On the benefits of using surface Laplacian (current source density) methodology in electrophysiology

The P300 wave is decomposed into components reflecting response selection and automatic reactivation of stimulus-response links

The parietal P300 wave of event-related potentials (ERPs) has been associated with various psychological operations in numerous laboratory tasks. This study aims to decompose the P3 wave of ERPs into subcomponents and link them with behavioral parameters, such as the strength of stimulus-response (S-R) links and GO/NOGO responses. EEGs (31 channels), referenced to linked ears, were recorded from 172 healthy adults (107 women) who participated in two cued GO/NOGO tasks, where the strength of S-R links was manipulated through instructions. P300 waves were observed in active conditions in response to cues, GO/NOGO stimuli, and in passive conditions when no manual response was required. Utilizing a combination of current source density transformation and blind source separation methods, we decomposed the P300 wave into two distinct components, purportedly originating from different parts of the parietal lobules. The amplitude of the parietal midline component (with current sources around Pz) closely mirrored the strength of the S-R link across proactive, reactive, and passive conditions. The amplitude of the lateral parietal component (with current sources around P3 and P4) resembled the push-pull activity of the output nuclei of the basal ganglia in action selection-inhibition operations. These findings provide insights into the neural mechanisms underlying action selection processes and the reactivation of S-R links.

Interindividual aperiodic resting-state EEG activity predicts cognitive-control styles

The ability to find the right balance between more persistent and more flexible cognitive-control styles is known as "metacontrol." Recent findings suggest a relevance of aperiodic EEG activity and task conditions that are likely to elicit a specific metacontrol style. Here we investigated whether individual differences in aperiodic EEG activity obtained off-task (during resting state) predict individual cognitive-control styles under task conditions that pose different demands on metacontrol. We analyzed EEG resting-state data, task-EEG, and behavioral outcomes from a sample of N = 65 healthy participants performing a Go/Nogo task. We examined aperiodic activity as indicator of "neural noise" in the EEG power spectrum, and participants were assigned to a high-noise or low-noise group according to a median split of the exponents obtained for resting state. We found that off-task aperiodic exponents predicted different cognitive-control styles in Go and Nogo conditions: Overall, aperiodic exponents were higher (i.e., noise was lower) in the low-noise group, who however showed no difference between Go and Nogo trials, whereas the high-noise group exhibited significant noise reduction in the more persistence-heavy Nogo condition. This suggests that trait-like biases determine the default cognitive-control style, which however can be overwritten or compensated for under challenging task demands. We suggest that aperiodic activity in EEG signals represents valid indicators of highly dynamic arbitration between metacontrol styles, representing the brain's capability to reorganize itself and adapt its neural activity patterns to changing environmental conditions.

From E-Commerce to the Metaverse: A Neuroscientific Analysis of Digital Consumer Behavior

The growing interest in consumer behavior in the digital environment is leading scholars and companies to focus on consumer behavior and choices on digital platforms, such as the metaverse. On this immersive digital shopping platform, consumer neuroscience provides an optimal opportunity to explore consumers' emotions and cognitions. In this study, neuroscience techniques (EEG, SC, BVP) were used to compare emotional and cognitive aspects of shopping between metaverse and traditional e-commerce platforms. Participants were asked to purchase the same product once on a metaverse platform (Second Life, SL) and once via an e-commerce website (EC). After each task, questionnaires were administered to measure perceived enjoyment, informativeness, ease of use, cognitive effort, and flow. Statistical analyses were conducted to examine differences between SL and EC at the neurophysiological and self-report levels, as well as between different stages of the purchase process. The results show that SL elicits greater cognitive engagement than EC, but it is also more mentally demanding, with a higher workload and more memorization, and fails to elicit a strong positive emotional response, leading to a poorer shopping experience. These findings provide insights not only for digital-related consumer research but also for companies to improve their metaverse shopping experience. Before investing in the platform or creating a digital retail space, companies should thoroughly analyze it, focusing on how to enhance users' cognition and emotions, ultimately promoting a better consumer experience. Despite its limitations, this pilot study sheds light on the emotional and cognitive aspects of metaverse shopping and suggests potential for further research with a consumer neuroscience approach in the metaverse field.

Mapping Motor Cortical Network Excitability and Connectivity Changes in De Novo Parkinson's Disease

BACKGROUND

Transcranial magnetic stimulation-electroencephalography (TMS-EEG) has demonstrated decreased excitability in the primary motor cortex (M1) and increased excitability in the pre-supplementary motor area (pre-SMA) in moderate-advanced Parkinson's disease (PD).

OBJECTIVES

The aim was to investigate whether these abnormalities are evident from the early stages of the disease, their behavioral correlates, and relationship to cortico-subcortical connections.

METHODS

Twenty-eight early, drug-naive (de novo) PD patients and 28 healthy controls (HCs) underwent TMS-EEG to record TMS-evoked potentials (TEPs) from the primary motor cortex (M1) and the pre-SMA, kinematic recording of finger-tapping movements, and a 3T-MRI (magnetic resonance imaging) scan to obtain diffusion tensor imaging (DTI) reconstruction of white matter (WM) tracts connecting M1 to the ventral lateral anterior thalamic nucleus and pre-SMA to the anterior putamen.

RESULTS

We found reduced M1 TEP P30 amplitude in de novo PD patients compared to HCs and similar pre-SMA TEP N40 amplitude between groups. PD patients exhibited smaller amplitude and slower velocity in finger-tapping movements and altered structural integrity in WM tracts of interest, although these changes did not correlate with TEPs.

CONCLUSIONS

M1 hypoexcitability is a characteristic of PD from early phases and may be a marker of the parkinsonian state. Pre-SMA hyperexcitability is not evident in early PD and possibly emerges at later stages of the disease. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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

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

Human Prosocial Preferences Are Related to Slow-Wave Activity in Sleep

Prosocial behavior is crucial for the smooth functioning of the society. Yet, individuals differ vastly in the propensity to behave prosocially. Here, we try to explain these individual differences under normal sleep conditions without any experimental modulation of sleep. Using a portable high-density EEG, we measured the sleep data in 54 healthy adults (28 females) during a normal night's sleep at the participants' homes. To capture prosocial preferences, participants played an incentivized public goods game in which they faced real monetary consequences. The whole-brain analyses showed that a higher relative slow-wave activity (SWA, an indicator of sleep depth) in a cluster of electrodes over the right temporoparietal junction (TPJ) was associated with increased prosocial preferences. Source localization and current source density analyses further support these findings. Recent sleep deprivation studies imply that sleeping enough makes us more prosocial; the present findings suggest that it is not only sleep duration, but particularly sufficient sleep depth in the TPJ that is positively related to prosociality. Because the TPJ plays a central role in social cognitive functions, we speculate that sleep depth in the TPJ, as reflected by relative SWA, might serve as a dispositional indicator of social cognition ability, which is reflected in prosocial preferences. These findings contribute to the emerging framework explaining the link between sleep and prosocial behavior by shedding light on the underlying mechanisms.

Prior probability cues bias sensory encoding with increasing task exposure

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

Why is there an error negativity on correct trials? A reappraisal

In healthy subjects, the Error Negativity (Ne) was initially reported on errors and on partial errors, only. Later on, application of the Laplacian transformation to EEG data unmasked a Ne-like wave (Nc) that shares a main generator with the Ne, suggesting that the Nc is just a small Ne. However, the reason why a small Ne would persist on correct responses remains unclear. Now, sometimes, subthreshold EMG activations in the muscles corresponding to correct responses (not strong enough to reach the response threshold) can precede full-blown correct responses. These "partially correct" activities seem to correspond to (force) execution errors, as they evoke a sizeable Ne. Within the frames of the Reward Value and Prediction Model or of the Predicted Response-Outcome model we propose that the action monitoring system evokes a Ne/Nc on correct responses because, even when a correct choice has been made, the accuracy of response (force) execution cannot be fully predicted. If this interpretation is correct, it can be assumed that, once these execution errors have been corrected, the correctness of the (full-blown) correcting response is highly predictable. Consequently, they should evoke a smaller Nc/Ne than "pure" correct responses. We show, that for the response thresholds set in the present experiment, the correcting response of the trials containing a partially correct activation evoke no identifiable Nc at all. Therefore it seems that there usually is an Error Negativity on correct trials because the correctness of response (force) execution cannot be fully predicted.

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.

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.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task

Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.

Methods

We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.

Results

Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.

Discussion

It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG

BACKGROUND

Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency.

METHODS

Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score.

RESULTS

The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%.

CONCLUSION

The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

The applied principles of EEG analysis methods in neuroscience and clinical neurology

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective.General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed.Approach.We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel-Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces.Main results.Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p< 0.001), PFC increased (p< 0.001) and PCMI decreased (p< 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p< 0.001) and nodal efficiency (NE) (p< 0.001) decreased in these two spaces. Global network indicators normalized average path length (Lave/Lr) (p< 0.01) and modularity (Q) (p< 0.05) only decreased in sensor space, while the normalized average clustering coefficient (Cave/Cr) and small-world index (σ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicatorsLave/LrandQwere not significantly different from the preoperative resting state in sensor space.Significance.GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.

An Accumulating Neural Signal Underlying Binocular Rivalry Dynamics

During binocular rivalry, conflicting images are presented one to each eye and perception alternates stochastically between them. Despite stable percepts between alternations, modeling suggests that neural signals representing the two images change gradually, and that the duration of stable percepts are determined by the time required for these signals to reach a threshold that triggers an alternation. However, direct physiological evidence for such signals has been lacking. Here, we identify a neural signal in the human visual cortex that shows these predicted properties. We measured steady-state visual evoked potentials (SSVEPs) in 84 human participants (62 females, 22 males) who were presented with orthogonal gratings, one to each eye, flickering at different frequencies. Participants indicated their percept while EEG data were collected. The time courses of the SSVEP amplitudes at the two frequencies were then compared across different percept durations, within participants. For all durations, the amplitude of signals corresponding to the suppressed stimulus increased and the amplitude corresponding to the dominant stimulus decreased throughout the percept. Critically, longer percepts were characterized by more gradual increases in the suppressed signal and more gradual decreases of the dominant signal. Changes in signals were similar and rapid at the end of all percepts, presumably reflecting perceptual transitions. These features of the SSVEP time courses are well predicted by a model in which perceptual transitions are produced by the accumulation of noisy signals. Identification of this signal underlying binocular rivalry should allow strong tests of neural models of rivalry, bistable perception, and neural suppression.SIGNIFICANCE STATEMENT During binocular rivalry, two conflicting images are presented to the two eyes and perception alternates between them, with switches occurring at seemingly random times. Rivalry is an important and longstanding model system in neuroscience, used for understanding neural suppression, intrinsic neural dynamics, and even the neural correlates of consciousness. All models of rivalry propose that it depends on gradually changing neural activity that on reaching some threshold triggers the perceptual switches. This manuscript reports the first physiological measurement of neural signals with that set of properties in human participants. The signals, measured with EEG in human observers, closely match the predictions of recent models of rivalry, and should pave the way for much future work.

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.

Temporal dynamics of attitude decisions: A test of the iterative reprocessing model using event-related potentials

Although evaluative judgments are a central component of everyday decision making little is known about the temporal dynamics of the processes used to make them. The present study used the high temporal resolution of event-related brain potentials (ERPs) to test Cunningham and Zelazo's (2007) posited differences in the timing of attitude tag retrieval relative to stimulus categorization for 'attitudes' and 'evaluations,' as well as tenets of their Iterative Reprocessing (IR) loop model. Participants made agree/disagree decisions about their attitudes and You/Not You decisions about their autobiographical memories in separate reaction time (RT) tasks while brain activity was recorded from 32 scalp sites. A median-split analysis on RT was used to separate fast and slow decisions. Decisions about autobiographical stimuli produced the typical results in which retrieval and stimulus categorization occurred together just before the response regardless of decision difficulty. By contrast, the relative timing of tag retrieval and categorization differed with difficulty for attitude decisions as predicted by the model. Fast attitude decisions were processed similarly to fast You decisions with retrieval and categorization timing coupled to the response. Slow attitude decisions, however, differed because, while tag retrieval timing was the same as for fast attitude decisions, post-retrieval processing delayed stimulus categorization and a response by 450 msec. ERP activity over dorsolateral prefrontal cortex (DLPFC) in the pre-response interval was asymmetrical, with greater activity for attitude and autobiographical decisions over left and right hemispheres, respectively, while amplitude and duration increased with decision difficulty for both. Slow attitude decisions alone elicited a reduced pre-response positivity, a correlate of goal-directed response selection. The results provide empirical support for key aspects of Cunningham and Zelazo's (2007) attitude-evaluation dichotomy and the timing of the posited component processes in their IR model as well as novel information about the roles of stored tags and reflective processes in different attitude decisions.

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.

Temporal Dynamics of Brain Activity Predicting Sense of Agency over Muscle Movements

Our muscles are the primary means through which we affect the external world, and the sense of agency (SoA) over the action through those muscles is fundamental to our self-awareness. However, SoA research to date has focused almost exclusively on agency over action outcomes rather than over the musculature itself, as it was believed that SoA over the musculature could not be manipulated directly. Drawing on methods from human-computer interaction and adaptive experimentation, we use human-in-the-loop Bayesian optimization to tune the timing of electrical muscle stimulation so as to robustly elicit a SoA over electrically actuated muscle movements in male and female human subjects. We use time-resolved decoding of subjects' EEG to estimate the time course of neural activity which predicts reported agency on a trial-by-trial basis. Like paradigms which assess SoA over action consequences, we found that the late (post-conscious) neural activity predicts SoA. Unlike typical paradigms, however, we also find patterns of early (sensorimotor) activity with distinct temporal dynamics predicts agency over muscle movements, suggesting that the "neural correlates of agency" may depend on the level of abstraction (i.e., direct sensorimotor feedback versus downstream consequences) most relevant to a given agency judgment. Moreover, fractal analysis of the EEG suggests that SoA-contingent dynamics of neural activity may modulate the sensitivity of the motor system to external input.SIGNIFICANCE STATEMENT The sense of agency, the feeling of "I did that," when directing one's own musculature is a core feature of human experience. We show that we can robustly manipulate the sense of agency over electrically actuated muscle movements, and we investigate the time course of neural activity that predicts the sense of agency over these actuated movements. We find evidence of two distinct neural processes: a transient sequence of patterns that begins in the early sensorineural response to muscle stimulation and a later, sustained signature of agency. These results shed light on the neural mechanisms by which we experience our movements as volitional.

Electrophysiological Correlates of Vocal Emotional Processing in Musicians and Non-Musicians

Musicians outperform non-musicians in vocal emotion recognition, but the underlying mechanisms are still debated. Behavioral measures highlight the importance of auditory sensitivity towards emotional voice cues. However, it remains unclear whether and how this group difference is reflected at the brain level. Here, we compared event-related potentials (ERPs) to acoustically manipulated voices between musicians (n = 39) and non-musicians (n = 39). We used parameter-specific voice morphing to create and present vocal stimuli that conveyed happiness, fear, pleasure, or sadness, either in all acoustic cues or selectively in either pitch contour (F0) or timbre. Although the fronto-central P200 (150-250 ms) and N400 (300-500 ms) components were modulated by pitch and timbre, differences between musicians and non-musicians appeared only for a centro-parietal late positive potential (500-1000 ms). Thus, this study does not support an early auditory specialization in musicians but suggests instead that musicality affects the manner in which listeners use acoustic voice cues during later, controlled aspects of emotion evaluation.

Task-Related Reorganization of Cognitive Network in Parkinson's Disease Using Electrophysiology

BACKGROUND

Cognitive deficits in Parkinson's disease (PD) patients are well described, however, their underlying neural mechanisms as assessed by electrophysiology are not clear.

OBJECTIVES

To reveal specific neural network alterations during the performance of cognitive tasks in PD patients using electroencephalography (EEG).

METHODS

Ninety participants, 60 PD patients and 30 controls underwent EEG recording while performing a GO/NOGO task. Source localization of 16 regions of interest known to play a pivotal role in GO/NOGO task was performed to assess power density and connectivity within this cognitive network. The connectivity matrices were evaluated using a graph-theory approach that included measures of cluster-coefficient, degree, and global-efficiency. A mixed-model analysis, corrected for age and levodopa equivalent daily dose was performed to examine neural changes between PD patients and controls.

RESULTS

PD patients performed worse in the GO/NOGO task (P < 0.001). The power density was higher in δ and θ bands, but lower in α and β bands in PD patients compared to controls (interaction group × band: P < 0.001), indicating a general slowness within the network. Patients had more connections within the network (P < 0.034) than controls and these were used for graph-theory analysis. Differences between groups in graph-theory measures were found only in cluster-coefficient, which was higher in PD compared to controls (interaction group × band: P < 0.001).

CONCLUSIONS

Cognitive deficits in PD are underlined by alterations at the brain network level, including higher δ and θ activity, lower α and β activity, increased connectivity, and segregated network organization. These findings may have important implications on future adaptive deep brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning

OBJECTIVE

This study explored the use of electroencephalogram (EEG) and eye gaze features, experience-related features, and machine learning to evaluate performance and learning rates in fundamentals of laparoscopic surgery (FLS) and robotic-assisted surgery (RAS).

METHODS

EEG and eye-tracking data were collected from 25 participants performing three FLS and 22 participants performing two RAS tasks. Generalized linear mixed models, using L1-penalized estimation, were developed to objectify performance evaluation using EEG and eye gaze features, and linear models were developed to objectify learning rate evaluation using these features and performance scores at the first attempt. Experience metrics were added to evaluate their role in learning robotic surgery. The differences in performance across experience levels were tested using analysis of variance.

RESULTS

EEG and eye gaze features and experience-related features were important for evaluating performance in FLS and RAS tasks with reasonable results. Residents outperformed faculty in FLS peg transfer (p value = 0.04), while faculty and residents both excelled over pre-medical students in the FLS pattern cut (p value = 0.01 and p value < 0.001, respectively). Fellows outperformed pre-medical students in FLS suturing (p value = 0.01). In RAS tasks, both faculty and fellows surpassed pre-medical students (p values for the RAS pattern cut were 0.001 for faculty and 0.003 for fellows, while for RAS tissue dissection, the p value was less than 0.001 for both groups), with residents also showing superior skills in tissue dissection (p value  = 0.03).

CONCLUSION

Findings could be used to develop training interventions for improving surgical skills and have implications for understanding motor learning and designing interventions to enhance learning outcomes.

Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions

Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact to maintain health. Within the information theory framework the information storage (IS) allows to measure the regularity and predictability of a dynamic process under stationarity assumption. However, this assumption does not allow to track over time the transient pathways occurring in the dynamical activity of a physiological system. To address this limitation, we propose a time-varying approach based on the recursive least squares algorithm (RLS) for estimating IS at each time instant, in non-stationary conditions. We tested this approach in simulated time-varying dynamics and in the analysis of electroencephalographic (EEG) signals recorded from healthy volunteers and timed with the heartbeat to investigate brain-heart interactions. In simulations, we show that the proposed approach allows to track both abrupt and slow changes in the information stored in a physiological system. These changes are reflected in its evolution and variability over time. The analysis of brain-heart interactions reveals marked differences across the cardiac cycle phases of the variability of the time-varying IS. On the other hand, the average IS values exhibit a weak modulation over parieto-occiptal areas of the scalp. Our study highlights the importance of developing more advanced methods for measuring IS that account for non-stationarity in physiological systems. The proposed time-varying approach based on RLS represents a useful tool for identifying spatio-temporal dynamics within the neurocardiac system and can contribute to the understanding of brain-heart interactions.

Developing cognitive workload and performance evaluation models using functional brain network analysis

Cognition, defined as the ability to learn, remember, sustain attention, make decisions, and solve problems, is essential in daily activities and in learning new skills. The purpose of this study was to develop cognitive workload and performance evaluation models using features that were extracted from Electroencephalogram (EEG) data through functional brain network and spectral analyses. The EEG data were recorded from 124 brain areas of 26 healthy participants conducting two cognitive tasks on a robot simulator. The functional brain network and Power Spectral Density features were extracted from EEG data using coherence and spectral analyses, respectively. Participants reported their perceived cognitive workload using the SURG-TLX questionnaire after each exercise, and the simulator generated actual performance scores. The extracted features, actual performance scores, and subjectively assessed cognitive workload values were used to develop linear models for evaluating performance and cognitive workload. Furthermore, the Pearson correlation was used to find the correlation between participants' age, performance, and cognitive workload. The findings demonstrated that combined EEG features retrieved from spectral analysis and functional brain networks can be used to evaluate cognitive workload and performance. The cognitive workload in conducting only Matchboard level 3, which is more challenging than Matchboard level 2, was correlated with age (0.54, p-value = 0.01). This finding may suggest playing more challenging computer games are more helpful in identifying changes in cognitive workload caused by aging. The findings could open the door for a new era of objective evaluation and monitoring of cognitive workload and performance.

Preserved perception-action integration in adolescents after a COVID-19 infection

Evidence is accumulating that the Coronavirus disease (COVID-19) can bring forth deficits in executive functioning via alterations in the dopaminergic system. Importantly, dopaminergic pathways have been shown to modulate how actions and perceptions are integrated within the brain. Such alterations in event file binding could thus underlie the cognitive deficits developing after a COVID-19 infection. We examined action-perception integration in a group of young people (11-19 years of age) that had been infected with COVID-19 before study participation (n = 34) and compared them to a group of uninfected healthy controls (n = 29) on the behavioral (i.e., task accuracy, reaction time) and neurophysiological (EEG) level using an established event file binding paradigm. Groups did not differ from each other regarding demographic variables or in reporting psychiatric symptoms. Overall, multiple lines of evidence (behavioral and neurophysiological) suggest that action-perception integration is preserved in adolescents who suffered from COVID-19 prior to study participation. Event file binding processes were intact in both groups on all levels. While cognitive impairments can occur following a COVID-19 infection, the study demonstrates that action-perception integration as one of the basic building blocks of cognition seems to be largely unaffected in adolescents with a rather mild course of the disease.

From pulse width modulated TENS to cortical modulation: based on EEG functional connectivity analysis

Modulation in the temporal pattern of transcutaneous electrical nerve stimulation (TENS), such as Pulse width modulated (PWM), has been considered a new dimension in pain and neurorehabilitation therapy. Recently, the potentials of PWM TENS have been studied on sensory profiles and corticospinal activity. However, the underlying mechanism of PWM TENS on cortical network which might lead to pain alleviation is not yet investigated. Therefore, we recorded cortical activity using electroencephalography (EEG) from 12 healthy subjects and assessed the alternation of the functional connectivity at the cortex level up to an hour following the PWM TENS and compared that with the effect of conventional TENS. The connectivity between eight brain regions involved in sensory and pain processing was calculated based on phase lag index and spearman correlation. The alteration in segregation and integration of information in the network were investigated using graph theory. The proposed analysis discovered several statistically significant network changes between PWM TENS and conventional TENS, such as increased local strength and efficiency of the network in high gamma-band in primary and secondary somatosensory sources one hour following stimulation. Our findings regarding the long-lasting desired effects of PWM TENS support its potential as a therapeutic intervention in clinical research.

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.

The within-person association of relative left frontal activity and vagally mediated heart rate variability not moderated by history of depression

Motivated by the Neurovisceral Integration Model (NVI) of cardiac vagal control, we investigated the relationship between relative left frontal activity (rLFA) and vagally mediated heart rate variability or respiratory sinus arrhythmia (RSA) in 287 participants, half of whom had a history of depression. We hypothesized that there would be a within-person association of rLFA and RSA such that when RSA is lower rLFA would also be lower (Hypothesis I). Moreover, it was hypothesized that this within-subject association would be moderated by a history of depression (Hypothesis II). Metrics of rLFA and RSA were derived from concurrent electroencephalogram and electrocardiogram recordings. The logarithmic difference in EEG alpha power between the homologous right and left electrodes (Ln (Right/Left)) in the frontal region was used to index rLFA. A Hilbert transform was applied to the mean-centered and bandpass-filtered (0.12-.40 Hz) inter-beat interval (IBI) time series to get a fine-grained measure (in the time domain) of RSA. A linear mixed ANOVA model with rLFA as the dependent variable and RSA as the main fixed effect found that participants had less rLFA during epochs when they had lower RSA, which was consistent with the prediction from Hypothesis I. Contrary to the prediction from Hypothesis II, the within-person association of RSA and rLFA was not moderated by a history of depression. However, the association between RSA and rLFA varied across the four pairs of frontal electrodes that we examined. Thus, more research is needed to determine the spatial extent of this association, e.g., examining the relationship between source-localized rLFA and RSA.

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.

Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG

The cortical response to transcranial magnetic stimulation (TMS) has notable inter-trial variability. One source of this variability can be the influence of the phase and power of pre-stimulus neuronal oscillations on single-trial TMS responses. Here, we investigate the effect of brain oscillatory activity on TMS response in 49 distinct healthy participants (64 datasets) who had received single-pulse TMS over the left dorsolateral prefrontal cortex. Across all frequency bands of theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 Hz), there was no significant effect of pre-TMS phase on single-trial cortical evoked activity. After high-powered oscillations, whether followed by a TMS pulse or not, the subsequent activity was larger than after low-powered oscillations. We further defined a measure, corrected_effect, to enable us to investigate brain responses to the TMS pulse disentangled from the power of ongoing (spontaneous) oscillations. The corrected_effect was significantly different from zero (meaningful added effect of TMS) only in theta and beta bands. Our results suggest that brain state prior to stimulation might play some role in shaping the subsequent TMS-EEG response. Specifically, our findings indicate that the power of ongoing oscillatory activity, but not phase, can influence brain responses to TMS. Aligning the TMS pulse with specific power thresholds of an EEG signal might therefore reduce variability in neurophysiological measurements and also has the potential to facilitate more robust therapeutic effects of stimulation.

Corticospinal excitability is highest at the early rising phase of sensorimotor µ-rhythm

Alpha oscillations are thought to reflect alternating cortical states of excitation and inhibition. Studies of perceptual thresholds and evoked potentials have shown the scalp EEG negative phase of the oscillation to correspond to a short-lasting low-threshold and high-excitability state of underlying visual, somatosensory, and primary motor cortex. The negative peak of the oscillation is assumed to correspond to the state of highest excitability based on biophysical considerations and considerable effort has been made to improve the extraction of a predictive signal by individually optimizing EEG montages. Here, we investigate whether it is the negative peak of sensorimotor µ-rhythm that corresponds to the highest corticospinal excitability, and whether this is consistent between individuals. In 52 adult participants, a standard 5-channel surface Laplacian EEG montage was used to extract sensorimotor µ-rhythm during transcranial magnetic stimulation (TMS) of primary motor cortex. Post-hoc trials were sorted from 800 TMS-evoked motor potentials (MEPs) according to the pre-stimulus EEG (estimated instantaneous phase) and MEP amplitude (as an index of corticospinal excitability). Different preprocessing transformations designed to improve the accuracy by which µ-alpha phase predicts excitability were also tested. By fitting a sinusoid to the MEP amplitudes, sorted according to pre-stimulus EEG-phase, we found that excitability was highest during the early rising phase, at a significant delay with respect to the negative peak by on average 45° or 10 ms. The individual phase of highest excitability was consistent across study participants and unaffected by two different EEG-cleaning methods that utilize 64 channels to improve signal quality by compensating for individual noise level and channel covariance. Personalized transformations of the montage did not yield better prediction of excitability from µ-alpha phase. The relationship between instantaneous phase of a brain oscillation and fluctuating cortical excitability appears to be more complex than previously hypothesized. In TMS of motor cortex, a standard surface Laplacian 5-channel EEG montage is effective in extracting a predictive signal and the phase corresponding to the highest excitability appears to be consistent between individuals. This is an encouraging result with respect to the clinical potential of therapeutic personalized brain interventions in the motor system. However, it remains to be investigated, whether similar results can be obtained for other brain areas and brain oscillations targeted with EEG and TMS.

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.

An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing

An explainable Artificial Intelligence (xAI) approach is proposed to longitudinally monitor subjects affected by Mild Cognitive Impairment (MCI) by using high-density electroencephalography (HD-EEG). To this end, a group of MCI patients was enrolled at IRCCS Centro Neurolesi Bonino Pulejo of Messina (Italy) within a follow-up protocol that included two evaluations steps: T0 (first evaluation) and T1 (three months later). At T1, four MCI patients converted to Alzheimer's Disease (AD) and were included in the analysis as the goal of this work was to use xAI to detect individual changes in EEGs possibly related to the degeneration from MCI to AD. The proposed methodology consists in mapping segments of HD-EEG into channel-frequency maps by means of the power spectral density. Such maps are used as input to a Convolutional Neural Network (CNN), trained to label the maps as "T0" (MCI state) or "T1" (AD state). Experimental results reported high intra-subject classification performance (accuracy rate up to 98.97% (95% confidence interval: 98.68-99.26)). Subsequently, the explainability of the proposed CNN is explored via a Grad-CAM approach. The procedure detected which EEG-channels (i.e., head region) and range of frequencies (i.e., sub-bands) were more active in the progression to AD. The xAI analysis showed that the main information is included in the delta sub-band and that, limited to the analyzed dataset, the highest relevant areas are: the left-temporal and central-frontal lobe for Sb01, the parietal lobe for Sb02, the left-frontal lobe for Sb03 and the left-frontotemporal region for Sb04.

Velocity-tuning of somatosensory EEG predicts the pleasantness of gentle caress

Numerous studies have established an inverted u-shaped effect between the velocity of a caress and its pleasantness and linked this effect to the C-tactile (CT) system considered central for physical and mental health. This study probed whether cortical somatosensory representations predict and explain the inverted u-shaped effect and addressed associated individual differences. Study participants (N = 90) rated the pleasantness of stroking at varying velocities while their electroencephalogram was being recorded. An analysis across all participants replicated a preference for intermediate velocities, while a cluster analysis discriminated individuals who preferred slow (N = 43) from those who preferred fast stroking (N = 47). In both groups, intermediate velocities maximized amplitudes of a somatosensory event-related potential referred to as sN400, in line with the average rating effect. By contrast, group differences emerged in how velocity modulated a late positive potential (LPP) and Rolandic power. Notably, both the sN400 and the velocity-tuning of LPP and Rolandic power predicted the participants' pleasantness ratings. Participants were more likely to prefer slow over fast stroking the better their LPP and Rolandic power differentiated between different velocities. Together, these results shed light on the complexity of tactile affect. They corroborate an average preference for intermediate velocities that relates to largely shared effects of CT-targeted touch on the activity of somatosensory cortex. Additionally, they identify individual differences as a function of how accurately somatosensory cortex represents the velocity of peripheral input and suggest these differences are relevant for the extent to which individuals pursue beneficial, CT-targeted touch.

Mild Cognitive Impairment in African Americans Is Associated with Differences in EEG Theta/Beta Ratio

BACKGROUND

Identification of older individuals with increased risk for cognitive decline can contribute not only to personal benefits (e.g., early treatment, evaluation of treatment), but could also benefit clinical trials (e.g., patient selection). We propose that baseline resting-state electroencephalography (rsEEG) could provide markers for early identification of cognitive decline.

OBJECTIVE

To determine whether rsEEG theta/beta ratio (TBR) differed between mild cognitively impaired (MCI) and healthy older adults.

METHODS

We analyzed rsEEG from a sample of 99 (ages 60-90) consensus-diagnosed, community-dwelling older African Americans (58 cognitively typical and 41 MCI). Eyes closed rsEEGs were acquired before and after participants engaged in a visual motion direction discrimination task. rsEEG TBR was calculated for four midline locations and assessed for differences as a function of MCI status. Hemispheric asymmetry of TBR was also analyzed at equidistant lateral electrode sites.

RESULTS

Results showed that MCI participants had a higher TBR than controls (p = 0.04), and that TBR significantly differed across vertex location (p < 0.001) with the highest TBR at parietal site. MCI and cognitively normal controls also differed in hemispheric asymmetries, such that MCI show higher TBR at frontal sites, with TBR greater over right frontal electrodes in the MCI group (p = 0.003) and no asymmetries found in the cognitively normal group. Lastly, we found a significant task aftereffect (post-task compared to pre-task measures) with higher TBR at posterior locations (Oz p = 0.002, Pz p = 0.057).

CONCLUSION

TBR and TBR asymmetries differ between MCI and cognitively normal older adults and may reflect neurodegenerative processes underlying MCI symptoms.

Cortical mechanisms of sensory trick in cervical dystonia

Patients with cervical dystonia (CD) often show an improvement in dystonic posture after sensory trick (ST), though the mechanisms underlying ST remain unclear. In this study, we aimed to investigate the effects of ST on cortical activity in patients with CD and to explore the contribution of motor and sensory components to ST mechanisms. To this purpose, we studied 15 CD patients with clinically effective ST, 17 without ST, and 14 healthy controls (HCs) who mimicked the ST. We used electroencephalographic (EEG) recordings and electromyography (EMG) data from bilateral sternocleidomastoid (SCM) muscles. We compared ST-related EEG spectral changes from sensorimotor and posterior parietal areas and EMG power changes between groups. To better understand the contribution of motor and sensory components to ST, we tested EEG and EMG correlates of three different conditions mimicking ST, the first without skin touch ("no touch" condition), the second without voluntary movements ("passive" condition), and finally without arm movements ("examiner touch" condition). Results showed ST-related alpha desynchronization in the sensorimotor cortex and theta desynchronization in the sensorimotor and posterior parietal cortex. Both spectral changes were more significant during maneuver execution in CD patients with ST than in CD patients without ST and HCs who mimicked the ST. Differently, the "no touch", "passive", or "examiner touch" conditions did not show significant differences in EEG or EMG changes determined by ST execution/mimicking between CD patients with or without ST. A higher desynchronization within alpha and theta bands in the sensorimotor and posterior parietal areas correlated with a more significant activity decrease in the contralateral SCM muscle, Findings from this study suggest that ST-related changes in the activity of sensorimotor and posterior parietal areas may restore dystonic posture and that both motor and sensory components contribute to the ST effect.

Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task

Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception-action system and investigated based on brain-behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) "suturing and intracorporeal knot-tying" task (FLS complex task)-the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain-behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1-40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS-EEG signals. The HbO signal from the overlying the left inferior frontal gyrus-opercular part, left superior frontal gyrus-medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus-medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a 'focusing' effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the 'focusing' effect.

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.

Neurocognitive analyses reveal that video game players exhibit enhanced implicit temporal processing

Winning in action video games requires to predict timed events in order to react fast enough. In these games, repeated waiting for enemies may help to develop implicit (incidental) preparation mechanisms. We compared action video game players and non-video game players in a reaction time task involving both implicit time preparations and explicit (conscious) temporal attention cues. Participants were immersed in virtual reality and instructed to respond to a visual target appearing at variable delays after a warning signal. In half of the trials, an explicit cue indicated when the target would occur after the warning signal. Behavioral, oculomotor and EEG data consistently indicate that, compared with non-video game players, video game players better prepare in time using implicit mechanisms. This sheds light on the neglected role of implicit timing and related electrophysiological mechanisms in gaming research. The results further suggest that game-based interventions may help remediate implicit timing disorders found in psychiatric populations.

A cross-sectional EEG study reveals that individuals who consistently play action video games exhibit improved performance in a reaction time task involving implicit time preparations, compared with participants who did not normally play video games

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.

Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders

OBJECTIVE

In publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilized to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognized. Here, we seek to emphasize the extent of residual EMG contamination of EEG.

METHODS

We compared scalp electrical recordings after applying different EMG-pruning methods with recordings of EMG-free data from 6 fully paralyzed healthy subjects. We calculated the ratio of the power of pruned, normal scalp electrical recordings in the six subjects, to the power of unpruned recordings in the same subjects when paralyzed. We produced "contamination graphs" for different pruning methods.

RESULTS

EMG contamination exceeds EEG signals progressively more as frequencies exceed 25 Hz and with distance from the vertex. In contrast, Laplacian signals are spared in central scalp areas, even to 100 Hz.

CONCLUSION

Given probable EMG contamination of EEG in psychiatric and other studies, few findings on beta- or gamma-frequency power can be relied upon. Based on the effectiveness of current methods of EEG de-contamination, investigators should be able to reanalyze recorded data, reevaluate conclusions from high-frequency EEG data, and be aware of limitations of the methods.

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

The Role of the Emotional Sequence in the Communication of the Territorial Cheeses: A Neuromarketing Approach

Over the past few years, many studies have shown how territoriality can be considered a driver for purchasing agri-food products. Products with certification of origin are perceived as more sustainable, safer and of better quality. At the same time, producers of traditional products often belong to small entities that struggle to compete with large multinational food corporations, having less budget to allocate to product promotion. In this study, we propose a neuromarketing approach, showing how the use of these techniques can help in choosing the most effective commercial in terms of likeability and ability to activate mnemonic processes. Two commercials were filmed for the purpose of this study. They differed from each other in terms of emotional sequence. The first aimed primarily at eliciting positive emotions derived from the product description. The second aimed to generate negative emotions during the early stages, highlighting the negative consequences of humans' loss of contact with nature and tradition and then eliciting positive emotions by presenting cheese production using traditional techniques as a solution to the problem. Based on the literature on the emotional sequences in social advertising, we hypothesised that the second commercial would generate an overall better emotional reaction and activate mnemonic processes to a greater extent. Our results partially support the research hypotheses, providing useful insights both to marketers and for future research on the topic.

The Relation between Induced Electric Field and TMS-Evoked Potentials: A Deep TMS-EEG Study

Transcranial magnetic stimulation (TMS) in humans induces electric fields (E-fields, EF) that perturb and modulate the brain’s endogenous neuronal activity and result in the generation of TMS-evoked potentials (TEPs). The exact relation of the characteristics of the induced E-field and the intensity of the brains’ response, as measured by electroencephalography (EEG), is presently unclear. In this pilot study, conducted on three healthy subjects and two patients with generalized epilepsy (total: 3 males, 2 females, mean age of 26 years; healthy: 2 males, 1 female, mean age of 25.7 years; patients: 1 male, 1 female, mean age of 26.5 years), we investigated the temporal and spatial relations of the E-field, induced by single-pulse stimuli, and the brain’s response to TMS. Brain stimulation was performed with a deep TMS device (BrainsWay Ltd., Jerusalem, Israel) and an H7 coil placed over the central area. The induced EF was computed on personalized anatomical models of the subjects through magneto quasi-static simulations. We identified specific time instances and brain regions that exhibit high positive or negative associations of the E-field with brain activity. In addition, we identified significant correlations of the brain’s response intensity with the strength of the induced E-field and finally prove that TEPs are better correlated with E-field characteristics than with the stimulator’s output. These observations provide further insight in the relation between E-field and the ensuing cortical activation, validate in a clinically relevant manner the results of E-field modeling and reinforce the view that personalized approaches should be adopted in the field of non-invasive brain stimulation.

EEG asymmetry and cognitive testing in MCI identification

BACKGROUND

Finding the baseline resting-state EEG markers for early identification of cognitive decline can contribute to the identification of individuals at risk of further change. Potential applications include identifying participants for clinical trials, early treatment, and evaluation of treatment, accessible even from a community setting.

METHODS

Analyses were completed on a sample of 99 (ages 60-90) consensus-diagnosed, community-dwelling African Americans (58 cognitively typical/HC, and 41 mildly cognitively impaired/MCI), who were recruited from the Michigan Alzheimer's Disease Research Center (MADRC) and the Wayne State University Institute of Gerontology. In addition to neuropsychological testing with CogState and Toolbox computerized batteries, resting-state EEGs (rsEEG, eyes closed) were acquired before and after participants were engaged in a visual motion direction discrimination task. rsEEG frontal alpha asymmetry (FAA) and frontal beta asymmetry (FBA) were calculated.

RESULTS

FAA showed no difference across groups for the pre-task resting state. FBA was significantly different between groups, with more asymmetric frontal beta in MCI. Both physiological indices, however, along with computerized neuropsychological tests were significant predictors in logistic regression classification of MCI vs. control participants.

CONCLUSION

rsEEG asymmetries can contribute significantly to successful discrimination of older persons with MCI from those without, over and above cognitive testing, alone.

Spectral differences in resting-state EEG associated to individual Emotional Styles

The ability to manage the emotions has been associated to the Emotional Styles (ES), a set of coherent ways to deal with life's experiences. Recently, the Emotional Style Questionnaire (ESQ) has been proposed as a self-report mea-sure to assess the individual ES. The present study investigates the spectral differences in the resting-state EEG due to the individual ES, in order to support the psychometric reliability of the ESQ with associated neurophysiological measurements. In the alpha and beta band, Social Intuition showed significant and large (d > 0.8) effect sizes on the parietal and parieto-occipital regions, as well as a significant and large effect size in the gamma band on the pre-frontal region. In the beta band, Attention showed a significant and large effect size on the parieto-occipital region.