ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

Neural signatures of recollection are sensitive to memory quality and specific event features

Episodic memories reflect a bound representation of multimodal features that can be recollected with varying levels of precision. Recent fMRI investigations have demonstrated that the precision and content of information retrieved from memory engage a network of posterior medial temporal and parietal regions co-activated with the hippocampus. Yet, comparatively little is known about how memory content and precision affect common neural signatures of memory captured by electroencephalography (EEG), where recollection has been associated with changes in event-related potential (ERP) and oscillatory measures of neural activity. Here, we used a multi-feature paradigm previously reported in Cooper & Ritchey (2019) with continuous measures of memory, in conjunction with scalp EEG, to characterize the content and quality of information that drives ERP and oscillatory markers of episodic memory. A common signature of memory retrieval in left posterior regions, called the late positive component (LPC), was sensitive to overall memory quality and also to precision of recollection for spatial features. Analysis of oscillatory markers during recollection revealed that alpha/beta desynchronization was modulated by overall memory quality and also by individual features in memory. Importantly, we found evidence of a relationship between these two neural markers of memory retrieval, suggesting that they may represent complementary aspects of the recollection experience. These findings demonstrate how time-sensitive and dynamic processes identified with EEG correspond to overall episodic recollection, and also to the retrieval of precise features in memory.

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals

Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.

Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG)

Objective. Neurological disorders affecting speech production adversely impact quality of life for over 7 million individuals in the US. Traditional speech interfaces like eye-tracking devices and P300 spellers are slow and unnatural for these patients. An alternative solution, speech brain-computer interfaces (BCIs), directly decodes speech characteristics, offering a more natural communication mechanism. This research explores the feasibility of decoding speech features using non-invasive EEG.Approach. Nine neurologically intact participants were equipped with a 63-channel EEG system with additional sensors to eliminate eye artifacts. Participants read aloud sentences selected for phonetic similarity to the English language. Deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks with and without attention modules, were optimized with a focus on minimizing trainable parameters and utilizing small input window sizes for real-time application. These models were employed for discrete and continuous speech decoding tasks.Main results. Statistically significant participant-independent decoding performance was achieved for discrete classes and continuous characteristics of the produced audio signal. A frequency sub-band analysis highlighted the significance of certain frequency bands (delta, theta, gamma) for decoding performance, and a perturbation analysis was used to identify crucial channels. Assessed channel selection methods did not significantly improve performance, suggesting a distributed representation of speech information encoded in the EEG signals. Leave-One-Out training demonstrated the feasibility of utilizing common speech neural correlates, reducing data collection requirements from individual participants.Significance. These findings contribute significantly to the development of EEG-enabled speech synthesis by demonstrating the feasibility of decoding both discrete and continuous speech features from EEG signals, even in the presence of EMG artifacts. By addressing the challenges of EMG interference and optimizing deep learning models for speech decoding, this study lays a strong foundation for EEG-based speech BCIs.

Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection

In an attempt to create a more familiar brain-machine interaction for biometric authentication applications, we investigated the efficiency of using the users' personal hobbies, interests, and memory collections. This approach creates a unique and pleasant experience that can be later utilized within an authentication protocol. This paper presents a new EEG dataset recorded while subjects watch images of popular hobbies, pictures with no point of interest and images with great personal significance. In addition, we propose several applications that can be tackled with our newly collected dataset. Namely, our study showcases 4 types of applications and we obtain state-of-the-art level results for all of them. The tackled tasks are: emotion classification, category classification, authorization process, and person identification. Our experiments show great potential for using EEG response to hobby visualization for people authentication. In our study, we show preliminary results for using predilection for personal hobbies, as measured by EEG, for identifying people. Also, we propose a novel authorization process paradigm using electroencephalograms. Code and dataset are available here.

Beyond the neural underpinnings of action emulation in expert athletes: An EEG study

Athletes specializing in sports demanding rapid predictions and hand-eye coordination are highly trained in predicting the consequences of motor commands. This can be framed as highly efficient action emulation, but the neural underpinnings of this remain elusive. We examined the neural processes linked to the training effect of athletes (4000 h of training) by employing a continuous pursuit tracking task and EEG data. We manipulated feedback availability by intermittently occluding the cursor. As a performance measure, we used the distance between cursor and target (position error), the angle between the cursor and target movement direction (direction error) and the magnitude of cursor acceleration (acceleration error) to quantify movement strategy. In EEG data, we investigated beta, alpha, and theta frequency band oscillations. Athletes' position error is lower than non-athletes' when there is no feedback about the cursor location, but direction error is not. We found no quantitative power differences in the investigated frequency bands, but evidence that athletes and non-athletes accomplish action emulation through different functional neuroanatomical structures, especially when alpha and beta band activity is concerned. We surmise that non-athletes seemed to rely on top-down inhibitory control to predict guesses on cursor trajectories in the absence of cursor position feedback. In contrast, athletes might benefit from enhanced inhibitory gating mechanisms in the ventral stream and the integration of sensory and motor processes in the insular cortex, which could provide them with processing advantages in computing forward models. We further reflect that this advantage might be supported by alpha band activity in athletes' motor cortex, suggesting less inhibitory gating and a higher likelihood of executing integrated sensorimotor programs. We posit that current framings of neuroanatomical structures and neurophysiological processes in the action emulation framework must be revised to better capture superior motor performance.

Heartbeat evoked potentials reflect interoceptive awareness during an emotional situation

Interoception, the ability to perceive internal bodily signals, involves various mental processes. This study investigated moments when individuals experienced a change in their interoceptive state. Interoception affects the subjective feelings of emotions. However, whether changes in interoceptive processing occur when visceral changes are perceived as interoceptive sensations remains unclear. This study examined the psychophysiological markers of perceptual changes in the interoceptive state using heartbeat evoked potentials (HEP). Emotional photos were viewed by participants who were asked whether they experienced changes in their interoceptive state. The results showed that the HEP amplitude increased when participants had subjective awareness of changes in their interoceptive state. Notably, the HEP amplitude did not correlate with individual differences in interoceptive accuracy measured by the heartbeat counting task. When individuals perceive changes in their interoceptive state, this perception is reflected in the HEP amplitude. Multiple aspects of interoception are related to the emotional recognition and conscious attention to changing bodily states.

Bayesian prior uncertainty and surprisal elicit distinct neural patterns during sound localization in dynamic environments

Estimating the location of a stimulus is a key function in sensory processing, and widely considered to result from the integration of prior information and sensory input according to Bayesian principles. A deviation of sensory input from the prior elicits surprisal, depending on the uncertainty of the prior. While this mechanism is increasingly understood in the visual domain, much less is known about its implementation in audition, especially regarding spatial localization. Here, we combined human EEG with computational modeling to study auditory spatial inference in a noisy, volatile environment and analyzed behavioral and neural patterns associated with prior uncertainty and surprisal. First, our results demonstrate that participants indeed used prior information during periods of stable environmental statistics, but showed evidence of surprisal and discarded prior information following environmental changes. Second, we observed distinct EEG activity patterns associated with prior uncertainty and surprisal in both the time- and time-frequency domain, which are in line with previous studies using visual tasks. Third, these EEG activity patterns were predictive of our participants' sound localization error, response uncertainty, and prior bias on a trial-by-trial basis. In summary, our work provides novel behavioral and neural evidence for Bayesian inference during dynamic auditory localization.

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications

The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if trained with bad processed data. Preprocessing is crucial for EEG data analysis, yet there is no consensus on the optimal strategies in deep learning scenarios, leading to uncertainty about the extent of preprocessing required for optimal results. This study is the first to thoroughly investigate the effects of EEG preprocessing in deep learning applications, drafting guidelines for future research. It evaluates the effects of varying preprocessing levels, from raw and minimally filtered data to complex pipelines with automated artifact removal algorithms. Six classification tasks (eye blinking, motor imagery, Parkinson's, Alzheimer's disease, sleep deprivation, and first episode psychosis) and four established EEG architectures were considered for the evaluation. The analysis of 4800 trained models revealed statistical differences between preprocessing pipelines at the intra-task level for each model and at the inter-task level for the largest model. Models trained on raw data consistently performed poorly, always ranking last in average scores. In addition, models seem to benefit more from minimal pipelines without artifact handling methods. These findings suggest that EEG artifacts may affect the performance and generalizability of deep neural networks.

Reevaluating the neural noise in dyslexia using biomarkers from electroencephalography and high-resolution magnetic resonance spectroscopy

The neural noise hypothesis of dyslexia posits an imbalance between excitatory and inhibitory (E/I) brain activity as an underlying mechanism of reading difficulties. This study provides the first direct test of this hypothesis using both electroencephalography (EEG) power spectrum measures in 120 Polish adolescents and young adults (60 with dyslexia, 60 controls) and glutamate (Glu) and gamma-aminobutyric acid (GABA) concentrations from magnetic resonance spectroscopy (MRS) at 7T MRI scanner in half of the sample. Our results, supported by Bayesian statistics, show no evidence of E/I balance differences between groups, challenging the hypothesis that cortical hyperexcitability underlies dyslexia. These findings suggest that alternative mechanisms must be explored and highlight the need for further research into the E/I balance and its role in neurodevelopmental disorders.

Effect of Different Frequencies of Transcutaneous Electrical Acupoint Stimulation (TEAS) on EEG Source Localization in Healthy Volunteers: A Semi-Randomized, Placebo-Controlled, Crossover Study

Background/Objectives: Transcutaneous electrical acupoint stimulation (TEAS), also known as transcutaneous electroacupuncture stimulation, delivers electrical pulses to the skin over acupuncture points ("acupoints") via surface electrodes. Electroencephalography (EEG) is an important tool for assessing the changes in the central nervous system (CNS) that may result from applying different TEAS frequencies peripherally-i.e., acting via the peripheral nervous system (PNS)-and determining how these influence cerebral activity and neural plasticity. Methods: A total of 48 healthy volunteers were allocated in a semi-randomized crossover study to receive four different TEAS frequencies: 2.5 pulses per second (pps); 10 pps; 80 pps; and sham (160 pps at a low, clinically ineffective amplitude). TEAS was applied for 20 min to each hand at the acupuncture point Hegu (LI4). The EEG was recorded during an initial 5 min baseline recording, then during TEAS application, and after stimulation for a further 15 min, separated into three periods of 5 min (initial, intermediate, and final) in order to assess post-stimulation changes. Source localization analysis was conducted for the traditional five EEG frequency bands: delta (0.1-3.9 Hz), theta (4-7.9 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (30.1-45 Hz). Results: Within-group source localization analyses of EEG data showed that during the initial 5 min post-stimulation, theta oscillations in the 2.5 pps TEAS group increased over the parahippocampal gyrus (t = 4.42, p < 0.01). The 10 pps TEAS group exhibited decreased alpha rhythms over the inferior parietal gyrus (t = -4.20, p < 0.05), whereas the sham (160 pps) TEAS group showed decreased delta rhythms over the postcentral gyrus (t = -3.97, p < 0.05). During the intermediate 5 min post-stimulation, the increased theta activity over the left parahippocampal gyrus (BA27) remained in the 2.5 pps TEAS group (t = 3.97, p < 0.05). However, diminished alpha rhythms were observed in the 10 pps TEAS group over the postcentral gyrus (t = -4.20, p < 0.01), as well as in the delta rhythms in the sham (160 pps) TEAS group in the same area (t = -4.35, p < 0.01). In the final 5 min post-stimulation, reduced alpha rhythms were exhibited over the insula in the 10 pps TEAS group (t = -4.07, p < 0.05). Interaction effects of condition by group demonstrate decreased alpha rhythms in the 10 pps TEAS group over the supramarginal gyrus during the initial 5 min post-stimulation (t = -4.31, p < 0.05), and decreased delta rhythms over the insula in the sham TEAS group during the final 5 min post-stimulation (t = -4.42, p < 0.01). Conclusions: This study revealed that low TEAS frequencies of 2.5 pps and 10 pps modulate theta and alpha oscillations over the brain areas related to emotional and attentional processes driven by external stimuli, as well as neural synchronization of delta rhythms in the sham group in brain areas related to stimulus expectation at baseline. It is hoped that these findings will stimulate further research in order to evaluate such TEAS modulation effects in clinical patients.

Cenobamate modulates EEG cortical activity and connectivity in individuals with drug-resistant epilepsy: a pharmaco-EEG study

Objective

Quantitative electroencephalography (qEEG) metrics are demonstrated to correlate with and predict clinical response in individuals with epilepsy. Cenobamate is an effective anti-seizure medication recently approved as an add-on therapy for individuals with epilepsy, but its effects on qEEG are unknown. We aimed to evaluate the modulation of qEEG metrics induced by cenobamate and its relationship with clinical response.

Methods

We performed a prospective study with a cohort of 18 individuals with epilepsy (8 women, 47 ± 16 years old) and 25 healthy subjects (HS). They underwent a 19-channel EEG before and 6 months after cenobamate administration. Power spectral density (PSD) and phase locking value (PLV) for delta, theta, alpha, beta, and gamma frequency bands were calculated. Correlation analysis and analysis of covariance exhibited significant cenobamate-induced changes in qEEG and their relationship with seizure frequency changes. A regression analysis was performed to evaluate the association with clinical responders.

Results

A total of 11 out of 16 individuals with epilepsy (69%, with 2 dropping out) were cenobamate responders (≥50% seizure frequency reduction). Cenobamate did not modify any PSD parameter but induced significant changes in PLV levels (p < 0.01). A decrease in PLV correlated with seizure reduction (p < 0.03). Regression analysis showed a strong association between PLV modulation and cenobamate responsiveness (a sensitivity of 0.75, a specificity of 0.84, and an accuracy of 0.81).

Conclusion

Cenobamate induces an EEG connectivity modulation that is highly associated with cenobamate clinical response.

Significance

Connectivity analysis of pharmaco-EEG can provide new hints toward the development of innovative biomarkers and precision medicine in individuals with epilepsy.

Dynamic modulations of effective brain connectivity associated with postural instability during multi-joint compound movement on compliant surface

Random fluctuations in somatosensory signals affect the ability of effectively coordinating multimodal information pertaining to the postural state during movement. Therefore, this study aimed to investigate the impact of a compliant surface on cortico-cortical causal information flow during multi-joint compound movements. Fifteen healthy adults (7 female / 8 male, 25.9 ± 4.0 years) performed 5 × 20 repetitions of bodyweight squats on firm and compliant surface. Motor behavior was quantified by center of pressure (CoP) displacements, hip movement and the root mean square of the rectus femoris activity. Using source space analysis, renormalized partial directed coherence (rPDC) computed subject-level multivariate effective brain connectivity of sensorimotor nodes. Bootstrap statistics revealed significantly decreased medio-lateral CoP displacement (p < 0.001), significantly increased velocity of medio-lateral hip motion (p < 0.001) as well as significantly lower rectus femoris activity (p < 0.01) in the compliant surface condition. On the cortical level, rPDC showed significantly modulated information flow in theta and beta frequencies for fronto-parietal edges (p < 0.01) only during the concentric phase of the movement. The compliant surface led to increased difficulties controlling hip but not center of pressure motion in the medio-lateral plane. Moreover, a decreased activation of the prime movers accompanied by modulations of effective brain connectivity among fronto-central nodes may point to altered demands on sensorimotor information processing in presence of sensory noise when performing bodyweight squats on compliant surface. Further studies are needed to evaluate a potential benefit for athletic and clinical populations.

Functional Connectivity in Chronic Schizophrenia: An EEG Resting-State Study with Corrected Imaginary Phase-Locking

Schizophrenia is a complex disorder characterized by altered brain functional connectivity, detectable during both task and resting state conditions using different neuroimaging methods. To this day, electroencephalography (EEG) studies have reported inconsistent results, showing both hyper- and hypo-connectivity with diverse topographical distributions. Interpretation of these findings is complicated by volume-conduction effects, where local brain activity fluctuations project simultaneously to distant scalp regions (zero-phase lag), inducing spurious inter-electrode correlations.

AIM

In the present study, we explored the network dynamics of schizophrenia using a novel functional connectivity metric-corrected imaginary phase locking value (ciPLV)-which is insensitive to changes in amplitude as well as interactions at zero-phase lag. This method, which is less prone to volume conduction effects, provides a more reliable estimate of sensor-space functional network connectivity in schizophrenia.

METHODS

We employed a cross-sectional design, utilizing resting state EEG recordings from two adult groups: individuals diagnosed with chronic schizophrenia (n = 30) and a control group of healthy participants (n = 30), all aged between 18 and 55 years old.

RESULTS

Our observations revealed that schizophrenia is characterized by a prevalence of excess theta (4-8 Hz) power localized to centroparietal electrodes. This was accompanied by significant alterations in inter- and intra-hemispheric functional network connectivity patterns, mainly between frontotemporal regions within the theta band and frontoparietal regions within beta/gamma bands.

CONCLUSIONS

Our findings suggest that patients with schizophrenia demonstrate long-range electrophysiological connectivity abnormalities that are independent of spectral power (i.e., volume conduction). Overall, distinct hemispheric differences were present in frontotemporo-parietal networks in theta and beta/gamma bands. While preliminary, these alterations could be promising new candidate biomarkers of chronic schizophrenia.

Changes in EEG Microstate Dynamics and Cognition Post-Chemotherapy in People With Breast Cancer

OBJECTIVE

Chemotherapy-related cognitive changes following breast cancer are commonly reported; however, changes in brain dynamics of large-scale neural networks remain unclear. Using data from the Aerobic exercise and CogniTIVe functioning in women with breAsT cancEr (ACTIVATE) trial, we conducted exploratory analyses to compare self-reported and objective measures of cognition and applied microstate analysis to resting state (RS) electroencephalography (EEG) data of women with breast cancer before and following chemotherapy treatment.

METHODS

Data from eight female participants between the ages of 30 and 52 (mean age = 44.8 years, SD = 7.3 years) were analyzed. Cognitive function was assessed using the PROMIS (Patient-Reported Outcomes Measurement Information System) and the Trail Making Test (TMT). Five minutes of RS eyes-closed EEG data were also collected. Seven EEG microstates were extracted, and mean microstate duration and occurrence were computed.

RESULTS

Following chemotherapy, there was a significant decrease in the PROMIS score (p = 0.003, d = 1.601), but no significant difference in the TMT score. Overall, microstate durations were significantly longer (p < 0.001, d = 2.837) and less evenly distributed following chemotherapy. The mean duration of microstate D (involved in attention/executive functions) significantly increased following chemotherapy (p = 0.007, d = 1.339). Comparing behavioral and microstate measures that exhibited a large effect size, no significant correlations were observed either before or after chemotherapy.

CONCLUSIONS

We observed self-reported cognitive impairment and disturbed functional dynamics in the RS brain following chemotherapy. This exploratory study provides new evidence using a within-cohort design showing that changes occur in large scale brain dynamics related to the cognitive effects of chemotherapy.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03277898.

The interplay between Hebbian and homeostatic plasticity in the adult visual cortex

Homeostatic and Hebbian plasticity co-operate during the critical period, refining neuronal circuits; however, the interaction between these two forms of plasticity is still unclear, especially in adulthood. Here, we directly investigate this issue in adult humans using two consolidated paradigms to elicit each form of plasticity in the visual cortex: the long-term potentiation-like change of the visual evoked potential (VEP) induced by high-frequency stimulation (HFS) and the shift of ocular dominance induced by short-term monocular deprivation (MD). We tested homeostatic and Hebbian plasticity independently, then explored how they interacted by inducing them simultaneously in a group of adult healthy volunteers. We successfully induced both forms of plasticity: 60 min of MD induced a reliable change in ocular dominance and HFS reliably modulated the amplitude of the P1 component of the VEP. Importantly, we found that, across participants, homeostatic and Hebbian plasticity were negatively correlated, indicating related neural mechanisms, potentially linked to intracortical excitation/inhibition balance. On the other hand, we did not find an interaction when the two forms of plasticity were induced simultaneously. Our results indicate a largely preserved plastic potential in the visual cortex of the adult brain, for both short-term homeostatic and Hebbian plasticity. Crucially, we show for the first time a direct relationship between these two forms of plasticity in the adult human visual cortex, which could inform future research and treatment protocols for neurological diseases. KEY POINTS: Homeostatic and Hebbian plasticity co-operate during the critical period to refine neuronal circuits in the visual cortex. The interaction between these two forms of plasticity is still unknown, especially after the closure of the critical periods and in humans. We directly investigate the interplay between Hebbian and homeostatic visual plasticity in adult humans using non-invasive paradigms. We found a negative correlation between these forms of plasticity showing for the first time a direct relationship between Hebbian and homeostatic plasticity. Our results could inform future research and treatment protocols for neurological diseases.

Differences in Consummatory but Not Anticipatory Reward Processing Predict Depressive Symptoms in Young Adult Women

Depression has been postulated to relate to alterations in both anticipatory (i.e., motivation) and consummatory (i.e., hedonic pleasure) stages of reward processing. However, few studies have concurrently examined the various processes of these stages. Furthermore, little attention has been paid to whether these associations are sex-specific, despite increasing evidence of the sex specificity of neural markers of internalizing disorders. The current study examines event-related potentials (ERPs) of reward processing recorded during a monetary incentive delay task among a community sample of n = 309 emerging adults in relation to self-reported symptoms of depression. Regression modeling indicated that greater depressive symptom scores were associated with reduced responsivity to reward feedback and increased responsivity to non-reward feedback (as indexed by the Feedback-P3) but only for participants who were identified as female at birth. Individual differences in anticipatory processes (as indexed by both the Cue-P3 and CNV) were not associated with depressive symptoms for either sex. Results of these models suggest that depressive symptoms appear to be associated with consummatory reward processing for young women. It is possible that other dimensions of negative affect could be more poignant for male participants or may provide an additional description of the relationship between reward processing and depressive symptoms.

Let them eat ceke: An electrophysiological study of form-based prediction in rich naturalistic contexts

It is well-established that people make predictions during language comprehension--the nature and specificity of these predictions, however, remain unclear. For example, do comprehenders routinely make predictions about which words (and phonological forms) might come next in a conversation, or do they simply make broad predictions about the gist of the unfolding context? Prior EEG studies using tightly controlled experimental designs have shown that form-based prediction can occur during comprehension, as N400s to unexpected words are reduced when they resemble the form of a predicted word (e.g., ceke when expecting cake). One limitation, however, is that these studies often create environments that are optimal for eliciting form-based prediction (e.g., highly constraining sentences, slower-than-natural rates of presentation). Thus, questions remain about whether form-based prediction can occur in settings that more closely resemble everyday comprehension. To address this, the present study explores form-based prediction during naturalistic spoken language comprehension. English-speaking adults listened to a story in which some of the words had been altered. Specifically, we experimentally manipulated whether participants heard the original word from the story (cake), a form-similar nonword (ceke), or a less-similar nonword (vake). Half of the target words were predictable given their context, and the other half were unpredictable. Consistent with the prior work, we found reduced N400s for form-similar nonwords (ceke) relative to less-similar nonwords (vake)-but only in predictable contexts. This study demonstrates that form-based prediction can emerge in naturalistic contexts, and therefore, it is likely to be a common aspect of language comprehension in the wild. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Hyposensitivity to losses under risk but hypersensitivity to gains under ambiguity during feedback evaluation

Uncertainty is ubiquitous in human life and can be fractioned into risk (known probability distribution) and ambiguity (unknown probability distribution), each with distinct functional correlates. This event-related potential study examined how contextual valence influences the dissociation between risk and ambiguity during feedback evaluation through the lens of neural dynamics. We manipulated contextual valence as a gain versus a loss context. In the gain context, decisions resulted in either gains or nongains, while in the loss context, decisions led to losses or nonlosses. We recorded EEG from 40 participants while they completed a wheel-of-fortune task under conditions of risk and ambiguity in both contexts. We observed a stronger valence effect on the P3a in the loss context under risk, but a stronger valence effect on the P3b in the gain context under ambiguity. Further comparisons revealed that feedback evaluation was primarily driven by a smaller P3a in response to losses under risk, but by a larger P3a and P3b in response to gains under ambiguity. Parametric analyses found that both the reward positivity and P3a for gains and nongains were modulated by winning probability under risk, while the P3a for gains was influenced by ambiguity level under ambiguity. Our findings demonstrate the dissociable influences of contextual valence on feedback-related neural dynamics based on uncertainty type, supporting a critical role of valence-asymmetry in distinguishing risk from ambiguity.

Effects of cardiac and respiratory phases on auditory evoked potentials

Brain-body interactions play a crucial role in the perceptual and cognitive processing of external stimuli. Previous research has examined how cardiac phases (systole, diastole) and respiratory phases (inhalation, exhalation) influence various psychological functions, though findings on their impact on auditory processing remain inconsistent. This study investigated whether cardiac and respiratory phases affect auditory ERP components, specifically N1 and P2. To control for cardiac-related artifacts, pure tones (70 dB) and silent stimuli (0 dB) were presented in alternating, randomized intervals, and ERP difference waveforms were computed by subtracting waveforms elicited by silent stimuli from those elicited by tones. Two experiments were conducted with different participants: watching a video while ignoring the tones (Experiment 1) or pressing a button as quickly as possible in response to the tones while watching the video (Experiment 2). Results showed no significant differences in N1 amplitude between cardiac or respiratory phases. P2 amplitude was significantly larger at diastole than systole, although the effect size was small (dz = 0.26). For respiratory phases, P2 amplitude was greater during exhalation than inhalation when participants ignored the tones (dz = 0.35), but this effect disappeared when they attended to the tones. These findings suggest that visceral afferent signals may influence auditory processing by modulating attentional resource allocation across different cardiac and respiratory phases.

An adaptive protocol to assess physiological responses as a function of task demand in speech-in-noise testing

BACKGROUND

Acoustic challenges impose demands on cognitive resources, known as listening effort (LE), which can substantially influence speech perception and communication. Standardized assessment protocols for monitoring LE are lacking, hindering the development of adaptive hearing assistive technology.

NEW METHOD

We employed an adaptive protocol, including a speech-in-noise test and personalized definition of task demand, to assess LE and its physiological correlates. Features extracted from electroencephalogram, galvanic skin response, electrocardiogram, respiration, pupil dilation, and blood volume pulse responses were analyzed as a function of task demand in 21 healthy participants with normal hearing.

RESULTS

Heightened sympathetic response was observed with higher task demand, evidenced by increased heart rate, blood pressure, and breath amplitude. Blood volume amplitude and breath amplitude exhibited higher sensitivity to changes in task demand.

COMPARISON WITH EXISTING METHODS

Notably, galvanic skin response showed higher amplitude during low task demand phases, indicating increased attention and engagement, aligning with findings from electroencephalogram signals and Lacey's attention theory.

CONCLUSIONS

The analysis of a range of physiological signals, spanning cardiovascular, central, and autonomic domains, demonstrated effectiveness in comprehensively examining LE. Future research should explore additional levels and manipulations of task demand, as well as the influence of individual motivation and hearing sensitivity, to further validate these outcomes and enhance the development of adaptive hearing assistive technology.

Speech Reception Threshold Estimation via EEG-Based Continuous Speech Envelope Reconstruction

This study investigates the potential of speech reception threshold (SRT) estimation through electroencephalography (EEG) based envelope reconstruction techniques with continuous speech. Additionally, we investigate the influence of the stimuli's signal-to-noise ratio (SNR) on the temporal response function (TRF). Twenty young normal-hearing participants listened to audiobook excerpts with varying background noise levels while EEG was recorded. A linear decoder was trained to reconstruct the speech envelope from the EEG data. The reconstruction accuracy was calculated as the Pearson's correlation between the reconstructed and actual speech envelopes. An EEG SRT estimate (SRTneuro) was obtained as the midpoint of a sigmoid function fitted to the reconstruction accuracy versus SNR data points. Additionally, the TRF was estimated at each SNR level, followed by a statistical analysis to reveal significant effects of SNR levels on the latencies and amplitudes of the most prominent components. The SRTneuro was within 3 dB of the behavioral SRT for all participants. The TRF analysis showed a significant latency decrease for N1 and P2 and a significant amplitude magnitude increase for N1 and P2 with increasing SNR. The results suggest that both envelope reconstruction accuracy and the TRF components are influenced by changes in SNR, indicating they may be linked to the same underlying neural process.

Dissociable effects of perceived control on reward-related neural dynamics under risk and ambiguity

Perceived control plays a crucial role in risk-taking behavior, but its neural effect on reward dynamics in risky and ambiguous decision making remains unclear. Here, we addressed this issue by measuring participants' (N = 40) EEG activity while they were performing a wheel-of-fortune task. Participants either made choices themselves (a high control condition) or followed the computer's choice (a low control condition) under risky or ambiguous decision contexts. Behavioral and rating data showed a stronger control effect in the risky compared to the ambiguous decision context. In parallel, we found an effect of perceived control on choice evaluation (indexed by the cue-P3) in the risky but not ambiguous context. However, the control effect was more pronounced during feedback anticipation (indexed by the stimulus-preceding negativity) and outcome appraisal (indexed by delta oscillation) in the ambiguous context compared to the risky context. Together, our findings suggest that experiencing control alters reward dynamics in uncertain decision making, with dissociable effects between risk and ambiguity.

Resting-State Electroencephalographic Signatures Predict Treatment Efficacy of tACS for Refractory Auditory Hallucinations in Schizophrenic Patients

Transcranial alternating current stimulation (tACS) has been reported to treat refractory auditory hallucinations in schizophrenia. Despite diligent efforts, it is imperative to underscore that tACS does not uniformly demonstrate efficacy across all patients as with all treatments currently employed in clinical practice. The study aims to find biomarkers predicting individual responses to tACS, guiding treatment decisions, and preventing healthcare resource wastage. We divided 17 schizophrenic patients with refractory auditory hallucinations into responsive(RE) and non-responsive(NR) groups based on their auditory hallucination symptom reduction rates after one month of tACS treatment. The pre-treatment resting-state electroencephalogram(rsEEG) was recorded and then computed absolute power spectral density (PSD), Hjorth parameters (HPs, Hjorth activity (HA), Hjorth mobility (HM), and Hjorth complexity (HC) included) from different frequency bands to portray the brain oscillations. The results demonstrated that statistically significant differences localized within the high gamma frequency bands of the right brain hemisphere. Immediately, we input the significant dissociable features into popular machine learning algorithms, the Cascade Forward Neural Network achieved the best recognition accuracy of 93.87%. These findings preliminarily imply that high gamma oscillations in the right brain hemisphere may be the main influencing factor leading to different responses to tACS treatment, and incorporating rsEEG signatures could improve personalized decisions for integrating tACS in clinical treatment.

Repetitive transcranial magnetic stimulation modulates brain connectivity in children with self-limited epilepsy with centrotemporal spikes

OBJECTIVE

Self-limited epilepsy with centrotemporal spikes (SeLECTS) is a common pediatric syndrome in which interictal epileptiform discharges (IEDs) emerge from the motor cortex and children often develop language deficits. IEDs may induce these language deficits by pathologically enhancing brain connectivity. Using a sham-controlled design, we test the impact of inhibitory low-frequency repetitive transcranial magnetic stimulation (rTMS) on connectivity and IEDs in SeLECTS.

METHODS

Nineteen children participated in a cross-over study comparing active vs. sham motor cortex rTMS. Single pulses of TMS combined with EEG (spTMS-EEG) were applied to the motor cortex before and after rTMS to probe connectivity. Connectivity was quantified by calculating the weighted phase lag index (wPLI) between six regions of interest: bilateral motor cortices (implicated in SeLECTS) and bilateral inferior frontal and superior temporal regions (important for language). IED frequency before and after rTMS was also quantified.

RESULTS

Active, but not sham, rTMS decreased wPLI connectivity between multiple regions, with the greatest reductions seen in superior temporal connections in the stimulated hemisphere. IED frequency decreased after active but not sham rTMS.

SIGNIFICANCE

Low-frequency rTMS reduces pathologic hyperconnectivity and IEDs in children with SeLECTS, making it a promising avenue for therapeutic interventions for SeLECTS and potentially other pediatric epilepsy syndromes.

Breaking up sitting enhances neurocognitive function which is associated with improved postprandial glucose regulation in healthy adults: A randomized crossover study

BACKGROUND AND AIMS

The glucose-centric hypothesis postulates that glycemic control may influence cognition. While research has examined the effects of breaking up sitting on blood glucose and inhibitory control, few studies have integrated these data and employed event-related potential (ERP) measures to delve into the neuroelectric processes. This study aimed to investigate the effects of breaking up sitting on postprandial blood glucose response, inhibitory control, and P3 component.

METHODS

Eighteen healthy male participants [25 ± 4 years, 23.5 ± 3.2 kg/m² (mean ± SD)] were subjected to 3.5 h uninterrupted sitting (SIT) or with 3 min walking at 6.4 km/h every 30 min (ACTIVE) trials in a randomized crossover design. The Stroop task was administered to assess inhibitory control before and after SIT and ACTIVE trials, and electroencephalography was employed to derive stimulus-elicited P3 component. Finger prick blood glucose levels were collected at baseline, 0.5 h, 1 h, and 3.5 h during the trials.

RESULTS

While no significant differences were found in inhibitory control performances between trials, greater P3 amplitude was found in the ACTIVE trial relative to the SIT trial (p = .041). Lower postprandial blood glucose iAUC was found in ACTIVE trial compared to SIT trial (p = .028), and this was correlated with the elevation of P3 amplitude (r = - 0.521, p = .023).

CONCLUSION

Breaking up sitting acutely facilitates neuroelectric indices of attentional processing, which is associated with the optimal postprandial blood glucose control.

The relationship between cognitive function and neuropsychiatric disorders with quantitative electroencephalogram (qEEG) on long COVID syndrome patients

Background

The COVID-19 pandemic has resulted in long-term consequences for a subset of affected individuals, known as long COVID syndrome. The neurological and psychiatric effects of this condition remain incompletely understood. This study aims to evaluate heightened common mental disorders in long COVID through assessing psychiatric, cognitive, neurophysiological aspects, and emphasizing lasting mental health impacts.

Methods

This cross-sectional study compared patients with long COVID to those who had recovered from COVID-19 without residual symptoms using quantitative electroencephalogram (qEEG) analysis. We conducted qEEG analyses, and Montreal Cognitive Assessment (MoCA) and Self-Rating Questionnaire (SRQ) tests on participants. Analyses included brain spectrum examination, hemispheric asymmetry, and inter-electrode connectivity.

Results

Analyses revealed lower MoCA scores in the memory domain were lower in the long COVID group (Mann Whitney Utest), indicating that individuals with long COVID experience more substantial cognitive deficits. There is no statistical difference for spectrum examination and hemispheric asymmetry observed in the qEEG data between the COVID and long COVID groups. Connectivity analysis showed statistically significant higher connectivity in temporal-occipital (T6-O2) in long COVID groups (Mann Whitney Utest).

Conclusion

Our findings underscore the persistent neuropsychiatric impact of COVID-19, particularly in long COVID patients. Notably, working memory deficits in MoCA scores were identified as one of the most frequent neuropsychological symptoms in these individuals. Decreased brain connectivity indicates cognitive-sensorimotor decline and is confirmed by the frequent brain fog symptoms in long COVID.

The Power of Food Advertisements: A Brief Mindfulness Instruction Does Not Prevent Psychophysiological Responses Triggered by Food Ads

Background: Exposure to visually appealing food items can enhance their subjective realism, leading to increased cravings, salivation, and automatic approach tendencies. Prior research suggests that brief mindfulness instructions promoting dereification-recognizing stimuli as transient mental events-can mitigate these automatic reactions. Objectives: This study assesses whether brief mindfulness instruction can mitigate automatic consumption tendencies induced by food advertisements, exploring the corresponding behavioral, physiological, and neurophysiological mechanisms. Methods: Sixty participants were randomly assigned to two groups: one receiving brief mindfulness instruction and the other a non-dereifying control instruction while exposed to advertised foods. This was followed by an approach-avoidance task (AAT), during which behavioral data, salivary volume, event-related potentials (ERPs) from electroencephalogram recordings, and self-reports were collected. Results: The results showed no significant differences in approach behaviors between the groups. Hunger, food craving, and salivation levels increased uniformly in response to food cues for both groups. The N1, N2, P3, and late positive potential (LPP) ERPs remained unaltered by the instructions and consistent with the established AAT literature. Advertising heightened the appeal of neutral foods, as evidenced by increased N2, P3, and LPP responses. Conclusions: The brief mindfulness instruction failed to shield participants from the automatic responses elicited by food advertising, contrasting with the effects seen with non-advertised food.

Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals

Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminating these artifacts during preprocessing enhances the performance of multivariate pattern analysis (MVPA; decoding), especially given that artifact rejection reduces the number of trials available for training the decoder. This study aimed to evaluate the impact of artifact-minimization approaches on the decoding performance of support vector machines. Independent component analysis (ICA) was used to correct ocular artifacts, and artifact rejection was used to discard trials with large voltage deflections from other sources (e.g., muscle artifacts). We assessed decoding performance in relatively simple binary classification tasks using data from seven commonly-used event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity), as well as more challenging multi-way decoding tasks, including stimulus location and stimulus orientation. The results indicated that the combination of artifact correction and rejection did not improve decoding performance in the vast majority of cases. However, artifact correction may still be essential to minimize artifact-related confounds that might artificially inflate decoding accuracy. Researchers who are decoding EEG data from paradigms, populations, and recording setups that are similar to those examined here may benefit from our recommendations to optimize decoding performance and avoid incorrect conclusions.

Cross-modal congruency modulates evidence accumulation, not decision thresholds

Audiovisual cross-modal correspondences (CMCs) refer to the brain's inherent ability to subconsciously connect auditory and visual information. These correspondences reveal essential aspects of multisensory perception and influence behavioral performance, enhancing reaction times and accuracy. However, the impact of different types of CMCs-arising from statistical co-occurrences or shaped by semantic associations-on information processing and decision-making remains underexplored. This study utilizes the Implicit Association Test, where unisensory stimuli are sequentially presented and linked via CMCs within an experimental block by the specific response instructions (either congruent or incongruent). Behavioral data are integrated with EEG measurements through neurally informed drift-diffusion modeling to examine how neural activity across both auditory and visual trials is modulated by CMCs. Our findings reveal distinct neural components that differentiate between congruent and incongruent stimuli regardless of modality, offering new insights into the role of congruency in shaping multisensory perceptual decision-making. Two key neural stages were identified: an Early component enhancing sensory encoding in congruent trials and a Late component affecting evidence accumulation, particularly in incongruent trials. These results suggest that cross-modal congruency primarily influences the processing and accumulation of sensory information rather than altering decision thresholds.

Enhanced Somatosensory Inhibition Sharpens Hand Representation and Sensorimotor Skills in Pianists

Dexterous motor skills, like those needed for playing musical instruments and sports, require the somatosensory system to accurately and rapidly process somatosensory information from multiple body parts. This is challenging due to the convergence of afferent inputs from different body parts into a single neuron and the overlapping representation of neighboring body parts in the somatosensory cortices. How do trained individuals, such as pianists and athletes, manage this? Here, a series of five experiments with pianists and nonmusicians (female and male) shows that pianists have enhanced inhibitory function in the somatosensory system, which isolates the processing of somatosensory afferent inputs from each finger. This inhibitory function was assessed using a paired-pulse paradigm of somatosensory evoked potentials in electroencephalography, which measures the suppressive effect of a first stimulus [i.e., conditioning stimulus (CS)] on the response to a subsequent second stimulus. We found that pianists and nonmusicians showed an inhibitory response to the sequential stimuli to the peripheral somatosensory nerve at the wrist when the CS was intense. However, only pianists exhibited an inhibitory response to a weak CS, indicating enhanced inhibitory function in pianists. Additionally, the CS increased the information content segregating individual fingers represented in the cortical activity evoked by passive finger movements and improved the perception of fast multifinger sequential movements, specifically for pianists. Our findings provide the first evidence for experience-dependent plasticity in somatosensory inhibitory function and highlight its role in the expert motor performance of pianists.

Age-related changes in neural oscillations vary as a function of brain region and frequency band

Advanced aging is associated with robust changes in neural activity. In addition to the well-established age-related slowing of the peak alpha frequency, there is a growing body of evidence showing that older age is also associated with changes in alpha power and beta power. Despite the important progress that has been made, the interacting effects of age and frequency band have not been directly tested in sensor and source space while controlling for aperiodic components. In the current study we address these limitations. We recruited 54 healthy younger and older adults and measured neural oscillations using a high-density electroencephalogram (EEG) system during resting-state with eyes closed. After preprocessing the EEG data and controlling for aperiodic components, we computed alpha and beta power in both sensor and source space. Permutation two-way ANOVAs between frequency band and age group were performed across all electrodes and across all dipoles. Our findings revealed significant interactions in sensorimotor, parietal, and occipital regions. The pattern driving the interaction varied across regions, with older age associated with a progressive decrease in alpha power and a progressive increase in beta power from parietal to sensorimotor regions. Our findings demonstrate that age-related changes in neural oscillations vary as a function of brain region and frequency band. We interpret our findings in the context of clinical and preclinical evidence of age effects on the cholinergic circuit and the Cortico-Basal Ganglia-Thalamo-Cortical (CBGTC) circuit.

Semantic memory space becomes denser with age

Semantic memory, a repository for concepts and factual information, plays a vital role in acquiring and retrieving knowledge. This study explores the impact of age-related knowledge accumulation on semantic cognition, investigating whether a denser representational space affects retrieval processes. Using a semantic feature verification task, we employ both behavioral (reaction time; RT) and neurophysiological (event-related potential; ERP) measures to explore these dynamics across young and older adults. Findings revealed an age-related RT difference in retrieval of semantically incongruent features, indicative of increased semantic search demands with age. ERP results show attenuated N400 responses in older adults for congruent features, possibly reflecting increased semantic relatedness. The late frontal effect (LFE) shows sustained modulation in older adults, indicative of enhanced post-retrieval monitoring. We propose that this extended search through semantic memory reflects an increase in the number of features to evaluate. These results support the idea that aging leads to a more densely packed semantic space, impacting the speed and dynamics of semantic retrieval.

Demystifying authenticity: Behavioral and neurophysiological signatures of self-positivity for authentic and presented selves

Authenticity has captivated scholars. But what is it? An emerging view considers it exaggerated favorability (self-enhancement), whereas traditional views regard it as self-accuracy and self-consistency. We tested these theoretical views by contrasting the authentic self with the presented self, a highly desirable representation. Behaviorally, participants ascribed less positivity to the authentic self: They endorsed more negative traits and were faster to admit having them; also, they endorsed fewer positive traits and were slower to admit having them. Neurally, participants manifested preferential processing of threatening information (P1), followed by preferential processing of favorable information (N170), about the presented self (than authentic self), indicating its brittleness. At a later stage (LPP), participants engaged in more elaborate processing of threatening and favorable information about the authentic self, indicating its subjective importance. Authenticity, albeit mostly positive, allows room for negativity.

Exercising self-control increases responsivity to hedonic and eudaimonic rewards

The reward responsivity hypothesis of self-control proposes that irrespective of self-control success, exercising self-control is aversive and engenders negative affect. To countermand this discomfort, reward-seeking behavior may be amplified after bouts of self-control, bringing individuals back to a mildly positive baseline state. Previous studies indicated that effort-an integral component of self-control-can increase reward responsivity. We sought to test and extend the reward responsivity hypothesis by asking if exercising self-control increases a neural marker of reward responsivity [Reward Positivity (RewP)] differentially for hedonic rewards or eudaimonic rewards. We instructed participants (N = 114) to complete a speeded reaction time task where they exercised self-control (incongruent Stroop trials) or not (congruent Stroop trials) and then had the opportunity to win money for themselves (hedonic rewards) or a charity (eudaimonic rewards) while electroencephalography was recorded. Consistent with the reward responsivity hypothesis, participants evinced a larger RewP after exercising self-control (vs. not exercising self-control). Participants also showed a larger RewP for hedonic over eudaimonic rewards. Self-control and reward type did not interactively modulate RewP, suggesting that self-control increases reward responsivity in a domain-general manner. The findings provide a neurophysiological mechanism for the reward responsivity hypothesis of self-control and promise to revitalize the relevant literature.

The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network

High frequency oscillations are important novel biomarkers of epileptic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. Power-to-power coupling (PPC) is one form of coupling with significant research attesting to its neurobiological significance as well as its computational efficiency, yet has been hitherto unexplored within seizure classification literature. New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. Here we present a Stacked Sparse Autoencoder (SSAE) trained to classify absence seizure activity based on this important form of cross-frequency patterns within scalp EEG. The analysis is done on the EEG records from the Temple University Hospital database. Absence seizures (n = 94) from 12 patients were taken into analysis along with segments of background activity. Power-to-power coupling was calculated between all frequencies 2-120 Hz pairwise using the EEGLAB toolbox. The resulting CFC matrices were used as training or testing inputs to the autoencoder. The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 93.1%, specificity of 99.5% and overall accuracy of 96.8%. The results provide evidence both for (1) the relevance of PPC for seizure classification, as well as (2) the efficacy of an approach combining PPC with SSAE neural networks for automated classification of absence seizures within scalp EEG.

Resting-State EEG Power Spectral Density Analysis Between Healthy and Cognitively Impaired Subjects

Background/Objectives: This study evaluates the potential of electroencephalography (EEG) as a noninvasive tool for distinguishing between healthy individuals (n = 79), those with mild cognitive impairment (MCI; n = 36), and dementia patients (n = 7). Methods: Using a 14-channel Emotiv EPOC-X headset, we analyzed power spectral density during a 2-min eyes-closed resting state. Results: Our results demonstrated that while EEG effectively differentiated dementia patients from healthy controls, it did not show significant differences between MCI and healthy controls. This indicates that EEG holds promise for identifying advanced cognitive decline but faces challenges in early-stage detection. Conclusions: The study contributes to the growing body of literature by highlighting EEG's potential as a cost-effective alternative to invasive diagnostic methods while also identifying the need for larger sample sizes and task-oriented approaches to improve its diagnostic precision.

Loudness dependence of the auditory evoked potential: temporal stability, associations to sociodemographic variables, and functional significance-implications for clinical research

Introduction

The loudness dependence of the auditory evoked potential (LDAEP) has been suggested as a biomarker for central serotonergic function, and as such a proxy for serotonin related psychiatric symptomatology and intervention outcome, particularly in depression. This study aims to explore LDAEP characteristics in a large healthy population by assessing its test-retest reliability and examining associations with sociodemographic variables, psychological distress, and performance-based cognitive function.

Methods

Our sample included 100 healthy adults whose LDAEP was measured and correlated with age, sex, self-reported psychological distress, and cognitive performance.

Results

Participants examined twice (n = 38) showed high test-retest reliability with intraclass correlations (ICCs) between 0.67 and 0.89 over a 2-to-3-month interval. Furthermore, the magnitude of the LDAEP was significantly higher in women than men, and female hormonal contraceptive users exhibited higher LDAEP than non-users. In females, age was inversely correlated with LDAEP. However, no significant associations were found between LDAEP and measures of psychological distress, including depressive symptoms, nor with cognitive test performance.

Discussion

These results underline LDAEP's reliability as a biomarker over time, but also highlight age, sex and hormonal contraceptive use as significant factors influencing the LDAEP. Future research in clinical population should take these results into account, with an emphasis on providing the necessary sample sizes for relevant sub-group analyses.

Neural tracking of auditory statistical regularities in adults with and without dyslexia

Listeners implicitly use statistical regularities to segment continuous sound input into meaningful units, eg transitional probabilities between syllables to segment a speech stream into separate words. Implicit learning of such statistical regularities in a novel stimulus stream is reflected in a synchronization of neural responses to the sequential stimulus structure. The present study aimed to test the hypothesis that neural tracking of the statistical stimulus structure is reduced in individuals with dyslexia who have weaker reading and spelling skills, and possibly also weaker statistical learning abilities in general, compared to healthy controls. To this end, adults with and without dyslexia were presented with continuous streams of (non-speech) tones, which were arranged into triplets, such that transitional probabilities between single tones were higher within triplets and lower between triplets. We found that the so-called Triplet Learning Index (ie the ratio of neural phase coherence at the triplet rate relative to the tone rate) was lower in adults with dyslexia compared to the control group. Moreover, a higher Triplet Learning Index was associated with better spelling skills. These results suggest that individuals with dyslexia have a rather broad deficit in processing structure in sound instead of a merely phonological deficit.

How does Independent Component Analysis Preprocessing Affect EEG Microstates?

Over recent years, electroencephalographic (EEG) microstates have been increasingly used to investigate, at a millisecond scale, the temporal dynamics of large-scale brain networks. By studying their topography and chronological sequence, microstates research has contributed to the understanding of the brain's functional organization at rest and its alteration in neurological or mental disorders. Artifact removal strategies, which differ from study to study, may alter microstates topographies and features, possibly reducing the generalizability and comparability of results across research groups. The aim of this work was therefore to test the reliability of the microstate extraction process and the stability of microstate features against different strategies of EEG data preprocessing with Independent Component Analysis (ICA) to remove artifacts embedded in the data. A normative resting state EEG dataset was used where subjects alternate eyes-open (EO) and eyes-closed (EC) periods. Four strategies were tested: (i) avoiding ICA preprocessing altogether, (ii) removing ocular artifacts only, (iii) removing all reliably identified physiological/non physiological artifacts, (iv) retaining only reliably identified brain ICs. Results show that skipping the removal of ocular artifacts affects the stability of microstate evaluation criteria, microstate topographies and greatly reduces the statistical power of EO/EC microstate features comparisons, however differences are not as prominent with more aggressive preprocessing. Provided a good-quality dataset is recorded, and ocular artifacts are removed, microstates topographies and features can capture brain-related physiological data and are robust to artifacts, independently of the level of preprocessing, paving the way to automatized microstate extraction pipelines.

Semantic Context Effects in Picture and Sound Naming: Evidence from Event-related Potentials and Pupillometric Data

When a picture is repeatedly named in the context of semantically related pictures (homogeneous context), responses are slower than when the picture is repeatedly named in the context of unrelated pictures (heterogeneous context). This semantic interference effect in blocked-cyclic naming plays an important role in devising theories of word production. Wöhner, Mädebach, and Jescheniak [Wöhner, S., Mädebach, A., & Jescheniak, J. D. Naming pictures and sounds: Stimulus type affects semantic context effects. Journal of Experimental Psychology: Human Perception and Performance, 47, 716-730, 2021] have shown that the effect is substantially larger when participants name environmental sounds than when they name pictures. We investigated possible reasons for this difference, using EEG and pupillometry. The behavioral data replicated Wöhner and colleagues. ERPs were more positive in the homogeneous compared with the heterogeneous context over central electrode locations between 140-180 msec and 250-350 msec for picture naming and between 250 and 350 msec for sound naming, presumably reflecting semantic interference during semantic and lexical processing. The later component was of similar size for pictures and sounds. ERPs were more negative in the homogeneous compared with the heterogeneous context over frontal electrode locations between 400 and 600 msec only for sounds. The pupillometric data showed a stronger pupil dilation in the homogeneous compared with the heterogeneous context only for sounds. The amplitudes of the late ERP negativity and pupil dilation predicted naming latencies for sounds in the homogeneous context. The latency of the effects indicates that the difference in semantic interference between picture and sound naming arises at later, presumably postlexical processing stages closer to articulation. We suggest that the processing of the auditory stimuli interferes with phonological response preparation and self-monitoring, leading to enhanced semantic interference.

Meanness and deficits in facial affect processing: Evidence from the N170

The triarchic construct of meanness, characterized by traits such as low empathy and lack of remorse, appears to be associated with deficits in affective processing. Specifically, meanness-related traits have been linked to deficits in brain reactivity and recognition of facial emotion. Recent meta-analytic work has provided preliminary evidence suggesting that N170 ERP amplitude reductions to fear faces might serve as a neurophysiological marker for meanness traits. However, most of these studies do not examine emotions other than fear, leaving open the question of whether meanness-related alterations in fear face processing might generalize to other facial expressions, nor the role of the other constructs of the triarchic model of psychopathy (i.e., boldness and disinhibition). The current study aimed to address these issues by measuring N170 amplitudes in a mixed-gender sample of 119 undergraduates while they passively viewed a broader range of emotional facial expressions (Anger, Fear, Happiness, and Neutral) alongside control (Scrambled) pictures. The specificity of this association to trait meanness compared to the other triarchic constructs was also assessed. Our results demonstrated meanness-related reductions in N170 amplitudes across all types of facial expressions, not just to fearful faces, even after controlling for the overlap with the other triarchic constructs. These findings highlight a general deficit in the structural encoding of facial features in high mean individuals, emphasizing the potential role of the N170 as a physiological tool for advancing our understanding of the mechanisms contributing to the aggressive, callous, and low-empathic characteristics of psychopathy.

The dynamic influence of language-switching contexts on domain-general cognitive control: An EEG study

In everyday conversation, bilingual individuals switch between their languages not only in reaction to monolinguals with different language profiles but also voluntarily and naturally. However, whether and how various switching contexts dynamically modulate domain-general cognitive control is still unclear. Using a cross-task paradigm in which a flanker task was interleaved with a language-switching task trial-by-trial, the present study examined the performance of unbalanced Chinese-English bilinguals on a flanker task in forced, voluntary, and natural switching contexts. The cross-domain interaction on the P3 component revealed an atypical flanker effect in forced switching contexts only, and the P3 amplitude of incongruent trials in forced switching contexts was smaller than in both natural and voluntary switching contexts. Furthermore, robust brain-brain and brain-behavior relationships between language control and domain-general control emerged in the forced switching context only. Altogether, our findings support the dynamic adaptation of language control to cognitive control and highlight the importance of different types of switching contexts.

Brainwave Activity Localization, Mood Symptoms, and Balance Impairment in a Male South African Rugby Player With Persisting Symptoms After Concussion: A Case Report

The case sets the foundation for clinical protocols to incorporate mobile EEG and qEEG techniques, instrumental balance testing, and mood symptom screening in athletes who have suffered a sports-related concussion. The protocol provides a framework for clinicians to monitor a patient's recovery progress in terms of brainwave activity, general cognition, moods, and motor control. Objective data obtained through the protocol may assist in developing personalized treatment plans, improving follow-up care, and identifying residual brain function deficits that may be missed in standardized clinical exams. Finally, this case highlights a need for more thorough communication and testing procedures that screen for mood symptoms and provide an opportunity for athletes to discuss their mental health after suffering from an SRC.

Extended Cognitive Load Induces Fast Neural Responses Leading to Commission Errors

Extended performance of cognitively demanding tasks induces cognitive fatigue manifested with an overall deterioration of behavioral performance. In particular, long practice with tasks requiring impulse control is typically followed by a decrease in self-control efficiency, leading to performance instability. Here, we show that this is due to changes in activation modalities of key task-related areas occurring if these areas previously underwent intensive use. We investigated in 25 healthy adults the effects of extended practice with high cognitive demand (HCD) tasks on a Go-No Go task and the underlying electroencephalographic (EEG) activity. We compared these effects with those induced by practice with similar, but low cognitive demand (LCD) tasks. HCD tasks were followed by an increase in response inhibition failures. These were correlated with the appearance of a distinct neural signature on fast response trials, characterized by lower levels of beta ([13-30] Hz) EEG activity in the prestimulus period, and by a lack of EEG markers of preresponse processing in frontal areas. Moreover, HCD tasks were followed by a decrease in N200 during correct withholds while LCD tasks were followed instead by a lesser fraction of hits and a decrease in P300, suggesting a decrease in engagement. Overall, these results show that exertion of cognitive control determines the appearance of two distinct modalities of response with different processing speeds, associated with distinct underlying neural activity.

The Interplay Between Multisensory Processing and Attention in Working Memory: Behavioral and Neural Indices of Audiovisual Object Storage

Although real-life events are multisensory, how audio-visual objects are stored in working memory is an open question. At a perceptual level, evidence shows that both top-down and bottom-up attentional processes can play a role in multisensory interactions. To understand how attention and multisensory processes interact in working memory, we designed an audiovisual delayed match-to-sample task in which participants were presented with one or two audiovisual memory items, followed by an audiovisual probe. In different blocks, participants were instructed to either (a) attend to the auditory features, (b) attend to the visual features, or (c) attend to both auditory and visual features. Participants were instructed to indicate whether the task-relevant features of the probe matched one of the task-relevant feature(s) or objects in working memory. Behavioral results showed interference from task-irrelevant features, suggesting bottom-up integration of audiovisual features and their automatic encoding into working memory, irrespective of task relevance. Yet, event-related potential analyses revealed no evidence for active maintenance of these task-irrelevant features, while they clearly taxed greater attentional resources during recall. Notably, alpha oscillatory activity revealed that linking information between auditory and visual modalities required more attentional demands at retrieval. Overall, these results offer critical insights into how and at which processing stage multisensory interactions occur in working memory.

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging

OBJECTIVE

To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age- and sex-matched controls.

METHODS

Twenty-two cognitively normal preclinical AD participants with elevated amyloid and 21 cognitively normal controls without elevated amyloid completed n-back working memory tasks (n = 0, 1, 2). The absolute and relative power of ERO was extracted using the discrete wavelet transform in the delta, theta, alpha, and beta bands. Four machine learning methods were employed, and classification performance was assessed using three metrics.

RESULTS

The low-frequency bands produced higher discriminative performances compared to high-frequency bands. The 2-back task yielded the best classification capability among the three tasks. The highest area under the curve value (0.86) was achieved in the 2-back delta band nontarget condition data. The highest accuracy (80.47%) was obtained in the 2-back delta and theta bands nontarget data. The highest F1 score (0.82) was in the 2-back theta band nontarget data. The support vector machine achieved the highest performance among tested classifiers.

CONCLUSION

This study demonstrates the promise of using machine learning on EEG ERO from working memory tasks to detect preclinical AD.

SIGNIFICANCE

EEG ERO may reveal pathophysiological differences in the earliest stage of AD when no cognitive impairments are apparent.

Posture-Dependent Modulation of Interoceptive Processing in Young Male Participants: A Heartbeat-Evoked Potential Study

Interoception, the internal perception of bodily states such as heartbeat and hunger, plays a crucial role in shaping cognitive and emotional states. Since postural control affects cognitive and emotional processing, exploring postural effects on interoception could help uncover the neural mechanisms underlying its effects on cognition and emotion. In this study, we aimed to investigate how different postures affect interoception by using heartbeat-evoked potentials (HEPs), which reflect the cortical processing of cardiac signals. Two experiments were conducted; Experiment 1 involved 47 healthy male participants comparing sitting and standing postures, and Experiment 2 involved 24 healthy male participants comparing stable and unstable standing conditions. HEPs were analyzed using cluster-based permutation analysis to identify statistically significant spatiotemporal clusters. In Experiment 1, significant clusters were identified over central electrodes (Cz, C1, C2, FCz, and FC1) within the post-R-wave interval of 304-572 ms, revealing significantly lower HEP amplitudes during standing compared to sitting [W = 80, p < 0.001, r = 0.62]. In Experiment 2, HEP amplitudes were significantly lower during unstable standing compared to stable standing [t(20) = 2.9, p = 0.0099, d = 0.62]. Furthermore, we found no significant correlations between HEP changes and physiological changes such as cardiac activity and periodic and aperiodic brain activity. These findings suggest postural differences modulate interoceptive processing, with standing postures attenuating HEP amplitudes, probably because of a redistribution of attentional resources from interoceptive to somatosensory (proprioceptive) and vestibular processing, necessary for maintaining standing posture. This study provides insights into the neural mechanisms underlying posture-interoception interaction.

Current source density and functional connectivity extracted from resting-state electroencephalography as biomarkers for chronic low back pain

Introduction

Chronic low back pain (CLBP) is a global health issue, and its nonspecific causes make treatment challenging. Understanding the neural mechanisms of CLBP should contribute to developing effective therapies.

Objectives

To compare current source density (CSD) and functional connectivity (FC) extracted from resting electroencephalography (EEG) between patients with CLBP and healthy controls and to examine the correlations between EEG indices and symptoms.

Methods

Thirty-four patients with CLBP and 34 healthy controls in an open data set were analyzed. Five-minute resting-state closed-eye EEG was acquired using the international 10-20 system. Current source density across frequency bands was calculated using exact low-resolution electromagnetic tomography. Functional connectivity was assessed between 24 cortical regions using lagged linear connectivity. Correlations between pain symptoms and CSD distribution and FC were examined in patients with CLBP.

Results

Current source density analysis showed no significant differences between the groups. The CLBP group exhibited significantly reduced FC in the β3 band between the left middle temporal gyrus and the posterior cingulate cortex, and between the ventral medial prefrontal cortex and the left inferior parietal lobule. Prefrontal θ and δ activity positively correlated with pain symptoms. Increased β1 band FC between the right dorsolateral prefrontal cortex and right auditory cortex correlated with greater pain intensity.

Conclusions

We found altered neural activity and connectivity in patients with CLBP, particularly in prefrontal and temporal regions. These results suggest potential targets for pain modulation through brain pathways and highlight the value of EEG biomarkers in understanding pain mechanisms and assessing treatment efficacy.

The Problem with Time: Application of Partial Least Squares Analysis on Time-Frequency Plots to Account for Varying Time Intervals with Applied EEG Data

Background/Objectives: When attempting to study neurocognitive mechanisms with electroencephalography (EEG) in applied ecologically valid settings, responses to stimuli may differ in time, which presents challenges to traditional EEG averaging methods. In this proof-of-concept paper, we present a method to normalize time over unequal trial lengths while preserving frequency content. Methods: Epochs are converted to time-frequency space where they are resampled to contain an equal number of timepoints representing the proportion of trial complete rather than true time. To validate this method, we used EEG data recorded from 8 novices and 4 experts in veterinary medicine while completing decision-making tasks using two question types: multiple-choice and script concordance questions used in veterinary school exams. Results: The resulting resampled time-frequency data were analyzed with partial least squares (PLS), a multivariate technique that extracts patterns of data that support a contrast between conditions and groups while controlling for Type I error. We found a significant latent variable representing a difference between question types for experts only. Conclusions: Despite within and between subject differences in timing, we found consistent differences between question types in experts in gamma and beta bands that are consistent with changes resulting from increased information load and decision-making. This novel analysis method may be a viable path forward to preserve ecological validity in EEG studies.

Double-blind, randomized, placebo-controlled pilot clinical trial with gamma-band transcranial alternating current stimulation for the treatment of schizophrenia refractory auditory hallucinations

Gamma oscillations are essential for brain communication. The 40 Hz neural oscillation deficits in schizophrenia impair left frontotemporal connectivity and information communication, causing auditory hallucinations. Transcranial alternating current stimulation is thought to enhance connectivity between different brain regions by modulating brain oscillations. In this work, we applied a frontal-temporal-parietal 40 Hz-tACS stimulation strategy for treating auditory hallucinations and further explored the effect of tACS on functional connectivity of brain networks. 32 schizophrenia patients with refractory auditory hallucinations received 20daily 20-min, 40 Hz, 1 mA sessions of active or sham tACS on weekdays for 4 consecutive weeks, followed by a 2-week follow-up period without stimulation. Auditory hallucination symptom scores and 64-channel electroencephalograms were measured at baseline, week2, week4 and follow-up. For clinical symptom score, we observed a significant interaction between group and time for auditory hallucinations symptoms (F(3,90) = 26.964, p < 0.001), and subsequent analysis showed that the 40Hz-tACS group had a higher symptom reduction rate than the sham group at week4 (p = 0.036) and follow-up (p = 0.047). Multiple comparisons of corrected EEG results showed that the 40Hz-tACS group had higher functional connectivity in the right frontal to parietal (F (1,30) = 7.24, p = 0.012) and right frontal to occipital (F (1,30) = 7.98, p = 0.008) than the sham group at week4. Further, functional brain network controllability outcomes showed that the 40Hz-tACS group had increased average controllability (F (1,30) = 6.26, p = 0.018) and decreased modality controllability (F (1,30) = 6.50, p = 0.016) in the right frontal lobe compared to the sham group. Our polit study indicates that 40Hz-tACS combined with medicine may be an effective treatment for targeting symptoms specific to auditory hallucinations and altering functional connectivity and controllability at the network level.