Generator localization by current source density (CSD): implications of volume conduction and field closure at intracranial and scalp resolutions

Electrophysiological correlates of inhibitory control in children: Relations with prenatal maternal risk factors and child psychopathology

Inhibitory control plays an important role in children's cognitive and socioemotional development, including their psychopathology. It has been established that contextual factors such as socioeconomic status (SES) and parents' psychopathology are associated with children's inhibitory control. However, the relations between the neural correlates of inhibitory control and contextual factors have been rarely examined in longitudinal studies. In the present study, we used both event-related potential (ERP) components and time-frequency measures of inhibitory control to evaluate the neural pathways between contextual factors, including prenatal SES and maternal psychopathology, and children's behavioral and emotional problems in a large sample of children (N = 560; 51.75% females; Mage = 7.13 years; Rangeage = 4-11 years). Results showed that theta power, which was positively predicted by prenatal SES and was negatively related to children's externalizing problems, mediated the longitudinal and negative relation between them. ERP amplitudes and latencies did not mediate the longitudinal association between prenatal risk factors (i.e., prenatal SES and maternal psychopathology) and children's internalizing and externalizing problems. Our findings increase our understanding of the neural pathways linking early risk factors to children's psychopathology.

Characteristics of spontaneous anterior-posterior oscillation-frequency convergences in the alpha band

Anterior-posterior interactions in the alpha band (8-12 Hz) have been implicated in a variety of functions including perception, attention, and working memory. The underlying neural communication can be flexibly controlled by adjusting phase relations when activities across anterior-posterior regions oscillate at a matched frequency. We thus investigated how alpha oscillation frequencies spontaneously converged along anterior-posterior regions by tracking oscillatory EEG activity while participants rested. As more anterior-posterior regions (scalp sites) frequency-converged, the probability of additional regions joining the frequency convergence increased, and so did oscillatory synchronization at participating regions (measured as oscillatory power), suggesting that anterior-posterior frequency convergences are driven by inter-regional entrainment. Notably, frequency convergences were accompanied by two types of approximately linear phase gradients, one progressively phase-lagged in the anterior direction-the posterior-to-anterior (P-A) gradient-and the other progressively phase-lagged in the posterior direction-the anterior-to-posterior (A-P) gradient. These gradients implied traveling waves propagating in the feedforward and feedback directions, respectively. Interestingly, while in natural viewing frequency convergences were accompanied by both gradient types (occurring at different frequencies) regardless of anterior-posterior routes, when the eyes were closed, the P-A and A-P gradients spatially segregated, channeling feedforward flows of information primarily through the midline and feedback flows primarily through each hemisphere. Future research may investigate how eye closure organizes information flows in this way and how it influences hierarchical information processing. Future research may also investigate the functional roles of frequency-convergence contingent traveling waves in contrast to those generated by other mechanisms.Significance Statement Anterior-posterior interactions in the alpha band (8-12 Hz) have been implicated in a variety of functions including perception, attention, and working memory. While alpha frequencies differ across anterior-posterior regions, they also dynamically converge while people rest. Our EEG study investigated the mechanisms and functions of spontaneous alpha-frequency convergences. Our results suggest that anterior-posterior frequency convergences are driven by inter-regional entrainment. Notably, frequency convergences were accompanied by approximately linear posterior-to-anterior and anterior-to-posterior phase gradients, likely facilitating feedforward and feedback information flows via travelling waves. Interestingly, closing eyes spatially organized these information flows, channeling feedforward flows through the midline and feedback flows through each hemisphere. Future research may investigate the behavioral significance of these frequency-convergence contingent flows of information.

Emotional influences on remembering and forgetting explained by frontal and parietal dynamics

Based on item-method directed forgetting (DF) task, 60 participants were recruited to explore the influence of emotion (negative, neutral, and positive) on memory encoding processing, with all data referring to the encoding phase of the task. Behavioral results showed that participants were more successful at remembering negative pictures that needed to be forgotten, with both higher recognition rates and discrimination accuracy (Pr) compared with neutral pictures. In the brain, parietal activities reflected preferential processing during negative picture viewing through enhanced late parietal positive potentials (LPP) relative to neutral ones. In addition, "Remember" (R) instruction evoked a larger parietal P3 component, whereas "Forget" (F) instruction evoked a stronger frontal N2 component, each of which component was significantly associated with the DF effect (i.e., more recognized items of R-cue than that of F-cue), reflecting the fact that inhibitory control and selective rehearsal mechanisms were jointly responsible for the directed forgetting of emotional materials. Finally, we showed the presence of instruction-evoked low-frequency phase synchronization between frontal and parietal regions, and that these synchronization patterns differed between R-cue and F-cue in an emotion-dependent manner. Together, these findings reveal cognitive mechanisms and specific patterns of large-scale phase synchronization underlying active forgetting of emotional memories, deepening our comprehension of the interplay between cognition and emotion.NEW & NOTEWORTHY This study provides experimental evidence that emotional memories, especially negative ones, are more difficult to intentionally forget than neutral memories within the item-method directed forgetting paradigm. It explores the cognitive mechanisms underlying this process, highlighting the role of selective rehearsal and inhibitory control. In addition, it reveals emotion-dependent low-frequency phase synchronization between frontal and parietal regions, offering new insights into active forgetting of emotional memories.

Identification of anticipatory brain activity in a time discrimination task

The purpose of this study was to investigate anticipatory functions in temporal cognition, identifying the presence of proactive brain processing specifically preceding a time discrimination task. To this aim, two discriminative response tasks (DRTs) were employed: a feature DRT and a temporal (T-DRT). While the F-DRT required discrimination among different geometrical shapes, the T-DRT required discrimination among different stimulus durations. Specifically, this study investigated the role of premotor and prefrontal cortices, and sensory visual areas in preparatory activity preceding time-processing by electroencephalographic methods and analyzing the event-related potential (ERP). ERP components associated with motor (the BP), cognitive (the pN), and sensory readiness (the vN) were analyzed on 21 participants completing the two DRTs. The results support the involvement of all considered brain areas in temporal cognition but extend this information by indicating that these areas can be engaged during the preparation phase before the stimulus is delivered. Furthermore, the T-DRT requires strong anticipatory activity in the PFC likely serving as a moderator of upcoming motor responses. Finally, visual areas were greatly engaged in the early phase of sensory readiness of the T-DRT probably to create top-down low-level representations of imminent events to facilitate perception.

Unravelling the origin of reward positivity: a human intracranial event-related brain potential study

Reward positivity (RewP) is an event-related brain potential component that emerges ∼250-350 ms after receiving reward-related feedback stimuli and is believed to be important for reinforcement learning and reward processing. Although numerous localization studies have indicated that the anterior cingulate cortex (ACC) is the neural generator of this component, other studies have identified sources outside of the ACC, fuelling a debate about its origin. Because the results of EEG and magnetoencephalography source-localization studies are severely limited by the inverse problem, we addressed this question by leveraging the high spatial and temporal resolution of intracranial EEG. We predicted that we would identify a neural generator of rthe RewP in the caudal ACC. We recorded intracranial EEG in 19 patients with refractory epilepsy who underwent invasive video-EEG monitoring at Ghent University Hospital, Belgium. Participants engaged in the virtual T-maze task, a trial-and-error task known to elicit a canonical RewP, while scalp and intracranial EEG were recorded simultaneously. The RewP was identified using a difference wave approach for both scalp and intracranial EEG. The data were aggregated across participants to create a virtual 'meta-participant' that contained all the recorded intracranial event-related brain potentials with respect to their intracranial contact locations. We used both hypothesis-driven (focused on ACC) and exploratory (whole-brain analysis) approaches to segment the brain into regions of interest. For each region of interest, we evaluated the degree to which the time course of the absolute current density (ACD) activity mirrored the time course of the RewP, and we confirmed the statistical significance of the results using permutation analysis. The grand average waveform of the scalp data revealed a RewP at 309 ms after reward feedback with a frontocentral scalp distribution, consistent with the identification of this component as the RewP. The meta-participant contained intracranial event-related brain potentials recorded from 582 intracranial contacts in total. The ACD activity of the aggregated intracranial event-related brain potentials was most similar to the RewP in the left caudal ACC, left dorsolateral prefrontal cortex, left frontomedial cortex and left white matter, with the highest score attributed to caudal ACC, as predicted. To our knowledge, this is the first study to use intracranial EEG aggregated across multiple human epilepsy patients and current source density analysis to identify the neural generator(s) of the RewP. These results provide direct evidence that the ACC is a neural generator of the RewP.

Human Auditory-Motor Networks Show Frequency-Specific Phase-Based Coupling in Resting-State MEG

Perception and production of music and speech rely on auditory-motor coupling, a mechanism which has been linked to temporally precise oscillatory coupling between auditory and motor regions of the human brain, particularly in the beta frequency band. Recently, brain imaging studies using magnetoencephalography (MEG) have also shown that accurate auditory temporal predictions specifically depend on phase coherence between auditory and motor cortical regions. However, it is not yet clear whether this tight oscillatory phase coupling is an intrinsic feature of the auditory-motor loop, or whether it is only elicited by task demands. Further, we do not know if phase synchrony is uniquely enhanced in the auditory-motor system compared to other sensorimotor modalities, or to which degree it is amplified by musical training. In order to resolve these questions, we measured the degree of phase locking between motor regions and auditory or visual areas in musicians and non-musicians using resting-state MEG. We derived phase locking values (PLVs) and phase transfer entropy (PTE) values from 90 healthy young participants. We observed significantly higher PLVs across all auditory-motor pairings compared to all visuomotor pairings in all frequency bands. The pairing with the highest degree of phase synchrony was right primary auditory cortex with right ventral premotor cortex, a connection which has been highlighted in previous literature on auditory-motor coupling. Additionally, we observed that auditory-motor and visuomotor PLVs were significantly higher across all structures in the right hemisphere, and we found the highest differences between auditory and visual PLVs in the theta, alpha, and beta frequency bands. Last, we found that the theta and beta bands exhibited a preference for a motor-to-auditory PTE direction and that the alpha and gamma bands exhibited the opposite preference for an auditory-to-motor PTE direction. Taken together, these findings confirm our hypotheses that motor phase synchrony is significantly enhanced in auditory compared to visual cortical regions at rest, that these differences are highest across the theta-beta spectrum of frequencies, and that there exist alternating information flow loops across auditory-motor structures as a function of frequency. In our view, this supports the existence of an intrinsic, time-based coupling for low-latency integration of sounds and movements which involves synchronized phasic activity between primary auditory cortex with motor and premotor cortical areas.

Using cluster-based permutation tests to estimate MEG/EEG onsets: How bad is it?

Localising effects in space, time and other dimensions is a fundamental goal of magneto- and electroencephalography (EEG) research. A popular exploratory approach applies mass-univariate statistics followed by cluster-sum inferences, an effective way to correct for multiple comparisons while preserving high statistical power by pooling together neighbouring effects. Yet, these cluster-based methods have an important limitation: each cluster is associated with a unique p-value, such that there is no error control at individual timepoints, and one must be cautious about interpreting when and where effects start and end. Sassenhagen and Draschkow (2019) provided an important reminder of this limitation. They also reported results from a simulation, suggesting that onsets estimated from EEG data are both positively biased and very variable. However, the simulation lacked comparisons to other methods. Here, I report such comparisons in a new simulation, replicating the positive bias of the cluster-sum method, but also demonstrating that it performs relatively well, in terms of bias and variability, compared to other methods that provide pointwise p-values: two methods that control the false discovery rate and two methods that control the familywise error rate (cluster-depth and maximum statistic methods). I also present several strategies to reduce estimation bias, including group calibration, group comparison and using binary segmentation, a simple change point detection algorithm that outperformed mass-univariate methods in simulations. Finally, I demonstrate how to generate onset hierarchical bootstrap confidence intervals that integrate variability over trials and participants, a substantial improvement over standard group approaches that ignore measurement uncertainty.

Altered Use of Context During Visual Perception in Psychotic Psychopathology: A Neurophysiological Investigation of Tuned and Untuned Suppression During Contrast Perception

BACKGROUND AND HYPOTHESIS

The human visual system streamlines visual processing by suppressing responses to textures that are similar to their surrounding context. Surround suppression is weaker in individuals with schizophrenia (ISZ); this altered use of visuospatial context may relate to the characteristic visual distortions they experience.

STUDY DESIGN

To understand atypical surround suppression in psychotic psychopathology, we investigated neurophysiological responses in ISZ, healthy controls (HC), individuals with bipolar disorder (IBP), and first-degree relatives (ISZR/IBPR). Participants performed a contrast judgment task on a circular target with annular surrounds, with concurrent electroencephalography. Orientation-independent (untuned) suppression was estimated from responses to central targets with orthogonal surrounds; the orientation-dependence of suppression was estimated by fitting an exponential function to the increase in suppression as surrounds became more aligned with the center.

RESULTS

ISZ exhibited weakened untuned suppression coupled with enhanced orientation-dependence of suppression. The N1 visual evoked potential was associated with the orientation-dependence of suppression, with ISZ and ISZR (but not IBP or IBPR) showing enhanced orientation-dependence of the N1. Collapsed across orientation conditions, the N1 for ISZ lacked asymmetry toward the right hemisphere; this reduction in N1 asymmetry was associated with reduced untuned suppression, real-world perceptual anomalies, and psychotic psychopathology. The overall amplitude of the N1 was reduced in ISZ and IBP.

CONCLUSIONS

Key measures of symptomatology for ISZ are associated with reductions in untuned suppression. Increased sensitivity for ISZ to the relative orientation of suppressive surrounds is reflected in the N1 VEP, which is commonly associated with higher-level visual functions such as allocation of spatial attention or scene segmentation.

Hierarchical Organization of Visual Feature Attention Control

Attention can be deployed in anticipation of visual stimuli based on features such as their color or direction of motion. This anticipatory feature-based attention involves top-down neural control signals from the frontoparietal network that bias visual cortex to enhance the processing of attended information and suppress distraction. So, for example, anticipatory attention control can enable effective selection based on stimulus color while ignoring distracting information about stimulus motion. But as well, anticipatory attention can be focused more narrowly, for example, to select specific colors or motion directions that define task-relevant events and objects. One important question that remains open is whether anticipatory attention control first biases broad feature dimensions such as color versus motion before biasing the specific feature attributes (e.g., blue vs. green). To investigate this, we recorded EEG activity during a task where participants were cued to either attend to a color (blue or green) or a motion direction (up or down) on a trial-by-trial basis. Applying multivariate decoding approaches to the EEG alpha band (8-12 Hz) activity during the attention control period (cue-target interval), we observed significant decoding for both the attended dimensions (color vs. motion) and specific feature attributes (blue vs. green; up vs. down). Importantly, the temporal onset of the dimension-level biasing (color vs. motion) preceded that of the attribute-level biasing (e.g., blue vs. green). These findings demonstrate that the top-down control of feature-based attention proceeds in a hierarchical fashion, first biasing the broad feature dimension, and then narrowing to the specific feature attribute.

Significance Statement

During voluntary feature-based attention, electrophysiological and neuroimaging studies have highlighted the role of anticipatory (top-down) biasing of the sensory cortex in enhancing the selection of attended stimulus attributes, but little is known about how this is achieved. In particular, it is not clear whether attending to an attribute such as a color (blue vs. green) or motion direction (up vs. down) first biases all neural structures coding that dimension (color/motion) before biasing the specific attribute, or if the top-down signals directly bias only the attended attribute. Using EEG and multivariate decoding, we report that top-down attention control follows a hierarchical organization: first, the broader attended feature dimension is biased, which is followed by the biasing of the specific feature attribute.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Dynamic Formation of a Posterior-to-Anterior Peak-Alpha-Frequency Gradient Driven by Two Distinct Processes

Peak-alpha frequency varies across individuals and mental states, but it also forms a negative gradient from posterior to anterior regions in association with increases in cortical thickness and connectivity, reflecting a cortical hierarchy in temporal integration. Tracking the spatial standard deviation of peak-alpha frequency in scalp EEG, we observed that a posterior-to-anterior gradient dynamically formed and dissolved. Periods of high spatial standard deviation yielded robustly negative posterior-to-anterior gradients-the "gradient state"-while periods of low spatial standard deviation yielded globally converged peak-alpha frequency-the "uniform state." The state variations were characterized by a combination of slow (0.3-0.5 Hz) oscillations and random-walk-like fluctuations. They were relatively independently correlated with peak-alpha frequency variations in anterior regions and peak-alpha power variations in central regions driven by posterior regions (together accounting for ∼50% of the state variations), suggesting that two distinct mechanisms modulate the state variations: an anterior mechanism that directly adjusts peak-alpha frequencies and a posterior-central mechanism that indirectly adjusts them by influencing synchronization. The state variations likely reflect general operations as their spatiotemporal characteristics remained unchanged while participants engaged in a variety of tasks (breath focus, vigilance, working memory, mental arithmetic, and generative thinking) with their eyes closed or watched a silent nature video. The ongoing state variations may dynamically balance two global processing modes, one that facilitates greater temporal integration (and potentially also information influx) toward anterior regions in the gradient state and the other that facilitates flexible global communication (via phase locking) in the uniform state.

Networks of cortical activity show graded responses to perinatal asphyxia

BACKGROUND

Perinatal asphyxia often leads to hypoxic-ischemic encephalopathy (HIE) with a high risk of neurodevelopmental consequences. While moderate and severe HIE link to high morbidity, less is known about brain effects of perinatal asphyxia with no or only mild HIE. Here, we test the hypothesis that cortical activity networks in the newborn infants show a dose-response to asphyxia.

METHODS

We performed EEG recordings for infants with perinatal asphyxia/HIE of varying severity (n = 52) and controls (n = 53) and examined well-established computational metrics of cortical network activity.

RESULTS

We found graded alterations in cortical activity networks according to severity of asphyxia/HIE. Furthermore, our findings correlated with early clinical recovery measured by the time to attain full oral feeding.

CONCLUSION

We show that both local and large-scale correlated cortical activity are affected by increasing severity of HIE after perinatal asphyxia, suggesting that HIE and perinatal asphyxia are better represented as a continuum rather than the currently used discreet categories. These findings imply that automated computational measures of cortical function may be useful in characterizing the dose effects of adversity in the neonatal brain; such metrics hold promise for benchmarking clinical trials via patient stratification or as early outcome measures.

IMPACT

Perinatal asphyxia causes every fourth neonatal death worldwide and provides a diagnostic and prognostic challenge for the clinician. We report that infants with perinatal asphyxia show specific graded responses in cortical networks according to severity of asphyxia and ensuing hypoxic-ischaemic encephalopathy. Early EEG recording and automated computational measures of brain function have potential to help in clinical evaluation of infants with perinatal asphyxia.

The scalp prefrontal-limbic functional connectivity moderates stress-related rumination effects on stress recovery

BACKGROUND

Mood disorders are often associated with hypothalamic-pituitary-adrenal (HPA) axis dysfunction, and rumination has been implicated in delayed cortisol recovery. However, research findings on the impact of rumination on cortisol recovery have been inconsistent. The moderating effects of scalp prefrontal-limbic connections on the relationship between rumination and cortisol recovery may explain these discrepancies.

METHOD

Acute stress was induced by a 5-min simulated job interview. Salivary samples and affective ratings were collected at seven pre-determined time points. After the simulated job interview, 35 healthy adult participants were randomly assigned to either the rumination condition (n = 17) or the distraction condition (n = 18).

RESULTS

Inducing stress and rumination led to increased cortisol levels, negative mood, and state rumination. Compared with the distraction group, the rumination group displayed delayed cortisol recovery and decreased scalp prefrontal-limbic connectivities, that is, left ventrolateral prefrontal cortex (LVLPFC) and left temporal area (LTMP) [ps < .05], and right dorsolateral prefrontal cortex (RDLPFC) and anterior cingulate cortex (ACC) [ps < .05]. The relationship between rumination and cortisol recovery was moderated by connectivities between the left dorsolateral prefrontal cortex (LDLPFC) and LTMP, RDLPFC and LTMP, LDLPFC and ACC, and RDLPFC and ACC [B = -0.98 to -0.35, SE = 0.15-0.34, ps < .05]. Higher rumination combined with reduced scalp prefrontal-limbic connectivities to predict delayed cortisol recovery.

CONCLUSION

The current findings suggest that scalp prefrontal-limbic connectivity is a neural underpinning related to emotion regulation for the effects of state rumination on stress recovery. These findings also provide a potential target for non-invasive intervention in HPA axis dysregulation.

Errors elicit frontoparietal theta-gamma coupling that is modulated by endogenous estradiol levels

Cognitive control-related error monitoring is intimately involved in behavioral adaptation, learning, and individual differences in a variety of psychological traits and disorders. Accumulating evidence suggests that a focus on women's health and ovarian hormones is critical to the study of such cognitive brain functions. Here we sought to identify a novel index of error monitoring using a time-frequency based phase amplitude coupling (t-f PAC) measure and examine its modulation by endogenous levels of estradiol in females. Forty-three healthy, naturally cycling young adult females completed a flanker task while continuous electroencephalogram was recorded on four occasions across the menstrual cycle. Results revealed significant error-related t-f PAC between theta phase generated in fronto-central areas and gamma amplitude generated in parietal-occipital areas. Moreover, this error-related theta-gamma coupling was enhanced by endogenous levels of estradiol both within females across the cycle as well as between females with higher levels of average circulating estradiol. While the role of frontal midline theta in error processing is well documented, this paper extends the extant literature by illustrating that error monitoring involves the coordination between multiple distributed systems with the slow midline theta activity modulating the power of gamma-band oscillatory activity in parietal regions. They further show enhancement of inter-regional coupling by endogenous estradiol levels, consistent with research indicating modulation of cognitive control neural functions by the endocrine system in females. Together, this work identifies a novel neurophysiological marker of cognitive control-related error monitoring in females that has implications for neuroscience and women's health.

Conflict monitoring and emotional processing in 3,4-methylenedioxymethamphetamine (MDMA) and methamphetamine users - A comparative neurophysiological study

In stimulant use and addiction, conflict control processes are crucial for regulating substance use and sustaining abstinence, which can be particularly challenging in social-affective situations. Users of methamphetamine (METH, "Ice") and 3,4-methylenedioxymethamphetamine (MDMA, "Ecstasy") both experience impulse control deficits, but display different social-affective and addictive profiles. We thus aimed to compare the effects of chronic use of the substituted amphetamines METH and MDMA on conflict control processes in different social-affective contexts (i.e., anger and happiness) and investigate their underlying neurophysiological mechanisms. For this purpose, chronic but recently abstinent users of METH (n = 38) and MDMA (n = 42), as well as amphetamine-naïve healthy controls (n = 83) performed an emotional face-word Stroop paradigm, while event-related potentials (ERPs) were recorded. Instead of substance-specific differences, both MDMA and METH users showed smaller behavioral effects of cognitive-emotional conflict processing (independently of emotional valence) and selective deficits in emotional processing of anger content. Both effects were underpinned by stronger P3 ERP modulations suggesting that users of substituted amphetamines employ altered stimulus-response mapping and decision-making. Given that these processes are modulated by noradrenaline and that both MDMA and METH use may be associated with noradrenergic dysfunctions, the noradrenaline system may underlie the observed substance-related similarities. Better understanding the functional relevance of this currently still under-researched neurotransmitter and its functional changes in chronic users of substituted amphetamines is thus an important avenue for future research.

Electrophysiological study of visual processing in children with cochlear implants

Electrophysiological studies of congenitally deaf children and adults have reported atypical visual evoked potentials (VEPs) which have been associated with both behavioral enhancements of visual attention as well as poorer performance and outcomes in tests of spoken language speech processing. This pattern has often been interpreted as a maladaptive consequence of early auditory deprivation, whereby a remapping of auditory cortex by the visual system ultimately reduces resources necessary for optimal rehabilitative outcomes of spoken language acquisition and use. Making use of a novel electrophysiological paradigm, we compare VEPs in children with severe to profound congenital deafness who received a cochlear implant(s) prior to 31 months (n = 28) and typically developing age matched controls (n = 28). We observe amplitude enhancements and in some cases latency differences in occipitally expressed P1 and N1 VEP components in CI-using children as well as an early frontal negativity, N1a. We relate these findings to developmental factors such as chronological age and spoken language understanding. We further evaluate whether VEPs are additionally modulated by auditory stimulation. Collectively, these data provide a means to examine the extent to which atypical VEPs are consistent with prior accounts of maladaptive cross-modal plasticity. Our results support a view that VEP changes reflect alterations to visual-sensory attention and saliency mechanisms rather than a re-mapping of auditory cortex. The present data suggests that early auditory deprivation may have temporally prolonged effects on visual system processing even after activation and use of cochlear implant.

A phase-shifting anterior-posterior network organizes global phase relations

Prior research has identified a variety of task-dependent networks that form through inter-regional phase-locking of oscillatory activity that are neural correlates of specific behaviors. Despite ample knowledge of task-specific functional networks, general rules governing global phase relations have not been investigated. To discover such general rules, we focused on phase modularity, measured as the degree to which global phase relations in EEG comprised distinct synchronized clusters interacting with one another at large phase lags. Synchronized clusters were detected with a standard community-detection algorithm, and the degree of phase modularity was quantified by the index q. Notably, we found that the mechanism controlling phase modularity is remarkably simple. A network comprising anterior-posterior long-distance connectivity coherently shifted phase relations from low-angles (|Δθ| < π/4) in low-modularity states (bottom 5% in q) to high-angles (|Δθ| > 3π/4) in high-modularity states (top 5% in q), accounting for fluctuations in phase modularity. This anterior-posterior network may play a fundamental functional role as (1) it controls phase modularity across a broad range of frequencies (3-50 Hz examined) in different behavioral conditions (resting with the eyes closed or watching a silent nature video) and (2) neural interactions (measured as power correlations) in beta-to-gamma bands were consistently elevated in high-modularity states. These results may motivate future investigations into the functional roles of phase modularity as well as the anterior-posterior network that controls it.

Feedback negativity and feedback-related P3 in individuals at risk for depression: Comparing surface potentials and current source densities

Blunted responses to reward feedback have been linked to major depressive disorder (MDD) and depression risk. Using a monetary incentive delay task (win, loss, break-even), we investigated the impact of family risk for depression and lifetime history of MDD and anxiety disorder with 72-channel electroencephalograms (EEG) recorded from 29 high-risk and 32 low-risk individuals (15-58 years, 30 male). Linked-mastoid surface potentials (ERPs) and their corresponding reference-free current source densities (CSDs) were quantified by temporal principal components analysis (PCA). Each PCA solution revealed a midfrontal feedback negativity (FN; peak around 310 ms) and a posterior feedback-P3 (fb-P3; 380 ms) as two distinct reward processing stages. Unbiased permutation tests and multilevel modeling of component scores revealed greater FN to loss than win and neutral for all stratification groups, confirming FN sensitivity to valence. Likewise, all groups had greater fb-P3 to win and loss than neutral, confirming that fb-P3 indexes motivational salience and allocation of attention. By contrast, group effects were subtle, dependent on data transformation (ERP, CSD), and did not confirm reduced FN or fb-P3 for at-risk individuals. Instead, CSD-based fb-P3 was overall reduced in individuals with than without MDD history, whereas ERP-based fb-P3 was greater for high-risk individuals than for low-risk individuals for monetary, but not neutral outcomes. While the present findings do not support blunted reward processing in depression and depression risk, our side-by-side comparison underscores how the EEG reference choice affects the characterization of subtle group differences, strongly advocating the use of reference-free techniques.

The effects of acute and chronic noise trauma on stimulus-evoked activity across primary auditory cortex layers

Exposure to intense noise environments is a major cause of sensorineural hearing loss and auditory perception disorders, such as tinnitus and hyperacusis, which may have a central origin. The effects of noise-induced hearing loss on the auditory cortex have been documented in many studies. One limitation of these studies, however, is that the effects of noise trauma have been mostly studied at the granular layer (i.e, the main cortical recipient of thalamic input), while the cortex is a very complex structure, with six different layers each having its own pattern of connectivity and role in sensory processing. The present study aims to investigate the effects of acute and chronic noise trauma on the laminar pattern of stimulus-evoked activity in the primary auditory cortex of the anesthetized guinea pig. We show that acute and chronic noise trauma are both followed by an increase in stimulus-evoked cortical responses, mostly in the granular and supragranular layers. The cortical responses are more monotonic as a function of the intensity level after noise trauma. There was minimal change, if any, in local field potential (LFP) amplitude after acute noise trauma, while LFP amplitude was enhanced after chronic noise trauma. Finally, LFP and the current source density analysis suggest that acute but more specifically chronic noise trauma is associated with the emergence of a new sink in the supragranular layer. This result suggests that supragranular layers become a major input recipient. We discuss the possible mechanisms and functional implications of these changes.NEW & NOTEWORTHY Our study shows that cortical activity is enhanced after trauma and that the sequence of cortical column activation during stimulus-evoked response is altered, i.e. the supragranular layer becomes a major input recipient. We speculate that these large cortical changes may play a key role in the auditory hypersensitivity (hyperacusis) that can be triggered after noise trauma in human subjects.

Event-related EEG synchronization and desynchronization in patients with obsessive-compulsive disorder

Symptoms in patients with obsessive-compulsive disorder (OCD) are associated with impairment in cognitive control, attention, and action inhibition. We investigated OCD group differences relative to healthy subjects in terms of event-related alpha and beta range synchronization (ERS) and desynchronization (ERD) during a visually cued Go/NoGo task. Subjects were 62 OCD patients and 296 healthy controls (HC). The OCD group in comparison with HC, showed a changed value of alpha/beta oscillatory power over the central cortex, in particular, an increase in the alpha/beta ERD over the central-parietal cortex during the interstimulus interval (Cue condition) as well as changes in the postmovement beta synchronization topography and frequency. Over the frontal cortex, the OCD group showed an increase in magnitude of the beta ERS in NoGo condition. Within the parietal-occipital ERS/ERD modulations, the OCD group showed an increase in the alpha/beta ERD over the parietal cortex after the presentation of the visual stimuli as well as a decrease in the beta ERD over the occipital cortex after the presentation of the Cue and Go stimuli. The specific properties in the ERS/ERD patterns observed in the OCD group may reflect high involvement of the frontal and central cortex in action preparation and action inhibition processes and, possibly, in maintaining the motor program, which might be a result of the dysfunction of the cortico-striato-thalamo-cortical circuits involving prefrontal cortex. The data about enhanced involvement of the parietal cortex in the evaluation of the visual stimuli are in line with the assumption about overfocused attention in OCD.

Laminar neural dynamics of auditory evoked responses: Computational modeling of local field potentials in auditory cortex of non-human primates

Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms. In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition. In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.

Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex

Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.

Perception-Action Integration Is Altered in Functional Movement Disorders

BACKGROUND

Although functional neurological movement disorders (FMD) are characterized by motor symptoms, sensory processing has also been shown to be disturbed. However, how the integration of perception and motor processes, essential for the control of goal-directed behavior, is altered in patients with FMD is less clear. A detailed investigation of these processes is crucial to foster a better understanding of the pathophysiology of FMD and can systematically be achieved in the framework of the theory of event coding (TEC).

OBJECTIVE

The aim was to investigate perception-action integration processes on a behavioral and neurophysiological level in patients with FMD.

METHODS

A total of 21 patients and 21 controls were investigated with a TEC-related task, including concomitant electroencephalogram (EEG) recording. We focused on EEG correlates established to reflect perception-action integration processes. Temporal decomposition allowed to distinguish between EEG codes reflecting sensory (S-cluster), motor (R-cluster), and integrated sensory-motor processing (C-cluster). We also applied source localization analyses.

RESULTS

Behaviorally, patients revealed stronger binding between perception and action, as evidenced by difficulties in reconfiguring previously established stimulus-response associations. Such hyperbinding was paralleled by a modulation of neuronal activity clusters, including reduced C-cluster modulations of the inferior parietal cortex and altered R-cluster modulations in the inferior frontal gyrus. Correlations of these modulations with symptom severity were also evident.

CONCLUSIONS

Our study shows that FMD is characterized by altered integration of sensory information with motor processes. Relations between clinical severity and both behavioral performance and neurophysiological abnormalities indicate that perception-action integration processes are central and a promising concept for the understanding of FMD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Extracting electromyographic signals from multi-channel LFPs using independent component analysis without direct muscular recording

Electromyography (EMG) has been commonly used for the precise identification of animal behavior. However, it is often not recorded together with in vivo electrophysiology due to the need for additional surgeries and setups and the high risk of mechanical wire disconnection. While independent component analysis (ICA) has been used to reduce noise from field potential data, there has been no attempt to proactively use the removed "noise," of which EMG signals are thought to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed without direct EMG recording using the "noise" ICA component from local field potentials. The extracted component is highly correlated with directly measured EMG, termed IC-EMG. IC-EMG is useful for measuring an animal's sleep/wake, freezing response, and non-rapid eye movement (NREM)/REM sleep states consistently with actual EMG. Our method has advantages in precise and long-term behavioral measurement in wide-ranging in vivo electrophysiology experiments.

Community detection in multi-frequency EEG networks

Functional connectivity networks of the human brain are commonly studied using tools from complex network theory. Existing methods focus on functional connectivity within a single frequency band. However, it is well-known that higher order brain functions rely on the integration of information across oscillations at different frequencies. Therefore, there is a need to study these cross-frequency interactions. In this paper, we use multilayer networks to model functional connectivity across multiple frequencies, where each layer corresponds to a different frequency band. We then introduce the multilayer modularity metric to develop a multilayer community detection algorithm. The proposed approach is applied to electroencephalogram (EEG) data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed following the correct response.

Enhanced temporal resolution of vision in action video game players

Video game play has been suggested to improve visual and attention processing. Nevertheless, while action video game play is highly dynamic, there is scarce research on how information is temporally discriminated at the millisecond level. This cross-sectional study investigates whether temporal discrimination at the millisecond level in vision varies across action video game players (VGPs; N = 23) and non-video game players (NVGPs; N = 23). Participants discriminated synchronous from asynchronous onsets of two visual targets in virtual reality, while their EEG and oculomotor movements were recorded. Results show an increased sensitivity to short asynchronies (11, 33 and 66 ms) in VGPs compared with NVGPs, which was especially marked at the start of the task, suggesting better temporal discrimination abilities. Pre-targets oculomotor freezing - the inhibition of small fixational saccades - was associated with correct temporal discrimination, probably revealing attentional preparation. However, this parameter did not differ between groups. EEG and reconstruction analyses suggest that the enhancement of temporal discrimination in VGPs during temporal discrimination is related to parieto-occipital processing, and a reduction of alpha-band (8-14 Hz) power and inter-trial phase coherence. Overall, the study reveals an enhanced ability in action video game players to discriminate in time visual events in close temporal proximity combined with reduced alpha-band oscillatory activities. Consequently, playing action video games is associated with an improved temporal resolution of vision.

Development of auditory change-detection and attentional capture, and their relation to inhibitory control

EEG methods offer a promising approach to study the development of attention or attention-related processes such as change-detection and attentional capture. However, the development of these attention processes from early to middle childhood is not well understood. In the current study, we utilized a passive three-stimulus oddball paradigm to examine age-related changes in auditory change-detection and attentional capture in a large sample of children across childhood (N = 475; 249 female, 226 male; Mage  = 6.71; SDage  = 2.22; Rangeage  = 4.01-11.5 years). Conventional ERP analyses revealed no age-related changes in change detection (mismatch negativity) and attentional capture (P3a) components, but we observed age-related reductions in late automatic processing of auditory change (late discriminative negativity). However, when utilizing time-frequency analyses, we observed developmental increases in frontocentral signal strength (power) and consistency (inter-trial phase synchrony) in delta and theta bands in response to novel sounds. Such frontocentral delta/theta responses have been linked in prior work to cognitive control. To further examine this possibility, we examined relations with inhibitory control. Results revealed that increased consistency in theta in response to novel sounds was related to improved inhibitory control. Together, our results advance our understanding of the development of attention in childhood. Moreover, they demonstrate the contributions of time-frequency approaches to studying neurocognitive development. Finally, our results highlight the utility of neuroimaging paradigms that have low cognitive and motor demands to study the development of psychological processes.

Tool use acquisition induces a multifunctional interference effect during object processing: evidence from the sensorimotor mu rhythm

A fundamental characteristic of human development is acquiring and accumulating tool use knowledge through observation and sensorimotor experience. Recent studies showed that, in children and adults, different action possibilities to grasp-to-move and grasp-to-use objects generate a conflict that extinguishes neural motor resonance phenomena during visual object processing. In this study, a training protocol coupled with EEG recordings was administered in virtual reality to healthy adults to evaluate whether a similar conflict occurs between novel tool use knowledge. Participants perceived and manipulated two novel 3D tools trained beforehand with either single or double-usage. A weaker reduction of mu-band (10-13 Hz) power, accompanied by a reduced inter-trial phase coherence, was recorded during the perception of the tool associated with the double-usage. These effects started within the first 200 ms of visual object processing and were predominantly recorded over the left motor system. Furthermore, interacting with the double usage tool delayed grasp-to-reach movements. The results highlight a multifunctional interference effect, such as tool use acquisition reduces the neural motor resonance phenomenon and inhibits the activation of the motor system during subsequent object recognition. These results imply that learned tool use information guides sensorimotor processes of objects.

Towards Practical BCI-Driven Wheelchairs: A Systematic Review Study

The use of brain signals in controlling wheelchairs is a promising solution for many disabled individuals, specifically those who are suffering from motor neuron disease affecting the proper functioning of their motor units. Almost two decades since the first work, the applicability of EEG-driven wheelchairs is still limited to laboratory environments. In this work, a systematic review study has been conducted to identify the state-of-the-art and the different models adopted in the literature. Furthermore, a strong emphasis is devoted to introducing the challenges impeding a broad use of the technology as well as the latest research trends in each of those areas.

Abnormal gait and motor cortical processing in drug-resistant juvenile myoclonic epilepsy

BACKGROUND

Juvenile myoclonic epilepsy (JME) is characterized by generalized seizures. Nearly 30% of JME patients are drug-resistant (DR-JME), indicating a widespread cortical dysfunction. Walking is an important function that necessitates orchestrated coordination of frontocentral cortical regions. However, gait alterations in JME have been scarcely investigated. Our aim was to assess changes in gait and motor-evoked responses in DR-JME patients.

METHODS

Twenty-nine subjects (11 JME drug-responder, 8 DR-JME, and 10 healthy controls) underwent a gait analyses during usual walking and dual-task walking. Later, subjects underwent 64-channel EEG recordings while performing a simple motor task. We calculated the motor-evoked current source densities (CSD) at a priori chosen cortical regions. Gait and CSD measures were compared between groups and tasks using mixed model analysis.

RESULTS

DR-JME patients demonstrated an altered gait pattern that included slower gait speed (p = .018), reduced cadence (p = .003), and smaller arm-swing amplitude (p = .011). The DR-JME group showed higher motor-evoked CSD in the postcentral gyri compared to responders (p = .049) and both JME groups showed higher CSD in the superior frontal gyri compared to healthy controls (p < .011). Moreover, higher CSD in the superior frontal gyri correlated with worse performance in dual-task walking (r > |-0.494|, p < .008).

CONCLUSIONS

These alterations in gait and motor-evoked responses in DRE-JME patients reflect a more severe dysfunction of motor-cognitive neural processing in frontocentral regions, leading to poorer gait performance. Further studies are needed to investigate the predictive value of altered gait and cortical motor processing as biomarkers for poor response to treatment in JME and other epilepsy syndromes.

The novel frontal alpha asymmetry factor and its association with depression, anxiety, and personality traits

Frontal alpha asymmetry (FAA) is widely examined in EEG research, yet a procedural consensus on its assessment is lacking. In this study, we tested a latent factorial approach to measure FAA. We assessed resting-state FAA at broad, low, and high alpha bands (8-13; 8-10.5; and 11-13 Hz) using mastoids as reference electrodes and Current Source Density (CSD) transformation (N = 139 non-clinical participants). From mastoid-referenced data, we extracted a frontal alpha asymmetry factor (FAAf) and a parietal factor (PAAf) subjecting all asymmetry indices to a varimax-rotated, principal component analysis. We explored split-half reliability and discriminant validity of the mastoid factors and the mastoid and CSD raw asymmetry indices (F3/4, F7/8, P3/4, and P7/8). Both factor and raw scores reached an excellent split-half reliability (>.99), but only the FAAf reached the maximum discriminant validity from parietal scores. Next, we explored the correlations of latent factor and raw FAA scores with symptoms of depression, anxiety, and personality traits to determine which associations were driven by FAA after variance from parietal activity was removed. After correcting for false discovery rate, only FAAf at the low alpha band was negatively associated with depression symptoms (a latent CES-D factor) and significantly diverged from PAAf's association with depression symptoms. With respect to personality traits, only CSD-transformed F7/8 was positively correlated with Conscientiousness and significantly diverged from the correlations between Conscientiousness and P3/4 and P7/8. Overall, the latent factor approach shows promise for isolating functionally distinct resting-state EEG signatures, although further research is needed to examine construct validity.

A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain

The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Shifting Baselines: Longitudinal Reductions in EEG Beta Band Power Characterize Resting Brain Activity with Intensive Meditation

Objectives

A core assumption of meditation training is that cognitive capacities developed during formal practice will transfer to other contexts or activities as expertise develops over time. This implies that meditation training might influence domain-general neurocognitive systems, the spontaneous activity of which should be reflected in the dynamics of the resting brain. Previous research has demonstrated that 3 months of meditation training led to reductions in EEG beta band power during mindfulness of breathing practice. The current study extends these findings to ask whether concomitant shifts in power are observed during 2 min of eyes closed rest, when participants are not explicitly engaged in formal meditation.

Methods

Experienced meditation practitioners were randomly assigned to practice 3 months of focused attention meditation in a residential retreat, or to serve as waitlist controls. The waitlist controls later completed their own 3-month retreat. Permutation-based cluster analysis of 88-channel resting EEG data was used to test for spectral changes in spontaneous brain activity over the course of the retreats.

Results

Longitudinal reductions in EEG power in the beta frequency range were identified and replicated across the two independent training periods. Less robust reductions were also observed in the high alpha frequency range, and in individual peak alpha frequency. These changes closely mirror those previously observed during formal mindfulness of breathing meditation practice.

Conclusions

These findings suggest that the neurocognitive effects of meditation training can extend beyond the bounds of formal practice, influencing the spontaneous activity of the resting brain. Rather than serving as an invariant baseline, resting states might carry meaningful training-related effects, blurring the line between state and trait change.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12671-022-01974-9.

Electrophysiological Examination of Ambient Speech Processing in Children With Cochlear Implants

PURPOSE

This research examined the expression of cortical auditory evoked potentials in a cohort of children who received cochlear implants (CIs) for treatment of congenital deafness (n = 28) and typically hearing controls (n = 28).

METHOD

We make use of a novel electroencephalography paradigm that permits the assessment of auditory responses to ambiently presented speech and evaluates the contributions of concurrent visual stimulation on this activity.

RESULTS

Our findings show group differences in the expression of auditory sensory and perceptual event-related potential components occurring in 80- to 200-ms and 200- to 300-ms time windows, with reductions in amplitude and a greater latency difference for CI-using children. Relative to typically hearing children, current source density analysis showed muted responses to concurrent visual stimulation in CI-using children, suggesting less cortical specialization and/or reduced responsiveness to auditory information that limits the detection of the interaction between sensory systems.

CONCLUSION

These findings indicate that even in the face of early interventions, CI-using children may exhibit disruptions in the development of auditory and multisensory processing.

Peak frequency of the sensorimotor mu rhythm varies with autism-spectrum traits

Autism spectrum disorder (ASD) is a neurodevelopmental syndrome characterized by impairments in social perception and communication. Growing evidence suggests that the relationship between deficits in social perception and ASD may extend into the neurotypical population. In electroencephalography (EEG), high autism-spectrum traits in both ASD and neurotypical samples are associated with changes to the mu rhythm, an alpha-band (8–12 Hz) oscillation measured over sensorimotor cortex which typically shows reductions in spectral power during both one’s own movements and observation of others’ actions. This mu suppression is thought to reflect integration of perceptual and motor representations for understanding of others’ mental states, which may be disrupted in individuals with autism-spectrum traits. However, because spectral power is usually quantified at the group level, it has limited usefulness for characterizing individual variation in the mu rhythm, particularly with respect to autism-spectrum traits. Instead, individual peak frequency may provide a better measure of mu rhythm variability across participants. Previous developmental studies have linked ASD to slowing of individual peak frequency in the alpha band, or peak alpha frequency (PAF), predominantly associated with selective attention. Yet individual variability in the peak mu frequency (PMF) remains largely unexplored, particularly with respect to autism-spectrum traits. Here we quantified peak frequency of occipitoparietal alpha and sensorimotor mu rhythms across neurotypical individuals as a function of autism-spectrum traits. High-density 128-channel EEG data were collected from 60 participants while they completed two tasks previously reported to reliably index the sensorimotor mu rhythm: motor execution (bimanual finger tapping) and action observation (viewing of whole-body human movements). We found that individual measurement in the peak oscillatory frequency of the mu rhythm was highly reliable within participants, was not driven by resting vs. task states, and showed good correlation across action execution and observation tasks. Within our neurotypical sample, higher autism-spectrum traits were associated with slowing of the PMF, as predicted. This effect was not likely explained by volume conduction of the occipitoparietal PAF associated with attention. Together, these data support individual peak oscillatory alpha-band frequency as a correlate of autism-spectrum traits, warranting further research with larger samples and clinical populations.

Dynamics of hidden brain states when people solve verbal puzzles

When people try to solve a problem, they go through distinct steps (encoding, ideation, evaluation, etc.) recurrently and spontaneously. To disentangle different cognitive processes that unfold throughout a trial, we applied an unsupervised machine learning method to electroencephalogram (EEG) data continuously recorded while 39 participants attempted 153 Compound Remote Associates problems (CRA). CRA problems are verbal puzzles that can be solved in either insight-leaning or analysis-leaning manner. We fitted a Hidden Markov Model to the time-frequency transformed EEG signals and decoded each trial as a time-resolved state sequence. The model characterizes hidden brain states with spectrally resolved power topography. Seven states were identified with distinct activation patterns in the theta (4-7 Hz), alpha (8-9 Hz and 10-13 Hz), and gamma (25-50 Hz) bands. Notably, a state featuring widespread activation only in alpha-band frequency emerged, from this data-driven approach, which exhibited dynamic characteristics associated with specific temporal stages and outcomes (whether solved with insight or analysis) of the trials. The state dynamics derived from the model overlap and extend previous literature on the cognitive function of alpha oscillation: the "alpha-state" probability peaks before stimulus onset and decreases before response. In trials solved with insight, relative to solved with analysis, the alpha-state is more likely to be visited and maintained during preparation and solving periods, and its probability declines more sharply immediately preceding a response. This novel paradigm provides a way to extract dynamic features that characterize problem-solving stages and potentially provide a novel window into the nature of the underlying cognitive processes.

Time-frequency analysis methods and their application in developmental EEG data

EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, most developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses (e.g., connectivity). Despite their unique contributions, a literature review of this journal reveals that time-frequency analyses of EEG are yet to be embraced by the developmental cognitive neuroscience field. This manuscript presents a conceptual introduction to time-frequency analyses for developmental researchers. To facilitate the use of time-frequency analyses, we include a tutorial of accessible scripts, based on Cohen (2014), to calculate time-frequency power (signal strength), inter-trial phase synchrony (signal consistency), and two types of phase-based connectivity (inter-channel phase synchrony and weighted phase lag index).

Time-frequency dynamics of error monitoring in childhood: An EEG study

Error monitoring allows individuals to monitor and adapt their behavior by detecting errors. Error monitoring is thought to develop throughout childhood and adolescence. However, most of this evidence comes from studies in late childhood and adolescence utilizing event-related potentials (ERPs). The current study utilizes time-frequency (TF) and connectivity analyses to provide a comprehensive examination of age-related changes in error-monitoring processes across early childhood (N = 326; 50.9% females; 4-9 years). ERP analyses indicated the presence of the error-related negativity (ERN) and error positivity (Pe) across all ages. Results showed no error-specific age-related changes in the ERN and the Pe. However, TF analyses suggested error-related frontocentral responses in delta and theta signal strength (power), delta consistency (intertrial phase synchrony), and delta synchrony (interchannel phase synchrony) between frontrocentral and frontolateral clusters-all of which increased with age. Additionally, the current study examines the reliability and effect size estimates of the ERP and TF measures. For most measures, more trials were needed to achieve acceptable reliability than what is commonly used in the psychophysiological literature. Resources to facilitate the measurement and reporting of reliability are provided. Overall, findings highlight the utility of TF analyses and provide useful information for future studies examining the development of error monitoring.

Phase-dependent local brain states determine the impact of image-guided transcranial magnetic stimulation on motor network electroencephalographic synchronization

Recent studies have synchronized transcranial magnetic stimulation (TMS) application with pre-defined brain oscillatory phases showing how brain response to perturbation depends on the brain state. However, none have investigated whether phase-dependent TMS can possibly modulate connectivity with homologous distant brain regions belonging to the same network. In the framework of network-targeted TMS, we investigated whether stimulation delivered at a specific phase of ongoing brain oscillations might favour stronger cortico-cortical (c-c) synchronization of distant network nodes connected to the stimulation target. Neuronavigated TMS pulses were delivered over the primary motor cortex (M1) during ongoing electroencephalography recording in 24 healthy individuals over two repeated sessions 1 month apart. Stimulation effects were analysed considering whether the TMS pulse was delivered at the time of a positive (peak) or negative (trough) phase of μ-frequency oscillation, which determines c-c synchrony within homologous areas of the sensorimotor network. Diffusion weighted imaging was used to study c-c connectivity within the sensorimotor network and identify contralateral regions connected with the stimulation spot. Depending on when during the μ-activity the TMS-pulse was applied (peak or trough), its impact on inter-hemispheric network synchrony varied significantly. Higher M1-M1 phase-lock synchronization after the TMS-pulse (0-200 ms) in the μ-frequency band was found for trough compared to peak stimulation trials in both study visits. Phase-dependent TMS delivery might be crucial not only to amplify local effects but also to increase the magnitude and reliability of the response to the external perturbation, with implications for interventions aimed at engaging more distributed functional brain networks. KEY POINTS: Synchronized transcranial magnetic stimulation (TMS) pulses with pre-defined brain oscillatory phases allow evaluation of the impact of brain states on TMS effects. TMS pulses over M1 at the negative peak of the μ-frequency band induce higher phase-lock synchronization with interconnected contralateral homologous regions. Cortico-cortical synchronization changes are linearly predicted by the fibre density and cross-section of the white matter tract that connects the two brain regions. Phase-dependent TMS delivery might be crucial not only to amplify local effects but also to increase the magnitude and reliability of within-network synchronization.

Changes in the spatiotemporal pattern of spontaneous activity across a cortical column after noise trauma

In the auditory modality, noise trauma has often been used to investigate cortical plasticity as it causes cochlear hearing loss. One limitation of these past studies, however, is that the effects of noise trauma have been mostly documented at the granular layer, which is the main cortical recipient of thalamic inputs. Importantly, the cortex is composed of six different layers each having its own pattern of connectivity and specific role in sensory processing. The present study aims at investigating the effects of acute and chronic noise trauma on the laminar pattern of spontaneous activity in primary auditory cortex of the anesthetized guinea pig. We show that spontaneous activity is dramatically altered across cortical layers after acute and chronic noise-induced hearing loss. First, spontaneous activity was globally enhanced across cortical layers, both in terms of firing rate and amplitude of spike-triggered average of local field potentials. Second, current source density on (spontaneous) spike-triggered average of local field potentials indicates that current sinks develop in the supra- and infragranular layers. These latter results suggest that supragranular layers become a major input recipient and that the propagation of spontaneous activity over a cortical column is greatly enhanced after acute and chronic noise-induced hearing loss. We discuss the possible mechanisms and functional implications of these changes.

Auditory Information Accelerates the Visuomotor Reaction Speed of Elite Badminton Players in Multisensory Environments

Although vision is the dominating sensory system in sports, many situations require multisensory integration. Faster processing of auditory information in the brain may facilitate time-critical abilities such as reaction speed however previous research was limited by generic auditory and visual stimuli that did not consider audio-visual characteristics in ecologically valid environments. This study investigated the reaction speed in response to sport-specific monosensory (visual and auditory) and multisensory (audio-visual) stimulation. Neurophysiological analyses identified the neural processes contributing to differences in reaction speed. Nineteen elite badminton players participated in this study. In a first recording phase, the sound profile and shuttle speed of smash and drop strokes were identified on a badminton court using high-speed video cameras and binaural recordings. The speed and sound characteristics were transferred into auditory and visual stimuli and presented in a lab-based experiment, where participants reacted in response to sport-specific monosensory or multisensory stimulation. Auditory signal presentation was delayed by 26 ms to account for realistic audio-visual signal interaction on the court. N1 and N2 event-related potentials as indicators of auditory and visual information perception/processing, respectively were identified using a 64-channel EEG. Despite the 26 ms delay, auditory reactions were significantly faster than visual reactions (236.6 ms vs. 287.7 ms, p < 0.001) but still slower when compared to multisensory stimulation (224.4 ms, p = 0.002). Across conditions response times to smashes were faster when compared to drops (233.2 ms, 265.9 ms, p < 0.001). Faster reactions were paralleled by a lower latency and higher amplitude of the auditory N1 and visual N2 potentials. The results emphasize the potential of auditory information to accelerate the reaction time in sport-specific multisensory situations. This highlights auditory processes as a promising target for training interventions in racquet sports.

Graph Theoretic Analysis of Multilayer EEG Connectivity Networks

Over the past twenty years, functional connectivity of the human brain has been studied in detail using tools from complex network theory. These methods include graph theoretic metrics ranging from the micro-scale such as the degree of a node to the macro-scale such as the small worldness of the brain network. However, most of these network models focus on average activity within a time window of interest and given frequency band. Therefore, they cannot capture the changes in network connectivity across time and different frequency bands. Recently, multilayer brain networks have attracted a lot of attention as they can capture the full view of neuronal connectivity. In this paper, we introduce a multilayer view of the functional connectivity network of the brain, where each layer corresponds to a different frequency band. We construct multi-frequency connectivity networks from electroencephalogram data where the intra-layer edges are quantified by phase synchrony while the inter-layer edges are quantified by phase-amplitude coupling. We then introduce multilayer degree, participation coefficient and clustering coefficient to quantify the centrality of nodes across frequency layers and to identify the importance of different frequency bands. The proposed framework is applied to electroencephalogram data collected during a study of error monitoring in the human brain.

Behavioral inhibition and dual mechanisms of anxiety risk: Disentangling neural correlates of proactive and reactive control

Background:

Behavioral inhibition (BI) is a temperament style characterized by heightened reactivity and negative affect in response to novel people and situations, and it predicts anxiety problems later in life. However, not all BI children develop anxiety problems, and mounting evidence suggests that how one manages their cognitive resources (cognitive control) influences anxiety risk. The present study tests whether more (proactive control) or less (reactive control) planful cognitive strategies moderate relations between early BI and later anxiety.

Methods:

Participants included 112 adolescents (55% female; M_age = 15.4 years) whose temperament was assessed during toddlerhood. In adolescence, participants completed an AX Continuous Performance Test while electroencephalography was recorded to disentangle neural activity related to proactive (cue-locked P3b) and reactive (probe-locked N2) control.

Results:

Greater BI was associated with greater total anxiety scores only among adolescents with smaller ΔP3bs and larger ΔN2s—a pattern consistent with decreased reliance on proactive strategies and increased reliance on reactive strategies. Additionally, a larger ΔP3b was associated with greater total anxiety scores; however, this effect was largely explained by the fact that females tended to have larger ΔP3bs and greater anxiety than males.

Conclusions:

Early BI relates to risk for later anxiety specifically among adolescents who rely less on proactive strategies and more on reactive control strategies. Thus, cognitive control strategy moderates the association between developmental context (i.e., temperament) and later anxiety. The present study is the first to characterize how proactive and reactive control uniquely relate to pathways toward anxiety risk.

Spatiotemporal dynamics of maximal and minimal EEG spectral power

Oscillatory neural activities are prevalent in the brain with their phase realignment contributing to the coordination of neural communication. Phase realignments may have especially strong (or weak) impact when neural activities are strongly synchronized (or desynchronized) within the interacting populations. We report that the spatiotemporal dynamics of strong regional synchronization measured as maximal EEG spectral power-referred to as activation-and strong regional desynchronization measured as minimal EEG spectral power-referred to as suppression-are characterized by the spatial segregation of small-scale and large-scale networks. Specifically, small-scale spectral-power activations and suppressions involving only 2-7% (1-4 of 60) of EEG scalp sites were prolonged (relative to stochastic dynamics) and consistently co-localized in a frequency specific manner. For example, the small-scale networks for θ, α, β1, and β2 bands (4-30 Hz) consistently included frontal sites when the eyes were closed, whereas the small-scale network for γ band (31-55 Hz) consistently clustered in medial-central-posterior sites whether the eyes were open or closed. Large-scale activations and suppressions involving over 17-30% (10-18 of 60) of EEG sites were also prolonged and generally clustered in regions complementary to where small-scale activations and suppressions clustered. In contrast, intermediate-scale activations and suppressions (involving 7-17% of EEG sites) tended to follow stochastic dynamics and were less consistently localized. These results suggest that strong synchronizations and desynchronizations tend to occur in small-scale and large-scale networks that are spatially segregated and frequency specific. These synchronization networks may broadly segregate the relatively independent and highly cooperative oscillatory processes while phase realignments fine-tune the network configurations based on behavioral demands.

Tool use and function knowledge shape visual object processing

Perceiving the environment automatically informs how we can interact with it through affordance mechanisms. However, it remains unknown how our knowledge about the environment shapes how it is perceived. In this training study, we evaluated whether motor and function knowledge about novel objects affects visual object processing. Forty-three participants associated a usage or function to a novel object in interactive virtual reality while their EEG was recorded. Both usage and function influenced the mu-band (8-12 Hz) rhythms, suggesting that motor and function object information influence motor processing during object recognition. Learning the usage also prevented the reduction of the theta-band (4-8 Hz) rhythms recorded over the posterior cortical areas, suggesting a predominant top-down influence of tool use information on visuo-motor pathways. The modulation being specifically induced by learning an object usage, the results support further the embodied cognition approach rather than the reasoning-based approach of object processing.

Prefrontal Theta-Phase Synchronized Brain Stimulation With Real-Time EEG-Triggered TMS

Background

Theta-band neuronal oscillations in the prefrontal cortex are associated with several cognitive functions. Oscillatory phase is an important correlate of excitability and phase synchrony mediates information transfer between neuronal populations oscillating at that frequency. The ability to extract and exploit the prefrontal theta rhythm in real time in humans would facilitate insight into neurophysiological mechanisms of cognitive processes involving the prefrontal cortex, and development of brain-state-dependent stimulation for therapeutic applications.

Objectives

We investigate individual source-space beamforming-based estimation of the prefrontal theta oscillation as a method to target specific phases of the ongoing theta oscillations in the human dorsomedial prefrontal cortex (DMPFC) with real-time EEG-triggered transcranial magnetic stimulation (TMS). Different spatial filters for extracting the prefrontal theta oscillation from EEG signals are compared and additional signal quality criteria are assessed to take into account the dynamics of this cortical oscillation.

Methods

Twenty two healthy participants were recruited for anatomical MRI scans and EEG recordings with 18 composing the final analysis. We calculated individual spatial filters based on EEG beamforming in source space. The extracted EEG signal was then used to simulate real-time phase-detection and quantify the accuracy as compared to post-hoc phase estimates. Different spatial filters and triggering parameters were compared. Finally, we validated the feasibility of this approach by actual real-time triggering of TMS pulses at different phases of the prefrontal theta oscillation.

Results

Higher phase-detection accuracy was achieved using individualized source-based spatial filters, as compared to an average or standard Laplacian filter, and also by detecting and avoiding periods of low theta amplitude and periods containing a phase reset. Using optimized parameters, prefrontal theta-phase synchronized TMS of DMPFC was achieved with an accuracy of ±55°.

Conclusion

This study demonstrates the feasibility of triggering TMS pulses during different phases of the ongoing prefrontal theta oscillation in real time. This method is relevant for brain state-dependent stimulation in human studies of cognition. It will also enable new personalized therapeutic repetitive TMS protocols for more effective treatment of neuropsychiatric disorders.

Probabilistic, entropy-maximizing control of large-scale neural synchronization

Oscillatory neural activity is dynamically controlled to coordinate perceptual, attentional and cognitive processes. On the macroscopic scale, this control is reflected in the U-shaped deviations of EEG spectral-power dynamics from stochastic dynamics, characterized by disproportionately elevated occurrences of the lowest and highest ranges of power. To understand the mechanisms that generate these low- and high-power states, we fit a simple mathematical model of synchronization of oscillatory activity to human EEG data. The results consistently indicated that the majority (~95%) of synchronization dynamics is controlled by slowly adjusting the probability of synchronization while maintaining maximum entropy within the timescale of a few seconds. This strategy appears to be universal as the results generalized across oscillation frequencies, EEG current sources, and participants (N = 52) whether they rested with their eyes closed, rested with their eyes open in a darkened room, or viewed a silent nature video. Given that precisely coordinated behavior requires tightly controlled oscillatory dynamics, the current results suggest that the large-scale spatial synchronization of oscillatory activity is controlled by the relatively slow, entropy-maximizing adjustments of synchronization probability (demonstrated here) in combination with temporally precise phase adjustments (e.g., phase resetting generated by sensorimotor interactions). Interestingly, we observed a modest but consistent spatial pattern of deviations from the maximum-entropy rule, potentially suggesting that the mid-central-posterior region serves as an "entropy dump" to facilitate the temporally precise control of spectral-power dynamics in the surrounding regions.