Electrophysiology as a theoretical and methodological hub for the neural sciences

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

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

Tracking gaze position from EEG: Exploring the possibility of an EEG-based virtual eye-tracker

INTRODUCTION

Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement-related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position.

METHODS

As proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.

RESULTS

The prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).

CONCLUSION

This work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement.

Diabetes-induced electrophysiological alterations on neurosomes in ganglia of peripheral nervous system

Diabetes mellitus (DM) leads to medical complications, the epidemiologically most important of which is diabetic peripheral neuropathy (DPN). Electrophysiology is a major component of neural functioning and several studies have been undertaken to elucidate the neural electrophysiological alterations caused by DM and their mechanisms of action. Due to the importance of electrophysiology for neuronal function, the review of the studies dealing predominantly with electrophysiological parameters and mechanisms in the neuronal somata of peripheral neural ganglia of diabetic animals during the last 45 years is here undertaken. These studies, using predominantly techniques of electrophysiology, most frequently patch clamp for voltage clamp studies of transmembrane currents through ionic channels, have investigated the experimental DPN. They also have demonstrated that various cellular and molecular mechanisms of action of diabetic physiopathology at the level of biophysical electrical parameters are affected in DPN. Thus, they have demonstrated that several passive and active transmembrane voltage parameters, related to neuronal excitability and neuronal functions, are altered in diabetes. The majority of the studies agreed that DM produces depolarization of the resting membrane potential; alters excitability, increasing and decreasing it in dorsal root ganglia (DRG) and in nodose ganglion, respectively. They have tried to relate these changes to sensorial alterations of DPN. Concerning ionic currents, predominantly studied in DRG, the most frequent finding was increases in Na+, Ca2+, and TRPV1 cation current, and decreases in K+ current. This review concluded that additional studies are needed before an understanding of the hierarchized, time-dependent, and integrated picture of the contribution of neural electrophysiological alterations to the DPN could be reached. DM-induced electrophysiological neuronal alterations that so far have been demonstrated, most of them likely important, are either consistent with the DPN symptomatology or suggest important directions for improvement of the elucidation of DPN physiopathology, which the continuation seems to us very relevant.

Electrophysiological Markers of Aberrant Cue-Specific Exploration in Hazardous Drinkers

Background

Hazardous drinking is associated with maladaptive alcohol-related decision-making. Existing studies have often focused on how participants learn to exploit familiar cues based on prior reinforcement, but little is known about the mechanisms that drive hazardous drinkers to explore novel alcohol cues when their value is not known.

Methods

We investigated exploration of novel alcohol and non-alcohol cues in hazardous drinkers (N = 27) and control participants (N = 26) during electroencephalography (EEG). A normative computational model with two free parameters was fit to estimate participants' weighting of the future value of exploration and immediate value of exploitation.

Results

Hazardous drinkers demonstrated increased exploration of novel alcohol cues, and conversely, increased probability of exploiting familiar alternatives instead of exploring novel non-alcohol cues. The motivation to explore novel alcohol stimuli in hazardous drinkers was driven by an elevated relative future valuation of uncertain alcohol cues. P3a predicted more exploratory decision policies driven by an enhanced relative future valuation of novel alcohol cues. P3b did not predict choice behavior, but computational parameter estimates suggested that hazardous drinkers with enhanced P3b to alcohol cues were likely to learn to exploit their immediate expected value.

Conclusions

Hazardous drinkers did not display atypical choice behavior, different P3a/P3b amplitudes, or computational estimates to novel non-alcohol cues-diverging from previous studies in addiction showing atypical generalized explore-exploit decisions with non-drug-related cues. These findings reveal that cue-specific neural computations may drive aberrant alcohol-related decision-making in hazardous drinkers-highlighting the importance of drug-relevant cues in studies of decision-making in addiction.

Neuroscience from the comfort of your home: Repeated, self-administered wireless dry EEG measures brain function with high fidelity

Recent advances have enabled the creation of wireless, “dry” electroencephalography (EEG) recording systems, and easy-to-use engaging tasks, that can be operated repeatedly by naïve users, unsupervised in the home. Here, we evaluated the validity of dry-EEG, cognitive task gamification, and unsupervised home-based recordings used in combination. Two separate cohorts of participants—older and younger adults—collected data at home over several weeks using a wireless dry EEG system interfaced with a tablet for task presentation. Older adults (n = 50; 25 females; mean age = 67.8 years) collected data over a 6-week period. Younger male adults (n = 30; mean age = 25.6 years) collected data over a 4-week period. All participants were asked to complete gamified versions of a visual Oddball task and Flanker task 5–7 days per week. Usability of the EEG system was evaluated via participant adherence, percentage of sessions successfully completed, and quantitative feedback using the System Usability Scale. In total, 1,449 EEG sessions from older adults (mean = 28.9; SD = 6.64) and 684 sessions from younger adults (mean = 22.87; SD = 1.92) were collected. Older adults successfully completed 93% of sessions requested and reported a mean usability score of 84.5. Younger adults successfully completed 96% of sessions and reported a mean usability score of 88.3. Characteristic event-related potential (ERP) components—the P300 and error-related negativity—were observed in the Oddball and Flanker tasks, respectively. Using a conservative threshold for inclusion of artifact-free data, 50% of trials were rejected per at-home session. Aggregation of ERPs across sessions (2–4, depending on task) resulted in grand average signal quality with similar Standard Measurement Error values to those of single-session wet EEG data collected by experts in a laboratory setting from a young adult sample. Our results indicate that easy-to-use task-driven EEG can enable large-scale investigations in cognitive neuroscience. In future, this approach may be useful in clinical applications such as screening and tracking of treatment response.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Joint analysis of frontal theta synchrony and white matter following mild traumatic brain injury

Some of the most disabling aspects of mild traumatic brain injury (mTBI) include lingering deficits in executive functioning. It is known that mTBI can damage white matter tracts, but it remains unknown how this structural brain damage translates into cognitive deficits. This experiment utilized theta band phase synchrony to identify the dysfunctional neural operations that contribute to cognitive problems following mTBI. Sub-acute stage (< 2 weeks) mTBI patients (N = 52) and healthy matched controls (N = 32) completed a control-demanding task with concurrent EEG. Structural MRI was also collected. While there were no performance-specific behavioral differences between groups in the dot probe expectancy task, the degree of theta band phase synchrony immediately following injury predicted the degree of symptom recovery two months later. Although there were no differences in fractional anisotropy (FA) between groups, joint independent components analysis revealed that a smaller network of lower FA-valued voxels contributed to a diminished frontal theta phase synchrony network in the mTBI group. This finding suggests that frontal theta band markers of cognitive control are sensitive to sub-threshold structural aberrations following mTBI.

From Correlational Signs to Markers. Current Trends in Neuroelectric Research on Visual Attentional Processing

Traditionally, electroencephalographic (EEG) and event-related brain potentials (ERPs) research on visual attentional processing attempted to account for mental processes in conceptual terms without reference to the way in which they were physically realized by the anatomical structures and physiological processes of the human brain. The brain science level of analysis, in contrast, attempted to explain the brain as an information processing system and to explain mental events in terms of brain processes. Somehow overcoming the separation between the two abovementioned levels of analysis, the cognitive neuroscience level considered how information was represented and processed in the brain. Neurofunctional processing takes place in a fraction of a second. Hence, the very high time resolution and the reliable sensitivity of EEG and ERPs in detecting fast functional changes in brain activity provided advantages over hemodynamic imaging techniques such as positron emission tomography (PET) or functional magnetic resonance imaging (fMRI), as well as over behavioral measures. However, volume conduction and lack of three-dimensionality limited applications of EEG and ERPs per se more than hemodynamic techniques for revealing locations in which brain processing occurs. These limits could only be overcome by subtraction methods for isolating attentional effects that might endure over time in EEG and may be riding even over several different ERP components, and by intracerebral single and distributed electric source analyses as well as the combining of these signals with high-spatial resolution hemodynamic signals (fMRI), both in healthy individuals and clinical patients. In my view, the articles of the Special Issue concerned with "ERP and EEG Markers of Brain Visual Attentional Processing" of the present journal Brain Sciences provide very good examples of all these levels of analysis.

Neural Oscillation Profiles of a Premise Monotonicity Effect During Semantic Category-Based Induction

A premise monotonicity effect during category-based induction is a robust effect, in which participants are more likely to generalize properties shared by many instances rather than those shared by few instances. Previous studies have shown the event-related potentials (ERPs) elicited by this effect. However, the neural oscillations in the brain underlying this effect are not well known, and such oscillations can convey task-related cognitive processing information which is lost in traditional ERP analysis. In the present study, the phase-locked and non-phase-locked power of neural oscillations related to this effect were measured by manipulating the premise sample size [single (S) vs. two (T)] in a semantic category-based induction task. For phase-locked power, the results illustrated that the premise monotonicity effect was revealed by anterior delta power, suggesting differences in working memory updating. The results also illustrated that T arguments evoked larger posterior theta-alpha power than S arguments, suggesting that T arguments led to enhanced subjectively perceived inductive confidence than S arguments. For non-phase-locked power, the results illustrated that the premise monotonicity effect was indicated by anterior theta power, suggesting that the differences in sample size were related to a change in the need for cognitive control and the implementation of adaptive cognitive control. Moreover, the results illustrated that the premise monotonicity effect was revealed by alpha-beta power, which suggested the unification of sentence and inference-driven information. Therefore, the neural oscillation profiles of the premise monotonicity effect during semantic category-based induction were elucidated, and supported the connectionist models of category-based induction.

ERPs predict symptomatic distress and recovery in sub-acute mild traumatic brain injury

Mild traumatic brain injury (mTBI) can affect high-level executive functioning long after somatic symptoms resolve. We tested if simple EEG responses within an oddball paradigm could capture variance relevant to this clinical problem. The P3a and P3b components reflect bottom-up and top-down processes driving engagement with exogenous stimuli. Since these features are related to primitive decision abilities, abnormal amplitudes following mTBI may account for problems in the ability to exert executive control. Sub-acute (<2 weeks) mTBI participants (N = 38) and healthy controls (N = 24) were assessed at an initial session as well as a two-month follow-up (sessions 1 and 2). We contrasted the initial assessment to a comparison group of participants with chronic symptomatology following brain injury (N = 23). There were no group differences in P3a or P3b amplitudes. Yet in the sub-acute mTBI group, higher symptomatology on the Frontal Systems Behavior scale (FrSBe), a questionnaire validated as measuring symptomatic distress related to frontal lobe injury, correlated with lower P3a in session 1. This relationship was replicated in session 2. These findings were distinct from chronic TBI participants, who instead expressed a relationship between increased FrSBe symptoms and a lower P3b component. In the sub-acute group, P3b amplitudes in the first session correlated with the degree of symptom change between sessions 1 and 2, above and beyond demographic predictors. Controls did not show any relationship between FrSBe symptoms and P3a or P3b. These findings identify symptom-specific alterations in neural systems that vary along the time course of post-concussive symptomatology.