients were followed-up over time with neuropsychological testing to define the trajectory of cognitive decline.

RESULTS

Over 5.1 years of follow-up, among 24 patients diagnosed with LOEU, 62.5% were female, mean age was 65.3 years, thirteen developed mild cognitive impairment (MCI), and four developed dementia. Patients with LOEU developing MCI had lower values of SW coefficients in the delta (p = 0.03) band and higher SW values in the alpha frequency bands (p = 0.02) compared to patients having normal cognition at last follow-up. The two separate ANOVAs, for low and alpha bands, confirmed an interaction between SW and cognitive decline at follow-up. A similar gradient was confirmed for patients developing dementia compared to those with normal cognitive function as well as to those developing MCI.

CONCLUSION

Baseline EEG analysis through SW is worth investigating as an affordable, widely available tool to stratify LOEU patients for their risk of cognitive decline.

Cortical sensorimotor activity in the execution and suppression of discrete and rhythmic movements

Although the engagement of sensorimotor cortices in movement is well documented, the functional relevance of brain activity patterns remains ambiguous. Especially, the cortical engagement specific to the pre-, within-, and post-movement periods is poorly understood. The present study addressed this issue by examining sensorimotor EEG activity during the performance as well as STOP-signal cued suppression of movements pertaining to two distinct classes, namely, discrete vs. ongoing rhythmic movements. Our findings indicate that the lateralized readiness potential (LRP), which is classically used as a marker of pre-movement processing, indexes multiple pre- and in- movement-related brain dynamics in a movement-class dependent fashion. In- and post-movement event-related (de)synchronization (ERD/ERS) observed in the Mu (8-13 Hz) and Beta (15-30 Hz) frequency ranges were associated with estimated brain sources in both motor and somatosensory cortical areas. Notwithstanding, Beta ERS occurred earlier following cancelled than actually performed movements. In contrast, Mu power did not vary. Whereas Beta power may reflect the evaluation of the sensory predicted outcome, Mu power might engage in linking perception to action. Additionally, the rhythmic movement forced stop (only) showed a post-movement Mu/Beta rebound, which might reflect an active "clearing-out" of the motor plan and its feedback-based online control. Overall, the present study supports the notion that sensorimotor EEG modulations are key markers to investigate control or executive processes, here initiation and inhibition, which are exerted when performing distinct movement classes.

Multiple Brain Sources Are Differentially Engaged in the Inhibition of Distinct Action Types

Most studies contributing to identify the brain network for inhibitory control have investigated the cancelation of prepared-discrete actions, thus focusing on an isolated and short-lived chunk of human behavior. Aborting ongoing-continuous actions is an equally crucial ability but remains little explored. Although discrete and ongoing-continuous rhythmic actions are associated with partially overlapping yet largely distinct brain activations, it is unknown whether the inhibitory network operates similarly in both situations. Thus, distinguishing between action types constitutes a powerful means to investigate whether inhibition is a generic function. We, therefore, used independent component analysis (ICA) of EEG data and show that canceling a discrete action and aborting a rhythmic action rely on independent brain components. The ICA showed that a delta/theta power increase generically indexed inhibitory activity, whereas N2 and P3 ERP waves did so in an action-specific fashion. The action-specific components were generated by partially distinct brain sources, which indicates that the inhibitory network is engaged differently when canceling a prepared-discrete action versus aborting an ongoing-continuous action. In particular, increased activity was estimated in precentral gyri and posterior parts of the cingulate cortex for action canceling, whereas an enhanced activity was found in more frontal gyri and anterior parts of the cingulate cortex for action aborting. Overall, the present findings support the idea that inhibitory control is differentially implemented according to the type of action to revise.

Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG

OBJECTIVES

Functional connectivity (FC) is increasingly used as target for neuromodulation and enhancement of performance. A reliable assessment of FC with electroencephalography (EEG) currently requires a laboratory environment with high-density montages and a long preparation time. This study investigated the feasibility of reconstructing source FC with a low-density EEG montage towards a usage in real life applications.

METHODS

Source FC was reconstructed with inverse solutions and quantified as node degree of absolute imaginary coherence in alpha frequencies. We used simulated coherent point sources as well as two real datasets to investigate the impact of electrode density (19 vs. 128 electrodes) and usage of template vs. individual MRI-based head models on localization accuracy. In addition, we checked whether low-density EEG is able to capture inter-individual variations in coherence strength.

RESULTS

In numerical simulations as well as real data, a reduction of the number of electrodes led to less reliable reconstructions of coherent sources and of coupling strength. Yet, when comparing different approaches to reconstructing FC from 19 electrodes, source FC obtained with beamformers outperformed sensor FC, FC computed after independent component analysis, and source FC obtained with sLORETA. In particular, only source FC based on beamformers was able to capture neural correlates of motor behavior.

CONCLUSION

Reconstructions of FC from low-density EEG is challenging, but may be feasible when using source reconstructions with beamformers.

Electrophysiological underpinnings of reward processing: Are we exploiting the full potential of EEG?

Understanding how the brain processes reward is an important and complex endeavor, which has involved the use of a range of complementary neuroimaging tools, including electroencephalography (EEG). EEG has been praised for its high temporal resolution but, because the signal recorded at the scalp is a mixture of brain activities, it is often considered to have poor spatial resolution. Besides, EEG data analysis has most often relied on event-related potentials (ERPs) which cancel out non-phase locked oscillatory activity, thus limiting the functional discriminative power of EEG attainable through spectral analyses. Because these three dimensions -temporal, spatial and spectral- have been unequally leveraged in reward studies, we argue that the full potential of EEG has not been exploited. To back up our claim, we first performed a systematic survey of EEG studies assessing reward processing. Specifically, we report on the nature of the cognitive processes investigated (i.e., reward anticipation or reward outcome processing) and the methods used to collect and process the EEG data (i.e., event-related potential, time-frequency or source analyses). A total of 359 studies involving healthy subjects and the delivery of monetary rewards were surveyed. We show that reward anticipation has been overlooked (88% of studies investigated reward outcome processing, while only 24% investigated reward anticipation), and that time-frequency and source analyses (respectively reported by 19% and 12% of the studies) have not been widely adopted by the field yet, with ERPs still being the dominant methodology (92% of the studies). We argue that this focus on feedback-related ERPs provides a biased perspective on reward processing, by ignoring reward anticipation processes as well as a large part of the information contained in the EEG signal. Finally, we illustrate with selected examples how addressing these issues could benefit the field, relying on approaches combining time-frequency analyses, blind source separation and source localization.

Perception-action integration in young age-A cross-sectional EEG study

Humans differ in their capacity for integrating perceived events and related actions. The "Theory of event coding" (TEC) conceptualizes how stimuli and actions are cognitively bound into a common functional representation (or "code"), known as the "event file". To date, however, the neural processes underlying the development of event file coding mechanisms across age are largely unclear. We investigated age-related neural changes of event file coding from late childhood to early adulthood, using EEG signal decompositions methods. We included a group of healthy participants (n = 91) between 10 and 30 years, performing an event file paradigm. Results of this study revealed age-related effects on event file coding processes both at the behavioural and the neurophysiological level. Performance accuracy data showed that event file unbinding und rebinding processes become more efficient from late childhood to early adulthood. These behavioural effects are reflected by age-related effects in two neurophysiological subprocesses associated with the superior parietal cortex (BA7) as revealed in the analyses using EEG signal decomposition. The first process entails mapping and association processes between stimulus and response; whereas, the second comprises inhibitory control subprocesses subserving the selection of the relevant motor programme amongst competing response options.

Neural dynamics of stimulus-response representations during inhibitory control

The investigation of action control processes is one major field in cognitive neuroscience and several theoretical frameworks have been proposed. One established framework is the "Theory of Event Coding" (TEC). However, only rarely, this framework has been used in the context of response inhibition and how stimulus-response association or binding processes modulate response inhibition performance. Particularly the neural dynamics of stimulus-response representations during inhibitory control are elusive. To address this, we examined n = 40 healthy controls and combined temporal EEG signal decomposition with source localization and temporal generalization multivariate pattern analysis (MVPA). We show that overlaps in features of stimuli used to trigger either response execution or inhibition compromised task performance. According to TEC, this indicates that binding processes in event file representations impact response inhibition through partial repetition costs. In the EEG data, reconfiguration of event files modulated processes in time windows well-known to reflect distinct response inhibition mechanisms. Crucially, event file coding processes were only evident in a specific fraction of neurophysiological activity associated with the inferior parietal cortex (BA40). Within that specific fraction of neurophysiological activity, the decoding of the dynamics of event file representations using temporal generalization MVPA suggested that event file representations are stable across several hundred milliseconds, and that event file coding during inhibitory control is reflected by a sustained activation pattern of neural dynamics.NEW & NOTEWORTHY The "mental representation" of how stimulus input translate into the appropriate response is central for goal-directed behavior. However, little is known about the dynamics of such representations on the neurophysiological level when it comes to the inhibition of motor processes. This dynamic is shown in the current study.

Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning

Humans are capable of acquiring multiple types of information presented in the same information stream. It has been suggested that at least two parallel learning processes are important during learning of sequential patterns-statistical learning and rule-based learning. Yet, the neurophysiological underpinnings of these parallel learning processes are not fully understood. To differentiate between the simultaneous mechanisms at the single trial level, we apply a temporal EEG signal decomposition approach together with sLORETA source localization method to delineate whether distinct statistical and rule-based learning codes can be distinguished in EEG data and can be related to distinct functional neuroanatomical structures. We demonstrate that concomitant but distinct aspects of information coded in the N2 time window play a role in these mechanisms: mismatch detection and response control underlie statistical learning and rule-based learning, respectively, albeit with different levels of time-sensitivity. Moreover, the effects of the two learning mechanisms in the different temporally decomposed clusters of neural activity also differed from each other in neural sources. Importantly, the right inferior frontal cortex (BA44) was specifically implicated in visuomotor statistical learning, confirming its role in the acquisition of transitional probabilities. In contrast, visuomotor rule-based learning was associated with the prefrontal gyrus (BA6). The results show how simultaneous learning mechanisms operate at the neurophysiological level and are orchestrated by distinct prefrontal cortical areas. The current findings deepen our understanding on the mechanisms of how humans are capable of learning multiple types of information from the same stimulus stream in a parallel fashion.

Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice

OBJECTIVE

Age-related differences in neural strategies for motor learning are not fully understood. We determined the effects of age on the relationship between motor network connectivity and motor skill acquisition, consolidation, and interlimb transfer using dynamic imaging of coherent sources.

METHODS

Healthy younger (n = 24, 18-24 y) and older (n = 24, 65-87 y) adults unilaterally practiced a visuomotor task and resting-state electroencephalographic data was acquired before and after practice as well as at retention.

RESULTS

The results showed that right-hand skill acquisition and consolidation did not differ between age groups. However, age affected the ability to transfer the newly acquired motor skill to the non-practiced limb. Moreover, strengthened left- and right-primary motor cortex-related beta connectivity was negatively and positively associated with right-hand skill acquisition and left-hand skill consolidation in older adults, respectively.

CONCLUSION

Age-dependent modulations of bilateral resting-state motor network connectivity indicate age-specific strategies for the acquisition, consolidation, and interlimb transfer of novel motor tasks.

SIGNIFICANCE

The present results provide insights into the mechanisms underlying motor learning that are important for the development of interventions for patients with unilateral injuries.

Anodal tDCS modulates specific processing codes during conflict monitoring associated with superior and middle frontal cortices

Conflict monitoring processes are central for cognitive control. Neurophysiological correlates of conflict monitoring (i.e. the N2 ERP) likely represent a mixture of different cognitive processes. Based on theoretical considerations, we hypothesized that effects of anodal tDCS (atDCS) in superior frontal areas affect specific subprocesses in neurophysiological activity during conflict monitoring. To investigate this, young healthy adults performed a Simon task while EEG was recorded. atDCS and sham tDCS were applied in a single-blind, cross-over study design. Using temporal signal decomposition in combination with source localization analyses, we demonstrated that atDCS effects on cognitive control are very specific: the detrimental effect of atDCS on response speed was largest in case of response conflicts. This however only showed in aspects of the decomposed N2 component, reflecting stimulus-response translation processes. In contrast to this, stimulus-related aspects of the N2 as well as purely response-related processes were not modulated by atDCS. EEG source localization analyses revealed that the effect was likely driven by activity modulations in the superior frontal areas, including the supplementary motor cortex (BA6), as well as middle frontal (BA9) and medial frontal areas (BA32). atDCS did not modulate effects of proprioceptive information on hand position, even though this aspect is known to be processed within the same brain areas. Physiological effects of atDCS likely modulate specific aspects of information processing during cognitive control.

MEG current source reconstruction using a meta-analysis fMRI prior

Magnetoencephalography (MEG) offers a unique way to noninvasively investigate millisecond-order cortical activities by mapping sensor signals (magnetic fields outside the head) to cortical current sources using current source reconstruction methods. Current source reconstruction is defined as an ill-posed inverse problem, since the number of sensors is less than the number of current sources. One powerful approach to solving this problem is to use functional MRI (fMRI) data as a spatial constraint, although it boosts the cost of measurement and the burden on subjects. Here, we show how to use the meta-analysis fMRI data synthesized from thousands of papers instead of the individually recorded fMRI data. To mitigate the differences between the meta-analysis and individual data, the former are imported as prior information of the hierarchical Bayesian estimation. Using realistic simulations, we found out the performance of current source reconstruction using meta-analysis fMRI data to be better than that using low-quality individual fMRI data and conventional methods. By applying experimental data of a face recognition task, we qualitatively confirmed that group analysis results using the meta-analysis fMRI data showed a tendency similar to the results using the individual fMRI data. Our results indicate that the use of meta-analysis fMRI data improves current source reconstruction without additional measurement costs. We assume the proposed method would have greater effect for modalities with lower measurement costs, such as optically pumped magnetometers.

Pushing to the Limits: What Processes during Cognitive Control are Enhanced by Reaction-Time Feedback?

To respond as quickly as possible in a given task is a widely used instruction in cognitive neuroscience; however, the neural processes modulated by this common experimental procedure remain largely elusive. We investigated the underlying neurophysiological processes combining electroencephalography (EEG) signal decomposition (residue iteration decomposition, RIDE) and source localization. We show that trial-based response speed instructions enhance behavioral performance in conflicting trials, but slightly impair performance in nonconflicting trials. The modulation seen in conflicting trials was found at several coding levels in EEG data using RIDE. In the S-cluster N2 time window, this modulation was associated with modulated activation in the posterior cingulate cortex and the superior frontal gyrus. Furthermore, in the C-cluster P3 time window, this modulation was associated with modulated activation in the middle frontal gyrus. Interestingly, in the R-cluster P3 time window, this modulation was strongest according to statistical effect sizes, associated with modulated activity in the primary motor cortex. Reaction-time feedback mainly modulates response motor execution processes, whereas attentional and response selection processes are less affected. The study underlines the importance of being aware of how experimental instructions influence the behavior and neurophysiological processes.

A distinct electrophysiological signature for synaesthesia that is independent of individual differences in sensory sensitivity

People with synaesthesia have been reported to show atypical electrophysiological responses to certain simple sensory stimuli, even if these stimuli are not inducers of synaesthesia. However, it is unclear whether this constitutes a neural marker that is relatively specific to synaesthesia or whether it reflects some other trait that co-occurs with synaesthesia, but is not specific to it. One candidate is atypical sensory sensitivity (e.g., strong aversion to certain lights and sounds, 'sensory overload') which is a feature of both synaesthesia and autism and that varies greatly in the neurotypical population. Using visual evoked-potentials (to stimuli varying in spatial frequency) and auditory-evoked potentials (to stimuli varying in auditory frequency), we found that synaesthetes had a modulated visual evoked-potential around P1/N1 (emanating from fusiform cortex), a greater auditory N1, as well as differences in the time-frequency domain (increased alpha and beta induced power for visual stimuli). This was distinct from that found in non-synaesthetes. By contrast, no significant electrophysiological differences were found that were linked to neurotypical variation in sensory sensitivity.

Measuring the cortical tracking of speech with optically-pumped magnetometers

During continuous speech listening, brain activity tracks speech rhythmicity at frequencies matching with the repetition rate of phrases (0.2-1.5 Hz), words (2-4 Hz) and syllables (4-8 Hz). Here, we evaluated the applicability of wearable MEG based on optically-pumped magnetometers (OPMs) to measure such cortical tracking of speech (CTS). Measuring CTS with OPMs is a priori challenging given the complications associated with OPM measurements at frequencies below 4 Hz, due to increased intrinsic interference and head movement artifacts. Still, this represents an important development as OPM-MEG provides lifespan compliance and substantially improved spatial resolution compared with classical MEG. In this study, four healthy right-handed adults listened to continuous speech for 9 min. The radial component of the magnetic field was recorded simultaneously with 45-46 OPMs evenly covering the scalp surface and fixed to an additively manufactured helmet which fitted all 4 participants. We estimated CTS with reconstruction accuracy and coherence, and determined the number of dominant principal components (PCs) to remove from the data (as a preprocessing step) for optimal estimation. We also identified the dominant source of CTS using a minimum norm estimate. CTS estimated with reconstruction accuracy and coherence was significant in all 4 participants at phrasal and word rates, and in 3 participants (reconstruction accuracy) or 2 (coherence) at syllabic rate. Overall, close-to-optimal CTS estimation was obtained when the 3 (reconstruction accuracy) or 10 (coherence) first PCs were removed from the data. Importantly, values of reconstruction accuracy (~0.4 for 0.2-1.5-Hz CTS and ~0.1 for 2-8-Hz CTS) were remarkably close to those previously reported in classical MEG studies. Finally, source reconstruction localized the main sources of CTS to bilateral auditory cortices. In conclusion, t his study demonstrates that OPMs can be used for the purpose of CTS assessment. This finding opens new research avenues to unravel the neural network involved in CTS across the lifespan and potential alterations in, e.g., language developmental disorders. Data also suggest that OPMs are generally suitable for recording neural activity at frequencies below 4 Hz provided PCA is used as a preprocessing step; 0.2-1.5-Hz being the lowest frequency range successfully investigated here.

Neurophysiology of embedded response plans: age effects in action execution but not in feature integration from preadolescence to adulthood

Performing a goal-directed movement consists of a chain of complex preparatory mechanisms. Such planning especially requires integration (or binding) of various action features, a process that has been conceptualized in the "theory of event coding." Theoretical considerations and empirical research suggest that these processes are subject to developmental effects from adolescence to adulthood. The aim of the present study was to investigate age-related modulations in action feature binding processes and to shed light on underlying neurophysiological development from preadolescence to early adulthood. We examined a group of healthy participants (n = 61) between 10 and 30 yr of age, who performed a task that requires a series of bimanual response selections in an embedded paradigm. For an in-depth analysis of the underlying neural correlates, we applied EEG signal decomposition together with source localization analyses. Behavioral results across the whole group did not show binding effects in reaction times but in intraindividual response variability. From age 10 to 30 yr, there was a decrease in reaction times and reaction time variability but no age-related effect in action file binding. The latter were corroborated by Bayesian data analyses. On the brain level, the developmental effects on response selection were associated with activation modulations in the superior parietal cortex (BA7). The results show that mechanisms of action execution and speed, but not those of action feature binding, are subject to age-related changes between the age of 10 and 30 yr.NEW & NOTEWORTHY Different aspects of an action need to be integrated to allow smooth unfolding of behavior. We examine developmental effects in these processes and show that mechanisms of action execution and speed, but not those of action feature binding, are subject to age-related changes between the age of 10 and 30 yr.

Task Switching and the Role of Motor Reprogramming in Parietal Structures

Human behaviour amazes with extraordinary flexibility and the underlying neural mechanisms have often been studied using task switching. Despite extensive research, the relative importance of "cognitive" and "motor" aspects during switching is unclear. In the current study we examine this question combining EEG analysis techniques and source localization to examine whether the selection of the response, or processes during the execution of the response, contribute most to switching effects. A clear dissociation was observed in the signal decomposition, since codes relating to motor aspects play a significant role in task switching and the scope of the switching costs. This was not the case for signals that denote reaction selection or decision processes that respond to selection or basic stimulus processing codes. On a functional neuroanatomical level, these modulations in motor processes showed a clear temporal sequence in that motor codes are processed primarily in superior parietal regions (Brodman area 7) and only then in premotor regions (Brodman area 6). The observed modulations may reflect motor reprogramming processes. The study shows how EEG signal analysis in combination with brain mapping methods can inform debates on theories of human cognitive flexibility.

Pragmatic spatial sampling for wearable MEG arrays

Several new technologies have emerged promising new Magnetoencephalography (MEG) systems in which the sensors can be placed close to the scalp. One such technology, Optically Pumped MEG (OP-MEG) allows for a scalp mounted system that provides measurements within millimetres of the scalp surface. A question that arises in developing on-scalp systems is: how many sensors are necessary to achieve adequate performance/spatial discrimination? There are many factors to consider in answering this question such as the signal to noise ratio (SNR), the locations and depths of the sources, density of spatial sampling, sensor gain errors (due to interference, subject movement, cross-talk, etc.) and, of course, the desired spatial discrimination. In this paper, we provide simulations which show the impact these factors have on designing sensor arrays for wearable MEG. While OP-MEG has the potential to provide high information content at dense spatial samplings, we find that adequate spatial discrimination of sources (< 1 cm) can be achieved with relatively few sensors (< 100) at coarse spatial samplings (~ 30 mm) at high SNR. After this point approximately 50 more sensors are required for every 1 mm improvement in spatial discrimination. Comparable discrimination for traditional cryogenic systems require more channels by these same metrics. We also show that sensor gain errors have the greatest impact on discrimination between deep sources at high SNR. Finally, we also examine the limitation that aliasing due to undersampling has on the effective SNR of on-scalp sensors.