A Tutorial on Data-Driven Methods for Statistically Assessing ERP Topographies

The neurophysiological mechanisms of emotional conflict are influenced by social associations information of varying valence

Previous research has indicated that emotional valence can influence the resolution of emotional conflicts, with this effect benefiting from the prioritized processing of negative emotions. In this study, a social learning paradigm was utilized to train participants to associate different neutral faces with distinct social meanings (e.g., stingy, generous). These learned neutral faces were then combined with emotion words of varying valence to create a novel face-word Stroop task. This task was employed to investigate whether social affective associations of different valences continue to impact emotional conflict processing. Concurrently, electroencephalogram data was recorded while participants completed the task. Behavioral results revealed that when participants were presented with neutral faces associated with negative social associations, emotional conflict resolution is facilitated, whereas when faced with neutral faces linked to positive social associations, the emotional conflict effect was significantly present. Consistency between event-related potentials and microstate results indicated that negative social associations facilitated conflict resolution, while positive social associations required participants to recruit more cognitive resources to inhibit irrelevant emotional interference. These findings further expand the factors influencing emotional conflict and relevant neurophysiological explanations.

Visual experience affects neural correlates of audio-haptic integration: A case study of non-sighted individuals

The ability to reduce sensory uncertainty by integrating information across different senses develops late in humans and depends on cross-modal, sensory experience during childhood and adolescence. While the dependence of audio-haptic integration on vision suggests cross-modal neural reorganization, evidence for such changes is lacking. Furthermore, little is known about the neural processes underlying audio-haptic integration even in sighted adults. Here, we examined electrophysiological correlates of audio-haptic integration in sighted adults (n = 29), non-sighted adults (n = 7), and sighted adolescents (n = 12) using a data-driven electrical neuroimaging approach. In sighted adults, optimal integration performance was predicted by topographical and super-additive strength modulations around 205-285 ms. Data from four individuals who went blind before the age of 8-9 years suggests that they achieved optimal integration via different, sub-additive mechanisms at earlier processing stages. Sighted adolescents showed no robust multisensory modulations. Late-blind adults, who did not show behavioral benefits of integration, demonstrated modulations at early latencies. Our findings suggest a critical period for the development of optimal audio-haptic integration dependent on visual experience around the late childhood and early adolescence.

Stimulation Parameters Recruit Distinct Cortico-Cortical Pathways: Insights from Microstate Analysis on TMS-Evoked Potentials

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) represent an innovative measure for examining brain connectivity and developing biomarkers of psychiatric conditions. Minimizing TEP variability across studies and participants, which may stem from methodological choices, is therefore vital. By combining classic peak analysis and microstate investigation, we tested how TMS pulse waveform and current direction may affect cortico-cortical circuit engagement when targeting the primary motor cortex (M1). We aim to disentangle whether changing these parameters affects the degree of activation of the same neural circuitry or may lead to changes in the pathways through which the induced activation spreads. Thirty-two healthy participants underwent a TMS-EEG experiment in which the pulse waveform (monophasic, biphasic) and current direction (posterior-anterior, anterior-posterior, latero-medial) were manipulated. We assessed the latency and amplitude of M1-TEP components and employed microstate analyses to test differences in topographies. Results revealed that TMS parameters strongly influenced M1-TEP components' amplitude but had a weaker role over their latencies. Microstate analysis showed that the current direction in monophasic stimulations changed the pattern of evoked microstates at the early TEP latencies, as well as their duration and global field power. This study shows that the current direction of monophasic pulses may modulate cortical sources contributing to TEP signals, activating neural populations and cortico-cortical paths more selectively. Biphasic stimulation reduces the variability associated with current direction and may be better suited when TMS targeting is blind to anatomical information.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis

In event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects' neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects' ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval.

Microstate Analyses to Study face Processing in Healthy Individuals and Psychiatric Disorders: A Review of ERP Findings

Microstates represent brief periods of quasi-stable electroencephalography (EEG) scalp topography, offering insights into dynamic fluctuations in event-related potential (ERP) topographies. Despite this, there is a lack of a comprehensive systematic overview of microstate findings concerning cognitive face processing. This review aims to summarize ERP findings on face processing using microstate analyses and assess their effectiveness in characterizing face-related neural representations. A literature search was conducted for microstate ERP studies involving healthy individuals and psychiatric populations, utilizing PubMed, Google Scholar, Web of Science, PsychInfo, and Scopus databases. Twenty-two studies were identified, primarily focusing on healthy individuals (n = 16), with a smaller subset examining psychiatric populations (n = 6). The evidence reviewed in this study suggests that various microstates are consistently associated with distinct ERP stages involved in face processing, encompassing the processing of basic visual facial features to more complex functions such as analytical processing, facial recognition, and semantic representations. Furthermore, these studies shed light on atypical attentional neural mechanisms in Autism Spectrum Disorder (ASD), facial recognition deficits among emotional dysregulation disorders, and encoding and semantic dysfunctions in Post-Traumatic Stress Disorder (PTSD). In conclusion, this review underscores the practical utility of ERP microstate analyses in investigating face processing. Methodologies have evolved towards greater automation and data-driven approaches over time. Future research should aim to forecast clinical outcomes and conduct validation studies to directly demonstrate the efficacy of such analyses in inverse space.

Electrophysiological Correlates of Reward Anticipation in Subjects with Schizophrenia: An ERP Microstate Study

The current study aimed to investigate alterations of event-related potentials (ERPs) microstate during reward anticipation in subjects with schizophrenia (SCZ), and their association with hedonic experience and negative symptoms. EEG data were recorded in thirty SCZ and twenty-three healthy controls (HC) during the monetary incentive delay task in which reward, loss and neutral cues were presented. Microstate analysis and standardized low-resolution electromagnetic tomography (sLORETA) were applied to EEG data. Furthermore, analyses correlating a topographic index (the ERPs score), calculated to quantify brain activation in relationship to the microstate maps, and scales assessing hedonic experience and negative symptoms were performed. Alterations in the first (125.0-187.5 ms) and second (261.7-414.1 ms) anticipatory cue-related microstate classes were observed. In SCZ, reward cues were associated to shorter duration and earlier offset of the first microstate class as compared to the neutral condition. In the second microstate class, the area under the curve was smaller for both reward and loss anticipation cues in SCZ as compared to HC. Furthermore, significant correlations between ERPs scores and the anticipation of pleasure scores were detected, while no significant association was found with negative symptoms. sLORETA analysis showed that hypo-activation of the cingulate cortex, insula, orbitofrontal and parietal cortex was detected in SCZ as compared to HC. Abnormalities in ERPs could be traced already during the early stages of reward processing and were associated with the anticipation of pleasure, suggesting that these dysfunctions might impair effective evaluation of incoming pleasant experiences. Negative symptoms and anhedonia are partially independent results.

Longstanding Auditory Sensory and Semantic Differences in Preterm Born Children

More than 10% of births are preterm, and the long-term consequences on sensory and semantic processing of non-linguistic information remain poorly understood. 17 very preterm-born children (born at < 33 weeks gestational age) and 15 full-term controls were tested at 10 years old with an auditory object recognition task, while 64-channel auditory evoked potentials (AEPs) were recorded. Sounds consisted of living (animal and human vocalizations) and manmade objects (e.g. household objects, instruments, and tools). Despite similar recognition behavior, AEPs strikingly differed between full-term and preterm children. Starting at 50ms post-stimulus onset, AEPs from preterm children differed topographically from their full-term counterparts. Over the 108-224ms post-stimulus period, full-term children showed stronger AEPs in response to living objects, whereas preterm born children showed the reverse pattern; i.e. stronger AEPs in response to manmade objects. Differential brain activity between semantic categories could reliably classify children according to their preterm status. Moreover, this opposing pattern of differential responses to semantic categories of sounds was also observed in source estimations within a network of occipital, temporal and frontal regions. This study highlights how early life experience in terms of preterm birth shapes sensory and object processing later on in life.

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods' efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

EEG microstate co-specificity in schizophrenia and obsessive-compulsive disorder

The past 20 years of research on EEG microstates has yielded the hypothesis that the imbalance pattern in the temporal dynamics of microstates C (increased) and D (decreased) is specific to schizophrenia. A similar microstate imbalance has been recently found in obsessive-compulsive disorder (OCD). The aim of the present high-density EEG study was to examine whether this pathological microstate pattern is co-specific to schizophrenia and OCD. We compared microstate temporal dynamics using Bayesian analyses, transition probabilities analyses and the Topographic Electrophysiological State Source-Imaging method for source reconstruction in 24 OCD patients and 28 schizophrenia patients, respectively, free of comorbid psychotic and OCD symptoms, and 27 healthy controls. OCD and schizophrenia patients exhibited the same increased contribution of microstate C, decreased duration and contribution of microstate D and greater D → C transition probabilities, compared with controls. A Bayes factor of 4.424 for the contribution of microstate C, 4.600 and 3.824, respectively, for the duration and contribution of microstate D demonstrated that there was no difference in microstate patterns between the two disorders. Source reconstruction further showed undistinguishable dysregulations between the Salience Network (SN), associated with microstate C, and the Executive Control Network (ECN), associated with microstate D, and between the ECN and cognitive cortico-striato-thalamo-cortical (CSTC) loop in the two disorders. The ECN/CSTC loop dysconnectivity was slightly worsened in schizophrenia. Our findings provide substantial evidence for a common aetiological pathway in schizophrenia and OCD, i.e. microstate co-specificity, and same anomalies in salience and external attention processing, leading to co-expression of symptoms.

Plasticity Changes in Central Auditory Systems of School-Age Children Following a Brief Training With a Remote Microphone System

OBJECTIVES

The objective of this study was to investigate whether a brief speech-in-noise training with a remote microphone (RM) system (favorable listening condition) would contribute to enhanced post-training plasticity changes in the auditory system of school-age children.

DESIGN

Before training, event-related potentials (ERPs) were recorded from 49 typically developing children, who actively identified two syllables in quiet and in noise (+5 dB signal-to-noise ratio [SNR]). During training, children completed the same syllable identification task as in the pre-training noise condition, but received feedback on their performance. Following random assignment, half of the sample used an RM system during training (experimental group), while the other half did not (control group). That is, during training' children in the experimental group listened to a more favorable speech signal (+15 dB SNR) than children from the control group (+5 dB SNR). ERPs were collected after training at +5 dB SNR to evaluate the effects of training with and without the RM system. Electrical neuroimaging analyses quantified the effects of training in each group on ERP global field power (GFP) and topography, indexing response strength and network changes, respectively. Behavioral speech-perception-in-noise skills of children were also evaluated and compared before and after training. We hypothesized that training with the RM system (experimental group) would lead to greater enhancement of GFP and greater topographical changes post-training than training without the RM system (control group). We also expected greater behavioral improvement on the speech-perception-in-noise task when training with than without the RM system.

RESULTS

GFP was enhanced after training only in the experimental group. These effects were observed on early time-windows corresponding to traditional P1-N1 (100 to 200 msec) and P2-N2 (200 to 400 msec) ERP components. No training effects were observed on response topography. Finally, both groups increased their speech-perception-in-noise skills post-training.

CONCLUSIONS

Enhanced GFP after training with the RM system indicates plasticity changes in the neural representation of sound resulting from listening to an enriched auditory signal. Further investigation of longer training or auditory experiences with favorable listening conditions is needed to determine if that results in long-term speech-perception-in-noise benefits.

Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation

Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention of sufficient trials become crucial. Here, we present one such algorithm that makes use of large spatiotemporal correlations in neural signals and solves the low-rank matrix completion problem, to fix artifactual entries. The method uses a gradient descent algorithm in lower dimensions to learn the missing entries and provide faithful reconstruction of signals. We carried out numerical simulations to benchmark the method and estimate optimal hyperparameters for actual EEG data. The fidelity of reconstruction was assessed by detecting event-related potentials (ERP) from a highly artifacted EEG time series from human infants. The proposed method significantly improved the standardized error of the mean in ERP group analysis and a between-trial variability analysis compared to a state-of-the-art interpolation technique. This improvement increased the statistical power and revealed significant effects that would have been deemed insignificant without reconstruction. The method can be applied to any time-continuous neural signal where artifacts are sparse and spread out across epochs and channels, increasing data retention and statistical power.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

The temporal dynamics of the Stroop effect from childhood to young and older adulthood

The processes involved in the Stroop task/effect are thought to involve conflict detection and resolution stages. Little is known about the evolution of these two components over the lifespan. It is well admitted that children and older adults tend to show longer response latencies than young adults. The present study aims at clarifying the rational of such changes from childhood to adulthood and in aging by comparing the impacted cognitive processes across age groups. More precisely, the aim was to clarify if all processes take more time to be executed, hence implying that longer latencies rely mainly on processing speed or if an additional process lengthens the resolution of the conflict in children and/or older adults. To this aim we recorded brain electrical activity using EEG in school-age children, young and older adults while they performed a classic verbal Stroop task. The signal was decomposed in microstate brain networks, and age groups and conditions were compared. Behavioral results evolved following an inverted U-shaped curve. In children, different brain states from the ones observed in adults were highlighted, both in the conflict detection and resolution time-windows. Longer latencies in the incongruent condition were mainly attributed to an overly increased duration of the microstates involved in the conflict resolution time window. In aging, the same microstate maps were reported for both young and older adult groups. The differences in performances between groups could be explained by a disproportionally long conflict detection phase, even compressing the latest stage of response articulation. These results tend to favor a specific immaturity of the brain networks involved coupled with a slowing of the processes in children, while cognitive decline could be mostly explained by a general slowing.

Electroencephalography Based Microstate Functional Connectivity Analysis in Emotional Cognitive Reappraisal Combined with Happy Music

Currently, research mainly focuses on the effects of happy music on the subjective assessment of cognitive reappraisal, but relevant results of the neural mechanism are lacking. By analysing the functional connectivity of microstates based on electroencephalography (EEG), we investigated the effect of cognitive reappraisal combined with happy music on emotional regulation and the dynamic characteristics of brain functional activities. A total of 52 healthy college students were divided into music group and control group. EEG data and behavioural scores were collected during an experiment of cognitive reappraisal combined with happy music. The dynamic time window of the brain functional network was determined by microstate analysis, and the metrics of functional connectivity, clustering coefficient (Cp) and characteristic path length (Lp), were calculated based on the phase-locked value. The arousal of cognitive reappraisal significantly increased (p = 0.005) in music group, but the valence did not change significantly. This suggested that happy music did not affect emotional regulation from the behavioural perspective. Four microstate global templates (A–D) were determined. With happy music, the duration (p = 0.043) and Lp (p = 0.033) of microstate B increased significantly, indicating that the transfection efficiency of the brain network decreased, reflecting a negative effect on cognitive reappraisal. The duration (p = 0.017) of microstate D decreased and of Cp (p < 0.001) increased significantly, indicating that the local information-processing ability of the brain network increased. We conclude that happy music can change the characteristics of brain functional networks and have a positive effect on cognitive reappraisal in specific period. The research provides a certain electrophysiological basis for applying happy music to cognitive reappraisal.

Microstates imbalance is associated with a functional dysregulation of the resting-state networks in obsessive-compulsive disorder: a high-density electrical neuroimaging study using the TESS method

The dysfunctional patterns of microstates dynamics in obsessive-compulsive disorder (OCD) remain uncertain. Using high-density electrical neuroimaging (EEG) at rest, we explored microstates deterioration in OCD and whether abnormal microstates patterns are associated with a dysregulation of the resting-state networks interplay. We used EEG microstates analyses, TESS method for sources reconstruction, and General Linear Models to test for the effect of disease severity on neural responses. OCD patients exhibited an increased contribution and decreased duration of microstates C and D, respectively. Activity was decreased in the Salience Network (SN), associated with microstate C, but increased in the Default Mode Network (DMN) and Executive Control Network (ECN), respectively, associated with microstates E and D. The hyperactivity of the right angular gyrus in the ECN correlated with the symptoms severity. The imbalance between microstates C and D invalidates the hypothesis that this electrophysiological pattern is specific to psychosis. Demonstrating that the SN-ECN dysregulation manifests as abnormalities in microstates C and D, we confirm that the SN deterioration in OCD is accompanied by a failure of the DMN to deactivate and aberrant compensatory activation mechanisms in the ECN. These abnormalities explain typical OCD clinical features but also detachment from reality, shared with psychosis.

The path of dishonesty: identification of mental processes with electrical neuroimaging

Much research finds that lying takes longer than truth-telling. Yet, the source of this response time difference remains elusive. Here, we assessed the spatiotemporal evolution of electrical brain activity during honesty and dishonesty in 150 participants using a sophisticated electrical neuroimaging approach-the microstate approach. This uniquely positioned us to identify and contrast the entire chain of mental processes involved during honesty and dishonesty. Specifically, we find that the response time difference is the result of an additional late-occurring mental process, unique to dishonest decisions, interrupting the antecedent mental processing. We suggest that this process inhibits the activation of the truth, thus permitting the execution of the lie. These results advance our understanding of dishonesty and clarify existing theories about the role of increased cognitive load. More broadly, we demonstrate the vast potential of our approach to illuminate the temporal organization of mental processes involved in decision-making.

Modulation of EEG theta by naturalistic social content is not altered in infants with family history of autism

Theta oscillations (spectral power and connectivity) are sensitive to the social content of an experience in typically developing infants, providing a possible marker of early social brain development. Autism is a neurodevelopmental condition affecting early social behaviour, but links to underlying social brain function remain unclear. We explored whether modulations of theta spectral power and connectivity by naturalistic social content in infancy are related to family history for autism. Fourteen-month-old infants with (family history; FH; N = 75) and without (no family history; NFH; N = 26) a first-degree relative with autism watched social and non-social videos during EEG recording. We calculated theta (4-5 Hz) spectral power and connectivity modulations (social-non-social) and associated them with outcomes at 36 months. We replicated previous findings of increased theta power and connectivity during social compared to non-social videos. Theta modulations with social content were similar between groups, for both power and connectivity. Together, these findings suggest that neural responses to naturalistic social stimuli may not be strongly altered in 14-month-old infants with family history of autism.

Closed-loop modulation of local slow oscillations in human NREM sleep

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations.

Automatized online prediction of slow-wave peaks during non-rapid eye movement sleep in young and old individuals: Why we should not always rely on amplitude thresholds

Brain-state-dependent stimulation during slow-wave sleep is a promising tool for the treatment of psychiatric and neurodegenerative diseases. A widely used slow-wave prediction algorithm required for brain-state-dependent stimulation is based on a specific amplitude threshold in the electroencephalogram. However, due to decreased slow-wave amplitudes in aging and psychiatric conditions, this approach might miss many slow-waves because they do not fulfill the amplitude criterion. Here, we compared slow-wave peaks predicted via an amplitude-based versus a multidimensional approach using a topographical template of slow-wave peaks in 21 young and 21 older healthy adults. We validate predictions against the gold-standard of offline detected peaks. Multidimensionally predicted peaks resemble the gold-standard regarding spatiotemporal dynamics but exhibit lower peak amplitudes. Amplitude-based prediction, by contrast, is less sensitive, less precise and - especially in the older group - predicts peaks that differ from the gold-standard regarding spatiotemporal dynamics. Our results suggest that amplitude-based slow-wave peak prediction might not always be the ideal choice. This is particularly the case in populations with reduced slow-wave amplitudes, like older adults or psychiatric patients. We recommend the use of multidimensional prediction, especially in studies targeted at populations other than young and healthy individuals.

Event-related microstate dynamics represents working memory performance

In recent years, EEG microstate analysis has attracted much attention as a tool for characterizing the spatial and temporal dynamics of large-scale electrophysiological activities in the human brain. Canonical 4 states (classes A, B, C, and D) have been widely reported, and they have been pointed out for their relationships with cognitive functions and several psychiatric disorders such as schizophrenia, in particular, through their static parameters such as average duration, occurrence, coverage, and transition probability. However, the relationships between event-related microstate changes and their related cognitive functions, as is often analyzed in event-related potentials under time-locked frameworks, is still not well understood. Furthermore, not enough attention has been paid to the relationship between microstate dynamics and static characteristics. To clarify the relationships between the static microstate parameters and dynamic microstate changes, and between the dynamics and working memory (WM) function, we first examined the temporal profiles of the microstates during the N-back task. We found significant event-related microstate dynamics that differed predominantly with WM loads, which were not clearly observed in the static parameters. Furthermore, in the 2-back condition, patterns of state transitions from class A to C in the high- and low-performance groups showed prominent differences at 50-300 ms after stimulus onset. We also confirmed that the transition patterns of the specific time periods were able to predict the performance level (low or high) in the 2-back condition at a significant level, where a specific transition between microstates, namely from class A to C with specific polarity, contributed to the prediction robustly. Taken together, our findings indicate that event-related microstate dynamics at 50-300 ms after onset may be essential for WM function. This suggests that event-related microstate dynamics can reflect more highly-refined brain functions.

Does mental rotation emulate motor processes? An electrophysiological study of objects and body parts

Several arguments suggest that motor planning may share embodied neural mechanisms with mental rotation (MR). However, it is not well established whether this overlap occurs regardless of the type of stimulus that is manipulated, in particular manipulable or non-manipulable objects and body parts. We here used high-density electroencephalography (EEG) to examine the cognitive similarity between MR of objects that do not afford specific hand actions (chairs) and bodily stimuli (hands). Participants had identical response options for both types of stimuli, and they gave responses orally in order to prevent possible interference with motor imagery. MR of hands and chairs generated very similar behavioral responses, time-courses and neural sources of evoked-response potentials (ERPs). ERP segmentation analysis revealed distinct time windows during which differential effects of stimulus type and angular disparity were observed. An early period (90-160 ms) differentiated only between stimulus types, and was associated with occipito-temporal activity. A later period (290-330 ms) revealed strong effects of angular disparity, associated with electrical sources in the right angular gyrus and primary motor/somatosensory cortex. These data suggest that spatial transformation processes and motor planning are recruited simultaneously, supporting the involvement of motor emulation processes in MR.

Qualitative differences in the spatiotemporal brain states supporting configural face processing emerge in adolescence in autism

BACKGROUND

Studying the neural processing of faces can illuminate the mechanisms of compromised social expertise in autism. To resolve a longstanding debate, we examined whether differences in configural face processing in autism are underpinned by quantitative differences in the activation of typical face processing pathways, or the recruitment of non-typical neural systems.

METHODS

We investigated spatial and temporal characteristics of event-related EEG responses to upright and inverted faces in a large sample of children, adolescents, and adults with and without autism. We examined topographic analyses of variance and global field power to identify group differences in the spatial and temporal response to face inversion. We then examined how quasi-stable spatiotemporal profiles - microstates - are modulated by face orientation and diagnostic group.

RESULTS

Upright and inverted faces produced distinct profiles of topography and strength in the topographical analyses. These topographical profiles differed between diagnostic groups in adolescents, but not in children or adults. In the microstate analysis, the autistic group showed differences in the activation strength of normative microstates during early-stage processing at all ages, suggesting consistent quantitative differences in the operation of typical processing pathways; qualitative differences in microstate topographies during late-stage processing became prominent in adults, suggesting the increasing involvement of non-typical neural systems with processing time and over development.

CONCLUSIONS

These findings suggest that early difficulties with configural face processing may trigger later compensatory processes in autism that emerge in later development.

Word Production Changes through Adolescence: A Behavioral and ERP Investigation of Referential and Inferential Naming

Changes in word production occur across the lifespan, with adolescence representing a knot point between children's and adults' performance and underlying brain processes. Previous studies on referential word production using picture naming tasks have shown a completely adult-like pattern in 17-year-old adolescents and an intermediate pattern between children and adults in adolescents aged 14-16 years old, suggesting a possible involvement of visuo-conceptual processes in the transition from childhood to adulthood. Given the visual nature of the picture naming task, it is unclear whether changes in visuo-conceptual processes are specifically related to the referential word production or if overall changes in conceptual to lexical processes drive maturation. To answer this question, we turned to an inferential word production task, i.e., naming from auditory definitions, involving different conceptual to lexical processes relative to referential naming. Behavior and electroencephalographic Event-Related Potentials (ERP) in a (visual) referential word production task and an (auditory) inferential word production task were recorded and compared in three groups of adolescents (respectively, aged 10 to 13, 14 to 16, and 17 to 18). Only the youngest group displayed longer production latencies and lower accuracy than the two older groups of adolescents who performed similarly on both tasks. Crucially, ERP waveform analysis and topographic pattern analysis revealed significant intergroup differences on both tasks. Changes across ages are not merely linked to the visual-conceptual processes of a picture naming task but are rather related to lexical-semantic processes involved in word production.

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and χ² analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.

Exploring brain activity for positive and negative emotions by means of EEG microstates

Microstate analysis applied to electroencephalographic signals (EEG) allows both temporal and spatial imaging exploration and represents the activity across the scalp. Despite its potential usefulness in understanding brain activity during a specific task, it has been mostly exploited at rest. We extracted EEG microstates during the presentation of emotional expressions, presented either unilaterally (a face in one visual hemifield) or bilaterally (two faces, one in each hemifield). Results revealed four specific microstate’s topographies: (i) M1 involves the temporal areas, mainly in the right hemisphere, with a higher occurrence for stimuli presented in the left than in the right visual field; (ii) M2 is localized in the left temporal cortex, with higher occurrence and coverage for unilateral than bilateral presentations; (iii) M3, with a bilateral temporo-parietal localization, shows higher coverage for bilateral than unilateral presentation; (iv) M4, mainly localized in the right fronto-parietal areas and possibly representing the hemispheric specialization for the peculiar stimulus category, shows higher occurrence and coverage for unilateral stimuli presented in the left than in the right visual field. These results suggest that microstate analysis is a valid tool to explore the cerebral response to emotions and can add new insights on the cerebral functioning, with respect to other EEG markers.

Sound gaps boost emotional audiovisual integration independent of attention: Evidence from an ERP study

The emotion discrimination paradigm was adopted to study the effect of interrupted sound on visual emotional processing under different attentional states. There were two experiments: Experiment 1: judging facial expressions (explicit task), Experiment 2: judging the position of a bar (implicit task). In Experiment 1, ERP results showed that there was a sound gap accelerating the effect of P1 present only under neutral faces. In Experiment 2, the accelerating effect (P1) existed regardless of the emotional condition. Combining two experiments, P1 findings suggest that sound gap enhances bottom-up attention. The N170 and late positive component (LPC) were found to be regulated by emotion face in both experiments, with fear over the neutral. Comparing the two experiments, the explicit task induced a larger LPC than the implicit task. Overall, sound gaps boosted the audiovisual integration by bottom-up attention in early integration, while cognitive expectations led to top-down attention in late stages.

Altered Visuospatial Processing in Schizophrenia: An Event-related Potential Microstate Analysis Comparing Patients with and without Hallucinations with Healthy Controls

Patients with schizophrenia present with various symptoms related to different domains. Abnormalities of auditory and visual perception are parts of a more general problem. Nevertheless, the relationship between the lifetime history of auditory verbal hallucination (AVH), one of the most prevalent symptoms in schizophrenia, and visuospatial deficits remains unclear. This study aimed to investigate differences in hemispheric involvement and visuospatial processing between healthy controls (HCs) and schizophrenia patients with and without AVHs. HCs (N = 20), schizophrenia patients with AVH (AVH group, N = 16), and schizophrenia patients without hallucinations (NH group, N = 10) participated in a 4-choice reaction task with lateralized stimuli. An event-related potential (ERP)-microstate approach was used to analyze ERP differences between the conditions and groups. The schizophrenia patients without hallucinations had slower responses than the HCs. An early visual N1 contralateral to stimulation side was prominent in all groups of participants but with decreased amplitude in the patients with schizophrenia, especially in the AVH group over the right hemisphere. The amplitude of P3b, a cognitive evaluation component, was also decreased in schizophrenia. Compared to AVH and HC groups, the patients in the NH group had altered microstate patterns: P3b was replaced by a novelty component, P3a. Although the difference between both patient groups was only based on the presence of AVHs, our findings indicated that patients had specific visuospatial deficits associated with a lifetime history of hallucinations: patients with AVHs showed early visual component alterations in the right hemisphere, and those without AVHs had more prominent visuospatial impairment.

Towards understanding how we pay attention in naturalistic visual search settings

Research on attentional control has largely focused on single senses and the importance of behavioural goals in controlling attention. However, everyday situations are multisensory and contain regularities, both likely influencing attention. We investigated how visual attentional capture is simultaneously impacted by top-down goals, the multisensory nature of stimuli, and the contextual factors of stimuli's semantic relationship and temporal predictability. Participants performed a multisensory version of the Folk et al. (1992) spatial cueing paradigm, searching for a target of a predefined colour (e.g. a red bar) within an array preceded by a distractor. We manipulated: 1) stimuli's goal-relevance via distractor's colour (matching vs. mismatching the target), 2) stimuli's multisensory nature (colour distractors appearing alone vs. with tones), 3) the relationship between the distractor sound and colour (arbitrary vs. semantically congruent) and 4) the temporal predictability of distractor onset. Reaction-time spatial cueing served as a behavioural measure of attentional selection. We also recorded 129-channel event-related potentials (ERPs), analysing the distractor-elicited N2pc component both canonically and using a multivariate electrical neuroimaging framework. Behaviourally, arbitrary target-matching distractors captured attention more strongly than semantically congruent ones, with no evidence for context modulating multisensory enhancements of capture. Notably, electrical neuroimaging of surface-level EEG analyses revealed context-based influences on attention to both visual and multisensory distractors, in how strongly they activated the brain and type of activated brain networks. For both processes, the context-driven brain response modulations occurred long before the N2pc time-window, with topographic (network-based) modulations at ∼30 ms, followed by strength-based modulations at ∼100 ms post-distractor onset. Our results reveal that both stimulus meaning and predictability modulate attentional selection, and they interact while doing so. Meaning, in addition to temporal predictability, is thus a second source of contextual information facilitating goal-directed behaviour. More broadly, in everyday situations, attention is controlled by an interplay between one's goals, stimuli's perceptual salience, meaning and predictability. Our study calls for a revision of attentional control theories to account for the role of contextual and multisensory control.

Acute alcohol intoxication modulates the temporal dynamics of resting electroencephalography networks

This study aimed to provide a currently missing link between general intoxication-induced changes in overall brain activity and the multiple cognitive control deficits typically observed during acute alcohol intoxication. For that purpose, we analyzed the effects of acute alcohol intoxication (1.1‰) on the four archetypal electroencephalography (EEG) resting networks (i.e., microstates A-D) and their temporal dynamics (e.g., coverage and transitions from one microstate to another), as well as on self-reported resting-state cognition in n = 22 healthy young males using a counterbalanced within-subject design. Our microstate analyses indicated that alcohol increased the coverage of the visual processing-related microstate B at the expense of the autonomic processing-related microstate C. Add-on exploratory analyses revealed that alcohol increased transitions from microstate C to microstate B and decreased bidirectional transitions between microstate C and the attention-related microstate D. In line with the observed alcohol-induced decrease of the autonomic processing-related microstate C, participants reported decreases of their somatic awareness during intoxication, which were positively associated with more transitions from microstate C to microstate B. In sum, the observed effects provide mechanistic insights into how alcohol might hamper cognitive processing by generally prioritizing the bottom-up processing of visual stimuli over top-down internal information processing. The fact that this was found during the resting state further proves that alcohol-induced changes in brain activity are continuously present and do not only emerge during demanding situations or tasks.

Does learning to read affect naming skills? Insights from ERPs during letter and picture naming tasks

Numerous studies report that poor readers display low performance in naming tasks. However, very few studies have investigated the development of naming skills along with the development of reading fluency and its variability in typically developing children. In this study, we used electro-encephalographic (EEG) recordings acquired during letter and picture naming tasks to investigate how naming skills develop and, possibly, interact with age and reading level variations. Ninety-three children aged 7-12 years named letters and pictures under an EEG recording, and their reading performance was assessed. ERP results on amplitudes show that age and reading level have similar effects on the entire letter naming time-course. By contrast, age and reading level have different effects on the picture naming time-course, with a specific effect of reading level on the N1 time-interval, associated with visuo-conceptual processing and an effect of both age and reading on later time-windows. On the microstate analysis, age remains the only predictor of the variance in global electric field at scalp for both letter and picture naming indicating that reading skill is not related to a modulation of the mental processes underlying naming.

Behavioral and Brain Responses Highlight the Role of Usage in the Preparation of Multiword Utterances for Production

Usage-based theories assume that all aspects of language processing are shaped by the distributional properties of the language. The frequency not only of words but also of larger chunks plays a major role in language processing. These theories predict that the frequency of phrases influences the time needed to prepare these phrases for production and their acoustic duration. By contrast, dominant psycholinguistic models of utterance production predict no such effects. In these models, the system keeps track of the frequency of individual words but not of co-occurrences. This study investigates the extent to which the frequency of phrases impacts naming latencies and acoustic duration with a balanced design, where the same words are recombined to build high- and low-frequency phrases. The brain signal of participants is recorded so as to obtain information on the electrophysiological bases and functional locus of frequency effects. Forty-seven participants named pictures using high- and low-frequency adjective-noun phrases. Naming latencies were shorter for high-frequency than low-frequency phrases. There was no evidence that phrase frequency impacted acoustic duration. The electrophysiological signal differed between high- and low-frequency phrases in time windows that do not overlap with conceptualization or articulation processes. These findings suggest that phrase frequency influences the preparation of phrases for production, irrespective of the lexical properties of the constituents, and that this effect originates at least partly when speakers access and encode linguistic representations. Moreover, this study provides information on how the brain signal recorded during the preparation of utterances changes with the frequency of word combinations.

Resting-State EEG Microstates Parallel Age-Related Differences in Allocentric Spatial Working Memory Performance

Alterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

Attentional bias during emotional processing: Behavioral and electrophysiological evidence from an Emotional Flanker Task

Threatening stimuli seem to capture attention more swiftly than neutral stimuli. This attention bias has been observed under different experimental conditions and with different types of stimuli. It remains unclear whether this adaptive behaviour reflects the function of automatic or controlled attention mechanisms. Additionally, the spatiotemporal dynamics of its neural correlates are largely unknown. The present study investigates these issues using an Emotional Flanker Task synchronized with EEG recordings. A group of 32 healthy participants saw response-relevant images (emotional scenes from IAPS or line drawings of objects) flanked by response-irrelevant distracters (i.e., emotional scenes flanked by line drawings or vice versa). We assessed behavioural and ERP responses drawn from four task conditions (Threat-Central, Neutral-Central, Threat-Peripheral, and Neutral-Peripheral) and subjected these responses to repeated-measures ANOVA models. When presented as response-relevant targets, threatening images attracted faster and more accurate responses. They did not affect response accuracy to targets when presented as response-irrelevant flankers. However, response times were significantly slower when threatening images flanked objects than when neutral images were shown as flankers. This result replicated the well-known Emotional Flanker Effect. Behavioural responses to response-relevant threatening targets were accompanied by significant modulations of ERP activity across all time-windows and regions of interest and displayed some meaningful correlations. The Emotional Flanker Effect was accompanied by a modulation over parietal and central-parietal regions within a time-window between 550-690ms. Such a modulation suggests that the attentional disruption to targets caused by response-irrelevant threatening flankers appears to reflect less neural resources available, which are seemingly drawn away by distracting threatening flankers. The observed spatiotemporal dynamics seem to concur with understanding of the important adaptive role attributed to threat-related attention bias.

Attentive brain states in infants with and without later autism

Early difficulties in engaging attentive brain states in social settings could affect learning and have cascading effects on social development. We investigated this possibility using multichannel electroencephalography during a face/non-face paradigm in 8-month-old infants with (FH, n = 91) and without (noFH, n = 40) a family history of autism spectrum disorder (ASD). An event-related potential component reflecting attention engagement, the Nc, was compared between FH infants who received a diagnosis of ASD at 3 years of age (FH-ASD; n = 19), FH infants who did not (FH-noASD; n = 72) and noFH infants (who also did not, hereafter noFH-noASD; n = 40). 'Prototypical' microstates during social attention were extracted from the noFH-noASD group and examined in relation to later categorical and dimensional outcome. Machine-learning was used to identify the microstate features that best predicted ASD and social adaptive skills at three years. Results suggested that whilst measures of brain state timing were related to categorical ASD outcome, brain state strength was related to dimensional measures of social functioning. Specifically, the FH-ASD group showed shorter Nc latency relative to other groups, and duration of the attentive microstate responses to faces was informative for categorical outcome prediction. Reduced Nc amplitude difference between faces with direct gaze and a non-social control stimulus and strength of the attentive microstate to faces contributed to the prediction of dimensional variation in social skills. Taken together, this provides consistent evidence that atypical attention engagement precedes the emergence of difficulties in socialization and indicates that using the spatio-temporal characteristics of whole-brain activation to define brain states in infancy provides an important new approach to understanding of the neurodevelopmental mechanisms that lead to ASD.

rTMS affects EEG microstates dynamic during evoked activity

Electrophysiological (EEG) correlates both at time (i.e., event-related potentials, ERP) and frequency (i.e., event-related desynchronization, ERD) domains have been shown to be modulated by external magnetic interference. Parallel studies reported a similar interference also for the EEG microstate at rest and in the period that anticipates a task. Here we investigated whether such interference was prolonged during the evoked activity in the framework of the semantic decision task. To this aim, rTMS was delivered over a core region of both the Default mode network and the language network (i.e., left angular gyrus, AG), previously associated to the current task, and as active control we stimulated the left IPS. When subjects received a non-active stimulation (i.e., Sham), in the period that follows the target onset (i.e., 2 sec after the rTMS) we found an interesting alternation of two dominant microstates (MS1, MS3), previously associated to the phonological network and the Cingulo-Opercular Network (CON), respectively. This dynamic was not altered when TMS was delivered over the left IPS. On the contrary, rTMS over left AG selectively suppressed the phonological-related microstate. These findings provide the first causal evidence of region specificity of the EEG microstates topography during the evoked activity corroborating the idea of a crucial role of AG in the semantic memory. Moreover, the present results might provide insight for understanding the neurophysiological correlates of language disorders e.g., aphasia as well as for planning non-invasive brain stimulation protocols for the rehabilitation.

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics.

Scalp Topography of Lower Urinary Tract Sensory Evoked Potentials

Impaired lower urinary tract (LUT) afferents often cause LUT symptoms. Assessment of LUT afferent pathways is possible using bipolar cortical sensory evoked potential (SEP) recordings with the active electrode at the vertex during electrical stimulation in the LUT. This study aimed to investigate the topographical distribution and microstates of lower urinary tract sensory evoked potentials (LUTSEPs) using different stimulation frequencies. Ninety healthy subjects (18-36 years old, 40 women) were randomly assigned to one of five stimulation locations [bladder dome; trigone; proximal, membranous (men only) or distal urethra]. Cycles of 0.5 Hz/1.1 Hz/1.6 Hz electrical stimulation were applied using a custom-made catheter. Cortical activity was recorded from 64 surface electrodes. Marker setting was performed manually on an individual subject-level for the P1, N1, and P2 components of vertex recordings. N1 and P2 topographies presented with central negativities and positivities around the vertex. Regarding topographical distribution, Randomization Graphical User interface (RAGU) analyses revealed consistent frequency effects and microstates for N1/P2. Higher stimulation frequencies resulted in decreasing map strength for P1, N1, and P2. LUTSEP topographies suggest central generators in the somatosensory cortex, which are not detectable in a bipolar set-up. The observed frequency effect indicates fiber refractoriness at higher frequencies. The multichannel approach allows more comprehensive assessment of LUTSEPs and might therefore be sensitive to pathological changes. Examinations in patients with LUT symptoms are needed to further investigate this biomarker.

Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering

Clustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects' data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed by a newly proposed time-window detection method to determine the most suitable time window for identifying the ERP of interest in each condition/group. Applying the proposed method to the simulated ERP data and real data indicated that the brain responses from the individual subjects can be collected to determine a reliable time window for different conditions/groups. Our results revealed more precise time windows to identify N2 and P3 components in the simulated data compared to the state-of-the-art methods. Additionally, our proposed method achieved more robust performance and outperformed statistical analysis results in the real data for N300 and prospective positivity components. To conclude, the proposed method successfully estimates the time window for ERP of interest by processing the individual data, offering new venues for spatiotemporal ERP processing.

The Neurophysiology of Implicit Alcohol Associations in Recently Abstinent Patients With Alcohol Use Disorder: An Event-Related Potential Study Considering Gender Effects

BACKGROUND

Neuroscientific models of alcohol use disorders (AUDs) postulate an imbalance between automatic, implicit, and controlled (conscious) processes. Implicit associations towards alcohol indicate the automatically attributed appeal of alcohol-related stimuli. First, behavioral studies indicate that negative alcohol associations are less pronounced in patients compared to controls, but potential neurophysiological differences remain unexplored. This study investigates neurophysiological correlates of implicit alcohol associations in recently abstinent patients with AUD for the first time, including possible gender effects.

METHODS

A total of 62 patients (40 males and 22 females) and 21 controls performed an alcohol valence Implicit Association Test, combining alcohol-related pictures with positive (incongruent condition) or negative (congruent condition) words, while brain activity was recorded using 64-channel electroencephalography. Event-related potentials (ERPs) for alcohol-negative and alcohol-positive trials were computed. Microstate analyses investigated the effects of group (patients, controls) and condition (incongruent, congruent); furthermore, possible gender effects in patients were analyzed. Significant effects were localized with standardized low-resolution brain electromagnetic topography analysis.

RESULTS

Although no behavioral group differences were found, ERPs of patients and controls were characterized by distinct microstates from 320 ms onwards. ERPs between conditions differed only in patients with higher signal strength during incongruent trials. Around 600 ms, controls displayed higher signal strength than patients. A gender effect mirrored this pattern with enhanced signal strength in females as opposed to male patients. Around 690 ms, a group-by-valence interaction indicated enhanced signal strength in congruent compared to incongruent trials, which was more pronounced in controls.

CONCLUSIONS

For patients with AUD, the pattern, timing, and source localization of effects suggest greater effort regarding semantic and self-relevant integration around 400 ms during incongruent trials and attenuated emotional processing during the late positive potential timeframe. Interestingly, this emotional attenuation seemed reduced in female patients, thus corroborating the importance of gender-sensitive research and potential treatment of AUD.

Interindividual differences in brain dynamics of early visual processes: Impact on score accuracy in the mental rotation task

Interindividual variations in the ability to perform visuospatial mental transformations have been investigated extensively, in particular through mental rotation tasks. However, the impact of early visual processes on performance has been largely ignored. To clarify this issue, we explored the time-course of early visual processing (from 0 to 450 ms poststimulus) using event-related potentials topographic analyses. The main findings demonstrated a significant link between early attentional processes and accuracy scores occurring more than five seconds later, as well as a strong association between spatial covariance and microstate topographies exhibiting substantial gender differences. More specifically, the results indicated that, in a classical mental rotation task, the male brain expends more time processing visual-spatial information resulting in a longer bilateral positive potential at posterior-occipital sites. In comparison, the female brain initiates earlier processing of non-spatial information resulting in a faster transition from a bilateral positive potential of posterior-occipital sites to a negative potential at central-frontal sites. These findings illustrate how a more complete utilization of the spatiotemporal information contained in EEG recordings can provide important insights about the impact of early visual processes on interindividual differences, particularly across gender, and thus shed new light on alternate cognitive strategies.

Deficient processing of alcohol cues in the addicted brain: Evidence from event-related potential microstates

OBJECTIVE

Patients with alcohol use disorder (AUD) show altered brain responses to alcohol cues as compared to healthy controls. Event-related potential (ERP) studies mostly focus on the P3, which is usually diminished in AUD patients. The few studies that have investigated earlier components have yielded inconsistent results. The present study aimed at identifying the onset of impaired alcohol cue processing in AUD patients, as well as the association between neurophysiological processing and subjective craving.

METHODS

A sample of 15 inpatients with AUD and 15 healthy controls completed a cue reactivity task with alcohol-related, neutral, and scrambled pictures. Multichannel-EEG was recorded from 70 scalp electrodes, and ERP microstates were analyzed.

RESULTS

Patients displayed impaired neurophysiological processing, as indexed by a weaker P3 and a weaker, insensitive P1. The blunted P1 was associated with higher subjective craving.

CONCLUSIONS

Impaired alcohol cue processing in AUD emerges early, at the stage of sensory processing. Such deficient initial processing seems crucial to understanding cue reactivity processes in the brain and in the subjective experience of craving.

SIGNIFICANCE

The results highlight the importance of investigating early ERP components preceding the P3, and contribute to the debate on the onset of information-processing dysfunction in the addicted brain.

Euthymic bipolar disorder patients and EEG microstates: a neural signature of their abnormal self experience?

BACKGROUND

A growing number of neuroimaging studies have revealed spatial abnormalities of resting-state functional brain network activity in bipolar disorder (BD). Conversely, abnormalities of resting state temporal dynamics have been scarcely investigated so far. The aim of this study was to characterize the EEG microstates activity in BD patients with a history of manic predominant polarity. Patients were euthymic and pharmacologically stabilized.

METHODS

Nineteen BD patients (mean age 34.4 ± 11.0, 7 female) and 19 healthy controls (HC; mean age 38.2 ± 9.9, 7 female) were recruited. The psychometric evaluation included the Hamilton Depression Scale (HAMD), the Young Mania Rating Scale (YMRS), the Dissociative Experience Scale (DES), and the State-Trait Anxiety Inventory (STAI).  Two runs of 2 minutes of EEG activity by a 128-channel system were acquired at rest and analyzed through microstate analysis.

RESULTS

We found a reduced presence of microstate B in BD patients compared to HC, since BD patients have a tendency to transit from the microstate B to the microstates C and D significantly more than HC. Furthermore, microstate B features were correlated with DES, state STAI and trait STAI scores.

CONCLUSION

The reduced presence of microstate B might be associated with episodic autobiographic memory deficit, exaggerated self-focusing and states of dissociations characteristic of BD. Strong correlations of microstate B metrics and dynamics with symptoms of dissociation and anxiety across the two groups supported this interpretation.

Neural dynamics of speech and non-speech motor planning

As the speech apparatus is also involved in producing non-speech movements, understanding whether speech and non-speech planning are controlled by the same brain mechanisms is central to the comprehension of motor speech planning. A crucial issue is whether a specialized motor planning/control system is dedicated to speech or if the motor planning/control system is shared across oromotor behaviors. We investigated the EEG/ERP spatio-temporal dynamics of the motor planning processes preceding articulation by comparing the production of non-speech gestures matched to monosyllabic words and non-words. To isolate motor from pre-motor planning, we used a delayed production task combined with an articulatory suppression task. Results suggest that the planning processes preceding articulation for non-speech, words and non-words recruit the same neural networks but with different involvement for non-speech and speech. The results support the idea of shared motor planning/control systems for speech and non-speech but with different dynamics for each oromotor behavior.