Imaging human EEG dynamics using independent component analysis

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.

Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction

Ocular artifacts, including blinks and saccades, pose significant challenges in the analysis of electroencephalographic (EEG) data, often obscuring crucial neural signals. This tutorial provides a comprehensive guide to the most effective methods for correcting these artifacts, with a focus on algorithms designed for both laboratory and real-world settings. We review traditional approaches, such as regression-based techniques and Independent Component Analysis (ICA), alongside more advanced methods like Artifact Subspace Reconstruction (ASR) and deep learning-based algorithms. Through detailed step-by-step instructions and comparative analysis, this tutorial equips researchers with the tools necessary to maintain the integrity of EEG data, ensuring accurate and reliable results in neurophysiological studies. The strategies discussed are particularly relevant for wearable EEG systems and real-time applications, reflecting the growing demand for robust and adaptable solutions in applied neuroscience.

Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals

The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.

Age-Related Differences in the Effects of Linguistic and Nonlinguistic Masking on Semantic Processing: Evidence From the N400 Component in Young and Middle-Aged Listeners

PURPOSE

The purpose of this study was to determine if there are age-related differences in semantic processing with linguistic and nonlinguistic masking, as measured by the N400.

METHOD

Sixteen young (19-31 years) and 16 middle-aged (41-57 years) adults with relatively normal hearing sensitivity were asked to determine whether word pairs were semantically related or unrelated in three listening conditions: quiet, forward, and reverse two-talker speech competition at 0 dB SNR. Behavioral data (accuracies and reaction times) and auditory event-related potential data (N400 amplitudes and latencies) were analyzed using separate mixed design multivariate analysis of variances.

RESULTS

Mean N400 amplitudes for semantically related word pairs were similar between young and middle-aged adults. Although neither group showed N400 amplitude differences between masker types, N400 amplitude was significantly greater in the presence of linguistic and nonlinguistic masking than in quiet. In contrast, mean N400 amplitudes for semantically unrelated words were significantly more negative for young adults and not significantly different among listening conditions.

CONCLUSIONS

Our findings illustrated age-related differences during a semantic processing task, as indexed by the N400, that may not be evident in suprathreshold speech repetition/recognition tasks or behavioral data. Additionally, N400 amplitudes indicated that linguistic masking effects were equivalent to nonlinguistic masking effects on semantic processing.

Parsing evoked and induced gamma response differences in Autism: A visual evoked potential study

OBJECTIVE

Electroencephalography (EEG) measures of visual evoked potentials (VEPs) provide a targeted approach for investigating neural circuit dynamics. This study separately analyses phase-locked (evoked) and non-phase-locked (induced) gamma responses within the VEP to comprehensively investigate circuit differences in autism.

METHODS

We analyzed VEP data from 237 autistic and 114 typically developing (TD) children aged 6-11, collected through the Autism Biomarkers Consortium for Clinical Trials (ABC-CT). Evoked and induced gamma (30-90 Hz) responses were separately quantified using a wavelet-based time-frequency analysis, and group differences were evaluated using a permutation-based clustering procedure.

RESULTS

Autistic children exhibited reduced evoked gamma power but increased induced gamma power compared to TD peers. Group differences in induced responses showed the most prominent effect size and remained statistically significant after excluding outliers.

CONCLUSIONS

Our study corroborates recent research indicating diminished evoked gamma responses in children with autism. Additionally, we observed a pronounced increase in induced power. Building upon existing ABC-CT findings, these results highlight the potential to detect variations in gamma-related neural activity, despite the absence of significant group differences in time-domain VEP components.

SIGNIFICANCE

The contrasting patterns of decreased evoked and increased induced gamma activity in autistic children suggest that a combination of different EEG metrics may provide a clearer characterization of autism-related circuitry than individual markers alone.

Adaptive Changes in Neurovascular Properties With Binocular Accommodation Functions in Myopic Participants by 3D Visual Training: An EEG and fNIRS Study

Although three-dimensional visual training (3DVT) has been used for myopia intervention, its neural mechanisms remain largely unknown. In this study, visual function was examined before and after 3DVT, while resting-state EEG-fNIRS signals were recorded from 38 myopic participants. A graph theoretical analysis was applied to compute the neurovascular properties, including static brain networks (SBNs), dynamic brain networks (DBNs), and dynamic neurovascular coupling (DNC). Correlations between the changes in neurovascular properties and the changes in visual functions were calculated. After 3DVT, the local efficiency and node efficiency in the frontal lobes increased in the SBNs constructed from EEG δ -band; the global efficiency and node efficiency in the frontal-parietal lobes decreased in the DBNs variability constructed from EEG δ -band. For the DNC constructed with EEG α -band and oxyhemoglobin (HbO), the local efficiency decreased, for EEG α -band and deoxyhemoglobin (HbR), the node efficiency in the frontal-occipital lobes decreased. For the SBNs constructed from HbO, the functional connectivity (FC) between the frontal-occipital lobes increased. The DNC constructed between the FC of the frontal-parietal lobes from EEG β -band and the FC of the frontal-occipital lobes from HbO increased, and between the FC of the frontal-occipital lobes from EEG β -band and the FC of the inter-frontal lobes from HbR increased. The neurovascular properties were significantly correlated with the amplitude of accommodation and accommodative facility. The result indicated the positive effects of 3DVT on myopic participants, including improved efficiency of brain networks, increased FC of SBNs and DNC, and enhanced binocular accommodation functions.

Electrocortical activity associated with movement-related fear: a methodological exploration of a threat-conditioning paradigm involving destabilising perturbations during quiet standing

Musculoskeletal trauma often leads to lasting psychological impacts stemming from concerns of future injuries. Often referred to as kinesiophobia or re-injury anxiety, such concerns have been shown to hinder return to physical activity and are believed to increase the risk for secondary injuries. Screening for re-injury anxiety is currently restricted to subjective questionnaires, which are prone to self-report bias. We introduce a novel approach to objectively identify electrocortical activity associated with the threat of destabilising perturbations. We aimed to explore its feasibility among non-injured persons, with potential future implementation for screening of re-injury anxiety. Twenty-three participants stood blindfolded on a translational balance perturbation platform. Consecutive auditory stimuli were provided as low (neutral stimulus [CS-]) or high (conditioned stimulus [CS+]) tones. For the main experimental protocol (Protocol I), half of the high tones were followed by a perturbation in one of eight unpredictable directions. A separate validation protocol (Protocol II) requiring voluntary squatting without perturbations was performed with 12 participants. Event-related potentials (ERP) were computed from electroencephalography recordings and significant time-domain components were detected using an interval-wise testing procedure. High-amplitude early contingent negative variation (CNV) waves were significantly greater for CS+ compared with CS- trials in all channels for Protocol I (> 521-800ms), most prominently over frontal and central midline locations (P ≤ 0.001). For Protocol II, shorter frontal ERP components were observed (541-609ms). Our test paradigm revealed electrocortical activation possibly associated with movement-related fear. Exploring the discriminative validity of the paradigm among individuals with and without self-reported re-injury anxiety is warranted.

Adaptive Filtering with Fitted Noise Estimate (AFFiNE): Blink Artifact Correction in Simulated and Real P300 Data

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter's reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful.

Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research

Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.

Patterns of spontaneous neural activity associated with social communication abilities among infants and toddlers showing signs of autism

Early disruptions to social communication development, including delays in joint attention and language, are among the earliest markers of autism spectrum disorder (autism, henceforth). Although social communication differences are a core feature of autism, there is marked heterogeneity in social communication-related development among infants and toddlers exhibiting autism symptoms. Neural markers of individual differences in joint attention and language abilities may provide important insight into heterogeneity in autism symptom expression during infancy and toddlerhood. This study examined patterns of spontaneous electroencephalography (EEG) activity associated with joint attention and language skills in 70 community-referred 12- to 23-month-olds with autism symptoms and elevated scores on an autism diagnostic instrument. Data-driven cluster-based permutation analyses revealed significant positive associations between relative alpha power (6-9 Hz) and concurrent response to joint attention skills, receptive language, and expressive language abilities. Exploratory analyses also revealed significant negative associations between relative alpha power and measures of core autism features (i.e., social communication difficulties and restricted/repetitive behaviors). These findings shed light on the neural mechanisms underlying typical and atypical social communication development in emerging autism and provide a foundation for future work examining neural predictors of social communication growth and markers of intervention response.

Correlated P300b and phasic pupil-dilation responses to motivationally significant stimuli

Motivationally significant events like oddball stimuli elicit both a characteristic event-related potential (ERPs) known as P300 and a set of autonomic responses including a phasic pupil dilation. Although co-occurring, P300 and pupil-dilation responses to oddball events have been repeatedly found to be uncorrelated, suggesting separate origins. We re-examined their relationship in the context of a three-stimulus version of the auditory oddball task, independently manipulating the frequency (rare vs. repeated) and motivational significance (relevance for the participant's task) of the stimuli. We used independent component analysis to derive a P300b component from EEG traces and linear modeling to separate a stimulus-related pupil-dilation response from a potentially confounding action-related response. These steps revealed that, once the complexity of ERP and pupil-dilation responses to oddball targets is accounted for, the amplitude of phasic pupil dilations and P300b are tightly and positively correlated (across participants: r = .69 p = .002), supporting their coordinated generation.

Adaptive spatial-temporal neural network for ADHD identification using functional fMRI

Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification.

Orthogonal extended infomax algorithm

Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Identification of Early Hippocampal Dynamics during Recognition Memory with Independent Component Analysis

The hippocampus is generally considered to have relatively late involvement in recognition memory, its main electrophysiological signature being between 400 and 800 ms after stimulus onset. However, most electrophysiological studies have analyzed the hippocampus as a single responsive area, selecting only a single-site signal exhibiting the strongest effect in terms of amplitude. These classical approaches may not capture all the dynamics of this structure, hindering the contribution of other hippocampal sources that are not located in the vicinity of the selected site. We combined intracerebral electroencephalogram recordings from epileptic patients with independent component analysis during a recognition memory task involving the recognition of old and new images. We identified two sources with different responses emerging from the hippocampus: a fast one (maximal amplitude at ∼250 ms) that could not be directly identified from raw recordings and a latter one, peaking at ∼400 ms. The former component presented different amplitudes between old and new items in 6 out of 10 patients. The latter component had different delays for each condition, with a faster activation (∼290 ms after stimulus onset) for recognized items. We hypothesize that both sources represent two steps of hippocampal recognition memory, the faster reflecting the input from other structures and the latter the hippocampal internal processing. Recognized images evoking early activations would facilitate neural computation in the hippocampus, accelerating memory retrieval of complementary information. Overall, our results suggest that the hippocampal activity is composed of several sources with an early activation related to recognition memory.

Multi-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example

The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for electroencephalographic (EEG) data, as made freely available in the open-source EP Toolkit (Dien, 2010). First the major EEG artifact correction methods (regression, spatial filters, principal components analysis, and independent components analysis) are reviewed. Contrary to the dominant approach of picking one method that is thought to be most effective, this review concludes that none are globally superior, but rather each has strengths and weaknesses. Then each of the major artifact types are reviewed (Blink, Corneo-Retinal Dipole, Saccadic Spike Potential, and Movement). For each one, it is proposed that one of the major correction methods is best matched to address it, resulting in the MAAC procedure. The MAAC itself is then presented, as implemented in the EP Toolkit, in order to provide a sense of the user experience. The primary goal of this present paper is to make the conceptual argument for the MAAC approach.

A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review

Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning-a neuroimaging technique that simultaneously measures the activity of multiple brain regions-has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.

Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers

Electroencephalography (EEG) is widely used to monitor epileptic seizures, and standard clinical practice consists of monitoring patients in dedicated epilepsy monitoring units via video surveillance and cumbersome EEG caps. Such a setting is not compatible with long-term tracking under typical living conditions, thereby motivating the development of unobtrusive wearable solutions. However, wearable EEG devices present the challenges of fewer channels, restricted computational capabilities, and lower signal-to-noise ratio. Moreover, artifacts presenting morphological similarities to seizures act as major noise sources and can be misinterpreted as seizures. This paper presents a combined seizure and artifacts detection framework targeting wearable EEG devices based on Gradient Boosted Trees. The seizure detector achieves nearly zero false alarms with average sensitivity values of [Formula: see text] for 182 seizures from the CHB-MIT dataset and [Formula: see text] for 25 seizures from the private dataset with no preliminary artifact detection or removal. The artifact detector achieves a state-of-the-art accuracy of [Formula: see text] (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms-up to [Formula: see text] compared to standalone seizure detection. Optimized for a Parallel Ultra-Low Power platform, these algorithms enable extended monitoring with a battery lifespan reaching 300 h. These findings highlight the benefits of integrating artifact detection in wearable epilepsy monitoring devices to limit the number of false positives.

Corticomuscular cross-recurrence analysis reveals between-limb differences in motor control among individuals with ACL reconstruction

Surgical reconstruction of the anterior cruciate ligament (ACL) and subsequent physical therapy can help athletes return to competition; however, re-injury rates remain disproportionately high due, in part, to lingering biomechanical and neurological factors that are not fully addressed during rehabilitation. Prior reports indicate that individuals exhibit altered electrical activity in both brain and muscle after ACL reconstruction (ACLR). In this investigation, we aimed to extend existing approaches by introducing a novel non-linear analysis of corticomuscular dynamics, which does not assume oscillatory coupling between brain and muscle: Corticomuscular cross-recurrence analysis (CM-cRQA). Our findings indicate that corticomuscular dynamics vary significantly between involved (injured) and uninvolved legs of participants with ACLR during voluntary isometric contractions between the brain and both the vastus medialis and lateralis. This finding points to a potential lingering neural deficit underlying re-injury for athletes after surgical reconstruction, namely the dynamical structure of neuromuscular (brain to quad muscle) coordination, which is significantly asymmetric, between limbs, in those who have ACLR.

Ongoing EEG artifact correction using blind source separation

OBJECTIVE

Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online.

METHODS

The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts.

RESULTS

The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time.

CONCLUSIONS

Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals.

SIGNIFICANCE

The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

Rapid withdrawal from a threatening animal is movement-specific and mediated by reflex-like neural processing

Responses to potentially dangerous stimuli are among the most basic animal behaviors. While research has shown that threats automatically capture the attention of human participants, research has failed to demonstrate automatic behavioral responses to threats in humans. Using a novel naturalistic paradigm, we show that two species of animals humans often report fearing trigger rapid withdrawal responses: participants withdrew their arm from photos of snakes and spiders faster, and with higher acceleration when compared to bird and butterfly stimuli. The behavior was specific to withdrawal as approach movements or button-press/release tasks failed to detect a similar difference. Moreover, between-participant differences in how aversive they found the stimuli predicted the participant's withdrawal speed, indicating that the paradigm was also sensitive to trait-level differences between individuals. Using electroencephalography (EEG), we show that the fast withdrawal was mediated by two attentional processes. First, fast withdrawal responses were associated with early amplification of sensory signals (40-110 ms after stimulus). Second, a later correlate of feature-based attention (early posterior negativity, EPN, 200-240 ms after stimulus) revealed the opposite pattern: Stronger EPN was associated with slower behavioral responses, suggesting that the deployment of attention towards the threatening stimulus features, or failure to "disengage" attention from the stimulus, was detrimental for withdrawal speed. Altogether, the results suggest that rapid behavioral withdrawal from a threatening animal is mediated by reflex-like attentional processing, and later, conscious attention to stimulus features may hinder escaping the treat.

A table tennis serve versus rally hit elicits differential hemispheric electrocortical power fluctuations

Human visuomotor control requires coordinated interhemispheric interactions to exploit the brain's functional lateralization. In right-handed individuals, the left hemisphere (right arm) is better for dynamic control and the right hemisphere (left arm) is better for impedance control. Table tennis is a game that requires precise movements of the paddle, whole body coordination, and cognitive engagement, providing an ecologically valid way to study visuomotor integration. The sport has many different types of strokes (e.g., serve, return, and rally shots), which should provide unique cortical dynamics given differences in the sensorimotor demands. The goal of this study was to determine the hemispheric specialization of table tennis serving - a sequential, self-paced, bimanual maneuver. We used time-frequency analysis, event-related potentials, and functional connectivity measures of source-localized electrocortical clusters and compared serves with other types of shots, which varied in the types of movement required, attentional focus, and other task demands. We found greater alpha (8-12 Hz) and beta (13-30 Hz) power in the right sensorimotor cortex than in the left sensorimotor cortex, and we found a greater magnitude of spectral power fluctuations in the right sensorimotor cortex for serve hits than return or rally hits, in all right-handed participants. Surprisingly, we did not find a difference in interhemispheric functional connectivity between a table tennis serve and return or rally hits, even though a serve could arguably be a more complex maneuver. Studying real-world brain dynamics of table tennis provides insight into bilateral sensorimotor integration.NEW & NOTEWORTHY We found different spectral power fluctuations in the left and right sensorimotor cortices during table tennis serves, returns, and rallies. Our findings contribute to the basic science understanding of hemispheric specialization in a real-world context.

Single-trial ERP Quantification Using Neural Networks

Traditional approaches to quantify components in event-related potentials (ERPs) are based on averaging EEG responses. However, this method ignores the trial-to-trial variability in the component's latency, resulting in a smeared version of the component and underestimates of its amplitude. Different techniques to quantify ERP components in single trials have therefore been described in literature. In this study, two approaches based on neural networks are proposed and their performance was compared with other techniques using simulated data and two experimental datasets. On the simulated dataset, the neural networks outperformed other techniques for most signal-to-noise ratios and resulted in better estimates of the topography and shape of the ERP component. In the first experimental dataset, the highest correlation values between the estimated latencies of the P300 component and the reaction times were obtained using the neural networks. In the second dataset, the single-trial latency estimation techniques showed an amplitude reduction of the N400 effect with age and ascertained this effect could not be attributed to differences in latency variability. These results illustrate the applicability and the added value of neural networks for the quantification of ERP components in individual trials. A limitation, however, is that simulated data is needed to train the neural networks, which can be difficult when the ERP components to be found are not known a priori. Nevertheless, the neural networks-based approaches offer more information on the variability of the timing of the component and result in better estimates of the shape and topography of ERP components.

iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG

The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.

Modulation of the heartbeat evoked cortical potential by hypnotizability and hypnosis

Hypnotizability is a psychophysiological trait measured by scales and associated with several differences, including interoceptive accuracy and the morpho-functional characteristics of interoception-related brain regions. The aim of the study was to assess whether the amplitude of the heartbeat evoked cortical potential (HEP), a correlate of interoceptive accuracy, differs in participants with low (lows) and high (highs) hypnotizability scores (assessed by the Stanford Hypnotic Susceptibility Scale, Form A) before and after the induction of hypnosis. ECG and EEG were monitored in 16 highs and 15 lows during an experimental session, including open eyes baseline (B), closed eyes relaxation (R), hypnotic induction (IND), neutral hypnosis (NH), and post session baseline (Post). No significant difference was observed between groups and conditions in autonomic variables. The HEP amplitude was lower in highs than in lows at the right parietal site, likely due to hypnotizability related differences in the functional connection between the right insula and parietal cortex. It increased in highs and decreased in lows across the session, possibly due to the highs' preeminently internally directed attention and to the lows' possible disengagement from the task. Since interoception is involved in several cognitive-emotional functions, its hypnotizability related differences may contribute to the variability of experience and behavior in daily life.

Children With Bilateral Cerebral Palsy Exhibit Bimanual Asymmetric Motor Deficits and EEG Evidence of Dominant Sensorimotor Hemisphere Overreliance During Reaching

BACKGROUND

Reaching is a fundamental motor skill often impaired in cerebral palsy (CP). Studies on manual function, intervention, and underlying brain mechanisms largely focus on unilateral CP. This first electroencephalography (EEG) evaluation of reaching exclusively in bilateral CP aims to quantify and relate brain activation patterns to bimanual deficits in this population.

METHODS

A total of 15 children with bilateral CP (13.4 ± 2.9 years) and 13 with typical development (TD: 14.3 ± 2.4 years) performed 45 reaches per hand while recording motion capture and EEG data. The Box and Blocks test was administered bilaterally. Cortical sources were identified using independent component analysis and clustered using k-means. Alpha (8-12 Hz) and beta (13-30 Hz) band event-related desynchronization (ERD) values were compared across groups and hands within clusters, between dominant and non-dominant sensorimotor clusters, and related to reach kinematics and the Box and Block test.

RESULTS

The group with CP demonstrated bimanual motor deficits with slower reaches, lower Box and Blocks scores, and stronger hand preference than in TD. Beta ERD, representing motor execution, was notably higher in the dominant sensorimotor cluster in CP compared to TD. Both groups demonstrated more contralateral than ipsilateral activity in both hands and clusters, with CP showing a less lateralized (more bilateral) alpha response. Higher brain activation was generally related to better function.

CONCLUSION

Bimanual deficits in bilateral CP and related EEG differences warrant more clinical and research attention particularly earlier in life when greater potential for neural and functional recovery exists.

Event-Related Spectral Perturbation, Inter Trial Coherence, and Functional Connectivity in motor execution: A comparative EEG study of old and young subjects

INTRODUCTION

The motor-related bioelectric brain activity of healthy young and old subjects was studied to understand the effect of aging on motor execution. A visually cued finger tapping movement paradigm and high-density EEG were used to examine the time and frequency characteristics.

METHODS

Twenty-two young and 22 healthy elderly adults participated in the study. Repeated trials of left and right index finger movements were recorded with a 128-channel EEG. Event-Related Spectral Perturbation (ERSP), Inter Trial Coherence (ITC), and Functional Connectivity were computed and compared between the age groups.

RESULTS

An age-dependent theta and alpha band ERSP decrease was observed over the frontal-midline area. Decrease of beta band ERSP was found over the ipsilateral central-parietal regions. Significant ITC differences were found in the delta and theta bands between old and young subjects over the contralateral parietal-occipital areas. The spatial extent of increased ITC values was larger in old subjects. The movement execution of older subjects showed higher global efficiency in the delta and theta bands, and higher local efficiency and node strengths in the delta, theta, alpha, and beta bands.

CONCLUSION

As functional compensation of aging, elderly motor networks involve more nonmotor, parietal-occipital, and frontal areas, with higher global and local efficiency, node strength. ERSP and ITC changes seem to be sensitive and complementary biomarkers of age-related motor execution.

Effortful listening produces both enhancement and suppression of alpha in the EEG

Introduction

Adverse listening conditions can drive increased mental effort during listening. Neuromagnetic alpha oscillations (8-13 Hz) may index this listening effort, but inconsistencies regarding the direction of the relationship are abundant. We performed source analyses on high-density EEG data collected during a speech-on-speech listening task to address the possibility that opposing alpha power relationships among alpha producing brain sources drive this inconsistency.

Methods

Listeners (N=20) heard two simultaneously presented sentences of the form: Ready go to now. They either reported the color/number pair of a "Baron" call sign sentence (active: high effort), or ignored the stimuli (passive: low effort). Independent component analysis (ICA) was used to segregate temporally distinct sources in the EEG.

Results

Analysis of independent components (ICs) revealed simultaneous alpha enhancements (e.g., for somatomotor mu ICs) and suppressions (e.g., for left temporal ICs) for different brain sources. The active condition exhibited stronger enhancement for left somatomotor mu rhythm ICs, but stronger suppression for central occipital ICs.

Discussion

This study shows both alpha enhancement and suppression to be associated with increases in listening effort. Literature inconsistencies could partially relate to some source activities overwhelming others in scalp recordings.

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

Feasibility of Using Source-Level Brain Computer Interface for People with Multiple Sclerosis

This work evaluates the feasibility of using a source level Brain-Computer Interface (BCI) for people with Multiple Sclerosis (MS). Data used was previously collected EEG of eight participants (one participant with MS and seven neurotypical participants) who performed imagined movement of the right and left hand. Equivalent current dipole cluster fitting was used to assess related brain activity at the source level and assessed using dipole location and power spectrum analysis. Dipole clusters were resolved within the motor cortices with some notable spatial difference between the MS and control participants. Neural sources that generate motor imagery originated from similar motor areas in the participant with MS compared to the neurotypical participants. Power spectral analysis indicated a reduced level of alpha power in the participant with MS during imagery tasks compared to neurotypical participants. Power in the beta band may be used to distinguish between left and right imagined movement for users with MS in BCI applications.Clinical Relevance- This paper demonstrates the cortical areas activated during imagined BCI-type tasks in a participant with Multiple Sclerosis (MS), and is a proof of concept for translating BCI research to potential users with MS.

Altered Auditory and Visual Evoked Potentials following Single and Repeated Low-Velocity Head Rotations in 4-Week-Old Swine

Auditory and visually evoked potentials (EP) have the ability to monitor cognitive changes after concussion. In the literature, decreases in EP are commonly reported; however, a subset of studies shows increased cortical activity after injury. We studied auditory and visual EP in 4-week-old female Yorkshire piglets (N = 35) divided into anesthetized sham, and animals subject to single (sRNR) and repeated (rRNR) rapid non-impact head rotations (RNR) in the sagittal direction. Two-tone auditory oddball tasks and a simple white-light visual stimulus were evaluated in piglets pre-injury, and at days 1, 4- and 7 post injury using a 32-electrode net. Traditional EP indices (N1, P2 amplitudes and latencies) were extracted, and a piglet model was used to source-localize the data to estimate brain regions related to auditory and visual processing. In comparison to each group's pre-injury baselines, auditory Eps and brain activity (but not visual activity) were decreased in sham. In contrast, sRNR had increases in N1 and P2 amplitudes from both stimuli. The rRNR group had decreased visual N1 amplitudes but faster visual P2 latencies. Auditory and visual EPs have different change trajectories after sRNR and rRNR, suggesting that injury biomechanics are an important factor to delineate neurofunctional deficits after concussion.

Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults

Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by  ∼  6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.

Is Hypnotic Induction Necessary to Experience Hypnosis and Responsible for Changes in Brain Activity?

The relevance of formal hypnotic induction to the experience of trance and its neural correlates is not clear, in that hypnotizability, beliefs and expectation of hypnosis may play a major role. The aim of the study was assessing the EEG brain activity of participants with high (highs) or low hypnotizability scores (lows), aware of their hypnotizability level and informed that the session will include simple relaxation, formal hypnotic induction and neutral hypnosis. A total of 16 highs and 15 lows (according to the Stanford Hypnotic Susceptibility Scale, form A) were enrolled. Their EEGs were recorded during consecutive conditions of open/closed-eyes relaxation, hypnotic induction, neutral hypnosis and post hypnosis not interrupted by interviews. The studied variables were theta, alpha and gamma power spectral density (PSD), and the Determinism (DET) and Entropy (ENT) of the EEG signal Multidimensional Recurrence Plot (mRP). Highs reported significantly greater changes in their state of consciousness than lows across the session. The theta, alpha and gamma PSD did not exhibit condition-related changes in both groups. The Alpha PSD was larger in highs than in lows on midline sites, and the different sides/regions' theta and gamma PSD were observed in the two groups independently from conditions. ENT showed no correlation with hypnotizability, while DET positively correlated with hypnotizability during hypnosis. In conclusion, the relevance of formal hypnotic induction to the experience of trance may be scarce in highs, as they are aware of their hypnotizability scores and expecting hypnosis. Cognitive processing varies throughout the session depending on the hypnotizability level.

Measurement of the N170 during facial neuromuscular electrical stimulation (fNMES)

BACKGROUND

Studies on facial feedback effects typically employ props or posed facial expressions, which often lack temporal precision and muscle specificity.

NEW METHOD

Facial Neuromuscular Electrical Stimulation (fNMES) allows for a controlled influence of contractions of facial muscles, and may be used to advance our understanding of facial feedback effects, especially when combined with Electroencephalography (EEG). However, electrical stimulation introduces significant interference that can mask underlying brain dynamics. Whether established signal processing methods can allow for a reduction of said interference whilst retaining effects of interest, remains unexplored.

RESULTS

We addressed these questions focusing on the classic N170 visual evoked potential, a face-sensitive brain component: 20 participants viewed images of houses, and of sad, happy, and neutral faces. On half of the trials, fNMES was delivered to bilateral lower-face muscles during the presentation of visual stimuli. A larger N170 amplitude was found for faces relative to houses. Interestingly, this was the case both without and during fNMES, regardless of whether the fNMES artefact was removed or not. Moreover, sad facial expressions elicited a larger N170 amplitude relative to neutral facial expressions, both with and without fNMES.

COMPARISON WITH EXISTING METHODS

fNMES offers a more precise way of manipulating proprioceptive feedback from facial muscles, which affords greater diversity in experimental design for studies on facial feedback effects.

CONCLUSIONS

We show that the combining of fNMES and EEG can be achieved and may serve as a powerful means of exploring the impact of controlled proprioceptive inputs on various types of cognitive processing.

Weaker Quadriceps Corticomuscular Coherence in Individuals after ACL Reconstruction during Force Tracing

PURPOSE

This study aimed to compare quadriceps corticomuscular coherence (CMC) and force steadiness between individuals with anterior cruciate ligament reconstruction (ACLR) and uninjured controls during a force tracing task.

METHODS

Individuals with ACLR ( n = 20) and controls ( n = 20) performed a knee extension force-control task at 50% of maximal voluntary effort. Electrocortical activity, electromyographic activity, and torque output were recorded concurrently. CMC in beta (13-30 Hz) and gamma (31-80 Hz) frequency bands was assessed using partial directed coherence between the contralateral motor cortex (e.g., C4-C2-Cz electrodes) and the ipsilateral quadriceps muscles (e.g., left vastus medialis and lateralis). Force steadiness was quantified using root-mean-square error and coefficient of variation. Active motor threshold was determined using transcranial magnetic stimulation. Differences between groups (ACLR vs control) and limbs (involved vs uninvolved) were assessed using peak knee extension strength and active motor threshold as a priori covariates.

RESULTS

Participants with ACLR had lower gamma band connectivity bilaterally when compared with controls (vastus medialis: d = 0.8; vastus lateralis: d = 0.7). Further, the ACLR group demonstrated worse quadriceps force steadiness (root-mean-square error, d = 0.5), lower involved limb quadriceps strength ( d = 1.1), and higher active motor threshold ( d = 1.0) compared with controls.

CONCLUSIONS

Lower quadriceps gamma band CMC in the ACLR group suggests lower cortical drive (e.g., corticomotor decoupling) to the quadriceps compared with matched controls. Further, the ACLR group demonstrated worse quadriceps force steadiness, suggesting impaired ability to modulate quadriceps neuromuscular control. Notably, CMC differences were present only in the gamma frequency band, suggesting impairments may be specific to multisensory integration and force modulation.

Brain activation and single-limb balance following anterior cruciate ligament reconstruction

OBJECTIVE

To compare brain activity between individuals with anterior cruciate ligament reconstruction (ACLR) and controls during balance. To determine the influence of neuromodulatory interventions (external focus of attention [EF] and transcutaneous electrical nerve stimulation [TENS]) on cortical activity and balance performance.

METHODS

Individuals with ACLR (n = 20) and controls (n = 20) performed a single-limb balance task under four conditions: internal focus (IF), object-based-EF, target-based-EF, and TENS. Electroencephalographic signals were decomposed, localized, and clustered to generate power spectral density in theta and alpha-2 frequency bands.

RESULTS

Participants with ACLR had higher motor-planning (d = 0.5), lower sensory (d = 0.6), and lower motor activity (d = 0.4-0.8), while exhibiting faster sway velocity (d = 0.4) than controls across all conditions. Target-based-EF decreased motor-planning (d = 0.1-0.4) and increased visual (d = 0.2), bilateral sensory (d = 0.3-0.4), and bilateral motor (d = 0.4-0.5) activity in both groups compared to all other conditions. Neither EF conditions nor TENS changed balance performance.

CONCLUSIONS

Individuals with ACLR exhibit lower sensory and motor processing, higher motor planning demands, and greater motor inhibition compared to controls, suggesting visual-dependence and less automatic balance control. Target-based-EF resulted in favorable reductions in motor-planning and increases in somatosensory and motor activity, transient effects in line with impairments after ACLR.

SIGNIFICANCE

Sensorimotor neuroplasticity underlies balance deficits in individuals with ACLR. Neuromodulatory interventions such as focus of attention may induce favorable neuroplasticity along with performance benefits.

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

Inhibition of Motor Planning and Response Selection after Anterior Cruciate Ligament Reconstruction

PURPOSE

The purpose of this study is to compare cortical motor planning activity during response selection and motor execution processes between individuals with anterior cruciate ligament reconstruction (ACLR) and uninjured controls during a reaction time and response selection task.

METHODS

Individuals with ACLR ( n = 20) and controls ( n = 20) performed a lateralized choice reaction time (e.g., Go/NoGo) task. Electrocortical activity and reaction time were recorded concurrently using electroencephalography and inertial measurement units. Separate stimulus locked and response-locked event-related potentials were computed for each limb. The lateralized readiness potential (LRP) was computed as the interhemispheric differences between waveforms and the mean LRP area and onset latency were recorded. Active motor threshold was determined using transcranial magnetic stimulation. Differences between groups (ACLR vs control) and limbs (involved vs uninvolved) and the associations between LRP characteristics and response performance (number of errors) were assessed.

RESULTS

Participants with ACLR have had smaller LRP area during periods of response selection ( P = 0.043, d = 0.4) and motor execution ( P = 0.015, d = 0.5) and committed more errors in both Go ( P < 0.001, d = 0.8) and NoGo ( P = 0.032, d = 0.5) response conditions. There were no differences in latency of response selection or motor execution. Participants with ACLR had higher active motor thresholds ( P < 0.001, d = 1.3) than controls, which was weakly associated with smaller LRP areas ( r = 0.32-0.42, P < 0.05).

CONCLUSIONS

The ACLR group demonstrated greater motor planning and response inhibition during a choice reaction time task. More errant performance also suggests poorer decision making in the presence of a "speed-accuracy" trade-off. Key features of the sample, including lower corticospinal excitability, lend support to an interpretation of widespread cortical inhibition contributing to impairments in response selection and motor execution.

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.

TMS combined with EEG: Recommendations and open issues for data collection and analysis

Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS-EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS-EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS-EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS-EEG covered all aspects that should be considered in TMS-EEG experiments, providing methodological recommendations (when possible) for effective TMS-EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS-EEG methodology and thus may promote standardization of experimental and computational procedures across groups.

iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.

Application of a Hermite-based measure of non-Gaussianity to normality tests and independent component analysis

In the analysis of neural data, measures of non-Gaussianity are generally applied in two ways: as tests of normality for validating model assumptions and as Independent Component Analysis (ICA) contrast functions for separating non-Gaussian signals. Consequently, there is a wide range of methods for both applications, but they all have trade-offs. We propose a new strategy that, in contrast to previous methods, directly approximates the shape of a distribution via Hermite functions. Applicability as a normality test was evaluated via its sensitivity to non-Gaussianity for three families of distributions that deviate from a Gaussian distribution in different ways (modes, tails, and asymmetry). Applicability as an ICA contrast function was evaluated through its ability to extract non-Gaussian signals in simple multi-dimensional distributions, and to remove artifacts from simulated electroencephalographic datasets. The measure has advantages as a normality test and, for ICA, for heavy-tailed and asymmetric distributions with small sample sizes. For other distributions and large datasets, it performs comparably to existing methods. Compared to standard normality tests, the new method performs better for certain types of distributions. Compared to contrast functions of a standard ICA package, the new method has advantages but its utility for ICA is more limited. This highlights that even though both applications-normality tests and ICA-require a measure of deviation from normality, strategies that are advantageous in one application may not be advantageous in the other. Here, the new method has broad merits as a normality test but only limited advantages for ICA.

Neuroanatomical Correlates of the Late Positive Potential in Youth with Pediatric Bipolar Disorder

BACKGROUND

The late positive potential (LPP) could be a marker of emotion dysregulation in youth with pediatric bipolar disorder (PBD). However, the neuroanatomical correlates of the LPP are still not clarified.

OBJECTIVE

To provide cortical and deep gray matter correlates of the LPP in youth, specifically, youth with PBD.

METHODS

Twenty-four 7 to 17 years-old children with PBD and 28 healthy controls (HC) underwent cortical thickness and deep gray matter volumes measurements through magnetic resonance imaging and LPP measurement elicited by passively viewing emotional faces through electroencephalography. T-tests compared group differences in LPP, cortical thickness, and deep gray matter volumes. Linear regressions tested the relationship between LPP amplitude and cortical thickness/deep gray matter volumes.

RESULTS

PBD had a more pronounced LPP amplitude for happy faces and a thinner cortex in prefrontal areas than HC. While considering both groups, a higher LPP amplitude was associated with a thicker cortex across occipital and frontal lobes, and with a smaller right globus pallidus volume. In addition, a higher LPP amplitude for happy faces was associated with smaller left caudate and left globus pallidus volumes across both groups. Finally, the LPP amplitude correlated negatively with right precentral gyrus thickness across youth with PBD, but positively across HC.

CONCLUSION

Neural correlates of LPP in youth included fronto-occipital areas that have been associated also with emotion processing and control. The opposite relationship between BPD and HC of LPP amplitude and right precentral gyrus thickness might explain the inefficacy of the emotional control system in PBD.

Abnormal dynamics of resting-state functional activity and couplings in postpartum depression with and without anxiety

Postpartum depression (PPD) and PPD comorbid with anxiety (PPD-A) are highly prevalent and severe mental health problems in postnatal women. PPD and PPD-A share similar pathopsychological features, leading to ongoing debates regarding the diagnostic and neurobiological uniqueness. This paper aims to delineate common and disorder-specific neural underpinnings and potential treatment targets for PPD and PPD-A by characterizing functional dynamics with resting-state functional magnetic resonance imaging in 138 participants (45 first-episode, treatment-naïve PPD; 31 PDD-A patients; and 62 healthy postnatal women [HPW]). PPD-A group showed specifically increased dynamic amplitude of low-frequency fluctuation in the subgenual anterior cingulate cortex (sgACC) and increased dynamic functional connectivity (dFC) between the sgACC and superior temporal sulcus. PPD group exhibited specifically increased static FC (sFC) between the sgACC and ventral anterior insula. Common disrupted sFC between the sgACC and middle temporal gyrus was found in both PPD and PPD-A patients. Interestingly, dynamic changes in dFC between the sgACC and superior temporal gyrus could differentiate PPD, PPD-A, and HPW. Our study presents initial evidence on specifically abnormal functional dynamics of limbic, emotion regulation, and social cognition systems in patients with PDD and PPD-A, which may facilitate understanding neurophysiological mechanisms, diagnosis, and treatment for PPD and PPD-A.

Facilitated extinction of conditioned fear responses by delta 9-tetrahyrdrocannabidol in humans: a pilot study

OBJECTIVE

We sought to determine whether acute delta 9-tetrahyrdrocannabidol (THC) administration would facilitate fear extinction in young occasional cannabis users, given that animal models indicate THC facilitates extinction learning, and recent studies indicate THC administration may also enhance threat memory extinction in humans.

METHODS

On each of the 2 days, 24+ hour THC-deprived participants were conditioned to fear visual stimuli in a delay conditioning and extinction paradigm. Both CS+ and CS- were faces of negative emotional valence, with the CS+ paired with mild electric shock. Throughout both conditioning and extinction paradigms, EEG was measured to quantify event-related potentials for these learning processes. Following conditioning, individuals, in a randomized and counter-balanced order, smoked either an active THC cigarette (26.25 mg/2.7% THC) or a placebo marijuana cigarette (0.002% THC) on 1 day and the opposite cigarette on the second day. After smoking, CS+ and CS- were presented without shock, resulting in extinction of conditioned fear.

RESULTS

Relative to placebo, THC facilitated extinction of the conditioned response to the CS+, as reflected by reductions in late positive potential amplitude during extinction learning.

CONCLUSIONS

The results indicate that acute THC administration may facilitate extinction of the conditioned fear response in humans.

Unique estimation in EEG analysis by the ordering ICA

Independent Component Analysis (ICA) is a method for solving blind source separation problems. Because ICA only needs weak assumptions to estimate the unknown sources from only the observed signals, it is suitable for Electroencephalography (EEG) analysis. A serious disadvantage of the traditional ICA algorithms is that their results often fluctuate and do not converge to the unique and globally optimal solution at each run. It is because there are many local optima and permutation ambiguities. We have recently proposed a new ICA algorithm named the ordering ICA, a simple extension of Fast ICA. The ordering ICA is theoretically guaranteed to extract the independent components in the unique order and avoids the local optima in practice. This paper investigated the usefulness of the ordering ICA in EEG analysis. Experiments showed that the ordering ICA could give unique solutions for the signals with large non-Gaussianity, and the ease of parallelization could reduce computation time.

Visual working memory recruits two functionally distinct alpha rhythms in posterior cortex

Oscillatory activity in the human brain is dominated by posterior alpha oscillations (8-14 Hz), which have been shown to be functionally relevant in a wide variety of cognitive tasks. Although posterior alpha oscillations are commonly considered a single oscillator anchored at an individual alpha frequency (IAF; ∼10 Hz), previous work suggests that IAF reflects a spatial mixture of different brain rhythms. In this study, we assess whether Independent Component Analysis (ICA) can disentangle functionally distinct posterior alpha rhythms in the context of visual short-term memory retention. Magnetoencephalography (MEG) was recorded in 33 subjects while performing a visual working memory task. Group analysis at sensor level suggested the existence of a single posterior alpha oscillator that increases in power and decreases in frequency during memory retention. Conversely, single-subject analysis of independent components revealed the existence of two dissociable alpha rhythms: one that increases in power during memory retention (Alpha1) and another one that decreases in power (Alpha2). Alpha1 and Alpha2 rhythms were differentially modulated by the presence of visual distractors (Alpha1 increased in power while Alpha2 decreased) and had an opposite relationship with accuracy (positive for Alpha1 and negative for Alpha2). In addition, Alpha1 rhythms showed a lower peak frequency, a narrower peak width, a greater relative peak amplitude and a more central source than Alpha2 rhythms. Together, our results demonstrate that modulations in posterior alpha oscillations during short-term memory retention reflect the dynamics of at least two distinct brain rhythms with different functions and spatiospectral characteristics.Significance statementAlpha oscillations are the most prominent feature of the human brain's electrical activity, and consist of rhythmic neuronal activity in posterior parts of the cortex. Alpha is usually considered a single brain rhythm that changes in power and frequency to support cognitive operations. We here show that posterior alpha entails at least two dissociable rhythms with distinct functions and characteristics. These findings could solve previous inconsistencies in the literature regarding the direction of task-related alpha power/frequency modulations and their relation to cognitive performance. In addition, the existence of two distinct posterior alpha rhythms could have important consequences for the design of neurostimulation protocols aimed at modulating alpha oscillations and subsequently cognition.

Neurofeedback for the Education of Children with ADHD and Specific Learning Disorders: A Review

Neurofeedback (NF) is a type of biofeedback in which an individual’s brain activity is measured and presented to them to support self-regulation of ongoing brain oscillations and achieve specific behavioral and neurophysiological outcomes. NF training induces changes in neurophysiological circuits that are associated with behavioral changes. Recent evidence suggests that the NF technique can be used to train electrical brain activity and facilitate learning among children with learning disorders. Toward this aim, this review first presents a generalized model for NF systems, and then studies involving NF training for children with disorders such as dyslexia, attention-deficit/hyperactivity disorder (ADHD), and other specific learning disorders such as dyscalculia and dysgraphia are reviewed. The discussion elaborates on the potential for translational applications of NF in educational and learning settings with details. This review also addresses some issues concerning the role of NF in education, and it concludes with some solutions and future directions. In order to provide the best learning environment for children with ADHD and other learning disorders, it is critical to better understand the role of NF in educational settings. The review provides the potential challenges of the current systems to aid in highlighting the issues undermining the efficacy of current systems and identifying solutions to address them. The review focuses on the use of NF technology in education for the development of adaptive teaching methods and the best learning environment for children with learning disabilities.

Neural signatures of default mode network in major depression disorder after electroconvulsive therapy

Functional abnormalities of default mode network (DMN) have been well documented in major depressive disorder (MDD). However, the association of DMN functional reorganization with antidepressant treatment and gene expression is unclear. Moreover, whether the functional interactions of DMN could predict treatment efficacy is also unknown. Here, we investigated the link of treatment response with functional alterations of DMN and gene expression with a comparably large sample including 46 individuals with MDD before and after electroconvulsive therapy (ECT) and 46 age- and sex-matched healthy controls. Static and dynamic functional connectivity (dFC) analyses showed increased intrinsic/static but decreased dynamic functional couplings of inter- and intra-subsystems and between nodes of DMN. The changes of static functional connections of DMN were spatially correlated with brain gene expression profiles. Moreover, static and dFC of the DMN before treatment as features could predict depressive symptom improvement following ECT. Taken together, these results shed light on the underlying neural and genetic basis of antidepressant effect of ECT and the intrinsic functional connectivity of DMN have the potential to serve as prognostic biomarkers to guide accurate personalized treatment.

Autistic traits heighten sensitivity to rejection-induced social pain

Autistic traits-subclinical forms of characteristics associated with autism spectrum disorders-are associated with poor social interactions and high risks for mental health disorders. We hypothesized that altered sensitivity to social rejection is an important contributor to psychological distress observed among individuals with high autistic traits. Experiment 1 adopted a social-judgment task and compared behavioral and neural activity in response to social rejection between participants exhibiting either high or low autistic traits (HAT and LAT, respectively). Rejection-induced hurt feelings, P3 amplitudes, and θ-oscillation magnitudes were greater in the HAT group than in the LAT group. Mediation analysis indicated that autistic traits heighten rejection-induced social pain through increasing frontal-midline θ-oscillations. Responses to nonsocial feedback in the age-judgment task were comparable, confirming that the between-group differences were specific to social negative feedback. Experiment 2 assessed the association between autistic traits, rejection sensitivity, and psychological distress among randomly recruited participants. Results showed that autistic traits affected depressive/anxious symptomatology partially through heightened rejection sensitivity. Therefore, autistic traits heighten sensitivity to rejection-induced social pain that leads to psychological distress. This finding will help facilitate the development of strategies for coping with social pain and improving mental health for individuals with high autistic traits.