Interindividual differences in alpha and theta power reflect memory performance

Correlates of Theta and Gamma Activity during Visuospatial Incidental/Intentional Encoding and Retrieval Indicate Differences in Processing in Young and Elderly Healthy Participants

Incidental visuospatial learning acquired under incidental conditions is more vulnerable to aging than in the intentional case. The theta and gamma correlates of the coding and retrieval of episodic memory change during aging. Based on the vulnerability of incidental coding to aging, different theta and gamma correlates could occur under the incidental versus intentional coding and retrieval of visuospatial information. Theta and gamma EEG was recorded from the frontotemporal regions, and incidental/intentional visuospatial learning was evaluated in young (25-60 years old) and elderly (60-85 years old) participants. The EEG recorded during encoding and retrieval was compared between incidental low-demand, incidental high-demand, and intentional conditions through an ANCOVA considering the patient's gender, IQ, and years of schooling as covariates. Older adults exhibited worse performances, especially in place-object associations. After the intentional study, older participants showed a further increase in false-positive errors. Higher power at the theta and gamma bands was observed for frontotemporal derivations in older participants for both encoding and retrieval. Under retrieval, only young participants had lower power in terms of errors compared with correct responses. In conclusion, the different patterns of power and coherence support incidental and intentional visuospatial encoding and retrieval in young and elderly individuals. The correlates of power with behavior are sensitive to age and performance.

Task-based default mode network connectivity predicts cognitive impairment and negative symptoms in first-episode schizophrenia

Individuals diagnosed with schizophrenia (SZ) demonstrate difficulty distinguishing between internally and externally generated stimuli. These aberrations in "source monitoring" have been theorized as contributing to symptoms of the disorder, including hallucinations and delusions. Altered connectivity within the default mode network (DMN) of the brain has been proposed as a mechanism through which discrimination between self-generated and externally generated events is disrupted. Source monitoring abnormalities in SZ have additionally been linked to impairments in selective attention and inhibitory processing, which are reliably observed via the N100 component of the event-related brain potential elicited during an auditory paired-stimulus paradigm. Given overlapping constructs associated with DMN connectivity and N100 in SZ, the present investigation evaluated relationships between these measures of disorder-related dysfunction and sought to clarify the nature of task-based DMN function in SZ. DMN connectivity and N100 measures were assessed using EEG recorded from SZ during their first episode of illness (N = 52) and demographically matched healthy comparison participants (N = 25). SZ demonstrated less evoked theta-band connectivity within DMN following presentation of pairs of identical auditory stimuli than HC. Greater DMN connectivity among SZ was associated with better performance on measures of sustained attention (p = .03) and working memory (p = .09), as well as lower severity of negative symptoms, though it was not predictive of N100 measures. Together, present findings provide EEG evidence of lower task-based connectivity among first-episode SZ, reflecting disruptions of DMN functions that support cognitive processes. Attentional processes captured by N100 appear to be supported by different neural mechanisms.

EEG alpha and theta time-frequency structure during a written mathematical task

Since the first electroencephalogram (EEG) was obtained, there have been many possibilities to use it as a tool to access brain cognitive dynamics. Mathematical (Math) problem solving is one of the most important cortical processes, but it is still far from being well understood. EEG is an inexpensive and simple indirect measure of brain operation, but only recently has low-cost equipment (mobile EEG) allowed sophisticated analyses in non-clinical settings. The main purpose of this work is to study EEG activation during a Math task in a realistic environment, using mobile EEG. A matching pursuit (MP)-based signal analysis technique was employed, since MP properties render it a priori suitable to study induced EEG activity over long time sequences, when it is not tightly locked to a given stimulus. The study sample comprised sixty healthy volunteers. Unlike the majority of previous studies, subjects were studied in a sitting position with their eyes open. They completed a written Math task outside the EEG lab, wearing a mobile EEG device (EPOC+). Theta [4 Hz-7.5 Hz], alpha (7.5 Hz-13 Hz] and 0.5 Hz micro-bands in the [0.5 Hz-20 Hz] range were studied with a low-density stochastic MP dictionary. Over 1-min windows, ongoing EEG alpha and theta activity was decomposed into numerous MP atoms with median duration around 3 s, similar to the duration of induced, time-locked activity obtained with event-related (des)synchronization (ERS/ERD) studies. Relative to Rest, there was lower right-side and posterior MP alpha atom/min during Math, whereas MP theta atom/min was significantly higher on anteriorly located electrodes, especially on the left side. MP alpha findings were particularly significant on a narrow range around 10 Hz-10.5 Hz, consistent with FFT alpha peak findings from ERS/ERD studies. With a streamlined protocol, these results replicate previous findings of EEG alpha and theta activation obtained during Math tasks with different signal analysis techniques and in different time frames. The efficient application to real-world, noisy EEG data with a low-resolution stochastic MP dictionary shows that this technique is very encouraging. These results provide support for studies of mathematical cognition with mobile EEG and matching pursuit.

EEG-based neurophysiological indices for expert psychomotor performance - a review

A primary objective of current human neuropsychological performance research is to define the physiological correlates of adaptive knowledge utilization, in order to support the enhanced execution of both simple and complex tasks. Within the present article, electroencephalography-based neurophysiological indices characterizing expert psychomotor performance, will be explored. As a means of characterizing fundamental processes underlying efficient psychometric performance, the neural efficiency model will be evaluated in terms of alpha-wave-based selective cortical processes. Cognitive and motor domains will initially be explored independently, which will act to encapsulate the task-related neuronal adaptive requirements for enhanced psychomotor performance associating with the neural efficiency model. Moderating variables impacting the practical application of such neuropsychological model, will also be investigated. As a result, the aim of this review is to provide insight into detectable task-related modulation involved in developed neurocognitive strategies which support heightened psychomotor performance, for the implementation within practical settings requiring a high degree of expert performance (such as sports or military operational settings).

Decomposing age effects in EEG alpha power

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.

Do we really measure what we think we are measuring?

Tests used in the empirical sciences are often (implicitly) assumed to be representative of a given research question in the sense that similar tests should lead to similar results. Here, we show that this assumption is not always valid. We illustrate our argument with the example of resting-state electroencephalogram (EEG). We used multiple analysis methods, contrary to typical EEG studies where one analysis method is used. We found, first, that many EEG features correlated significantly with cognitive tasks. However, these EEG features correlated weakly with each other. Similarly, in a second analysis, we found that many EEG features were significantly different in older compared to younger participants. When we compared these EEG features pairwise, we did not find strong correlations. In addition, EEG features predicted cognitive tasks poorly as shown by cross-validated regression analysis. We discuss several explanations of these results.

Computational analysis of speed-accuracy tradeoff

Speed-accuracy tradeoff (SAT) in the decision making of humans and animals is a well-documented phenomenon, but its underlying neuronal mechanism remains unclear. Modeling approaches have conceptualized SAT through the threshold hypothesis as adjustments to the decision threshold. However, the leading neurophysiological view is the gain modulation hypothesis. This hypothesis postulates that the SAT mechanism is implemented through changes in the dynamics of the choice circuit, which increase the baseline firing rate and the speed of neuronal integration. In this paper, I investigated alternative computational mechanisms of SAT and showed that the threshold hypothesis was qualitatively consistent with the behavioral data, but the gain modulation hypothesis was not. In order to reconcile the threshold hypothesis with the neurophysiological evidence, I considered the interference of alpha oscillations with the decision process and showed that alpha oscillations could increase the discriminatory power of the decision system, although they slowed down the decision process. This suggests that the magnitude of alpha waves suppression during the event related desynchronization (ERD) of alpha oscillations depends on a SAT condition and the amplitude of alpha oscillations is lower in the speed condition. I also showed that the lower amplitude of alpha oscillations resulted in an increase in the baseline firing rate and the speed of neuronal intergration. Thus, the interference of the event related desynchronization of alpha oscillations with a SAT condition explains why an increase in the baseline firing rate and the speed of neuronal integration accompany the speed condition.

Electroen cephalography correlates of word and non-word listening in children with specific language impairment: An observational study20F0

Auditory processing in children diagnosed with speech and language impairment (SLI) is atypical and characterized by reduced brain activation compared to typically developing (TD) children. In typical speech and language development processes, frontal, temporal, and posterior regions are engaged during single-word listening, while for non-word listening, it is highly unlikely that perceiving or speaking them is not followed by frequent neurones' activation enough to form stable network connections. This study aimed to investigate the electrophysiological cortical activity of alpha rhythm while listening words and non-words in children with SLI compared to TD children. The participants were 50 children with SLI, aged 4 to 6, and 50 age-related TD children. Groups were divided into 2 subgroups: first subgroup - children aged 4.0 to 5.0 years old (E = 25, C = 25) and second subgroup - children aged 5.0 to 6.0 years old (E = 25, C = 25). The younger children's group did not show statistically significant differences in alpha spectral power in word or non-word listening. In contrast, in the older age group for word and non-word listening, differences were present in the prefrontal, temporal, and parieto-occipital regions bilaterally. Children with SLI showed a certain lack of alpha desynchronization in word and non-word listening compared with TD children. Non-word perception arouses more brain regions because of the unknown presence of the word stimuli. The lack of adequate alpha desynchronization is consistent with established difficulties in lexical and phonological processing at the behavioral level in children with SLI.

Study of EEG characteristics while solving scientific problems with different mental effort

Studying the mental effort in problem-solving is important to the understanding of how the brain allocates cognitive resources to process information. The electroencephalogram is a promising physiological approach to assessing the online mental effort. In this study, we investigate the EEG indicators of mental effort while solving scientific problems. By manipulating the complexity of the scientific problem, the level of mental effort also changes. With the increase of mental effort, theta synchronization in the frontal region and lower alpha desynchronization in the parietal and occipital regions significantly increase. Also, upper alpha desynchronization demonstrates a widespread enhancement across the whole brain. According to the functional topography of brain activity in the theta and alpha frequency, our results suggest that the mental effort while solving scientific problems is related to working memory, visuospatial processing, semantic processing and magnitude manipulation. This study suggests the reliability of EEG to evaluate the mental effort in an educational context and provides valuable insights into improving the problem-solving abilities of students in educational practice.

Multivariate patterns and long-range temporal correlations of alpha oscillations are associated with flexible manipulation of visual working memory representations

The ability to flexibly manipulate memory representations is embedded in visual working memory (VWM) and can be tested using paradigms with retrospective cues. Although valid retrospective cues often facilitate memory recall, invalid ones may or may not result in performance costs. We investigated individual differences in utilising retrospective cues and evaluated how these individual differences are associated with brain oscillatory activity at rest. At the behavioural level, we operationalised flexibility as the ability to make effective use of retrospective cues or disregard them if required. At the neural level, we tested whether individual differences in such flexibility were associated with properties of resting-state alpha oscillatory activity (8-12 Hz). To capture distinct aspects of these brain oscillations, we evaluated their power spectral density and temporal dynamics using long-range temporal correlations (LRTCs). In addition, we performed multivariate patterns analysis (MVPA) to classify individuals' level of behavioural flexibility based on these neural measures. We observed that alpha power alone (magnitude) at rest was not associated with flexibility. However, we found that the participants' ability to manipulate VWM representations was correlated with alpha LRTC and could be decoded using MVPA on patterns of alpha power. Our findings suggest that alpha LRTC and multivariate patterns of alpha power at rest may underlie some of the individual differences in using retrospective cues in working memory tasks.

Associations between pre-stimulus alpha power, hearing level and performance in a digits-in-noise task

OBJECTIVE

Baseline electroencephalography (EEG) alpha power, i.e. that measured prior to stimulus presentation, is a potential objective predictor of task performance. Here we assessed the predictive power of EEG alpha on performance accuracy in a digits-in-noise recognition task, factoring in hearing thresholds and age.

DESIGN

EEG alpha power, recorded while participants listened to target digits presented in a noise background, was analysed during two different baseline periods: i) a pre-stimulus baseline (pre-STIM) free from any acoustic stimulus, and ii) a pre-target baseline (pre-TARG) recorded in background noise only.

STUDY SAMPLE

Eighty-five participants with either normal hearing or aided hearing impairment (age range: 55-85 years old, 42 male).

RESULTS

Hierarchical multiple regression analyses indicated that i) lower hearing thresholds and, to a lesser extent, higher pre-STIM alpha power were associated with improved performance accuracy ii) alpha power in pre-STIM and pre-TARG were highly correlated across individuals but pre-TARG alpha power was not a significant predictor of performance accuracy.

CONCLUSION

Investigations of baseline EEG alpha power as a predictor of speech-in-noise performance accuracy should control for associations between hearing thresholds and measures of EEG baseline periods.

Risk Variants in Three Alzheimer's Disease Genes Show Association with EEG Endophenotypes

Background:

Dementia due to Alzheimer’s disease (AD) is a complex neurodegenerative disorder, which much of heritability remains unexplained. At the clinical level, one of the most common physiological alterations is the slowing of oscillatory brain activity, measurable by electroencephalography (EEG). Relative power (RP) at the conventional frequency bands (i.e., delta, theta, alpha, beta-1, and beta-2) can be considered as AD endophenotypes.

Objective:

The aim of this work is to analyze the association between sixteen genes previously related with AD: APOE, PICALM, CLU, BCHE, CETP, CR1, SLC6A3, GRIN2 β, SORL1, TOMM40, GSK3 β, UNC5C, OPRD1, NAV2, HOMER2, and IL1RAP, and the slowing of the brain activity, assessed by means of RP at the aforementioned frequency bands.

Methods:

An Iberian cohort of 45 elderly controls, 45 individuals with mild cognitive impairment, and 109 AD patients in the three stages of the disease was considered. Genomic information and brain activity of each subject were analyzed.

Results:

The slowing of brain activity was observed in carriers of risk alleles in IL1RAP (rs10212109, rs9823517, rs4687150), UNC5C (rs17024131), and NAV2 (rs1425227, rs862785) genes, regardless of the disease status and situation towards the strongest risk factors: age, sex, and APOE ɛ4 presence.

Conclusion:

Endophenotypes reduce the complexity of the general phenotype and genetic variants with a major effect on those specific traits may be then identified. The found associations in this work are novel and may contribute to the comprehension of AD pathogenesis, each with a different biological role, and influencing multiple factors involved in brain physiology.

Consolidation and generalisation across sleep depend on individual EEG factors and sleep spindle density

Sleep is involved in both the consolidation of discrete episodes, as well as the generalisation of acquired memories into schemata. Here, we have isolated early versus late periods of sleep in order to replicate previous behavioural findings and to demonstrate: i) that distinct sleep and sleep electroencephalography (EEG) factors influence the generalisation of learned information, and; ii) that the consolidation and generalisation of memory across sleep depends on individual alpha frequency (IAF) and strength of initial encoding. Subjects underwent a night-half protocol with polysomnography (PSG), and completed a Chinese character-English paired associates learning task. Recognition accuracy of learned word-pairs, the extent to which the subject was able to generalise this knowledge, and the extent of explicit transfer of knowledge were measured. Results demonstrate that quality of initial learning determined the relationship between sleep neurophysiology and outcome, with IAF modulating this effect. We also note an effect of IAF in modulating the effect of sleep spindles in determining generalisation of learned materials. Finally, we note a complex relationship between initial learning, IAF and sleep spindle density in determining when information will reach explicit awareness across sleep. Together, these data implicate encoding factors in subsequent offline processing, demonstrate a potential role for individual differences in the EEG and subsequently add to our understanding of the the conditions in which sleep may benefit both memory and learning.

Alterations of Functional Connectivity During the Resting State and Their Associations With Visual Memory in College Students Who Binge Drink

This study investigated the characteristics of neural oscillation and functional connectivity (FC) in college students engaging in binge drinking (BD) using resting-state electroencephalography (EEG). Also, the associations of visual memory, evaluated by the Rey-Osterrieth Complex Figure Test (RCFT), and neural oscillation with FC during the resting state were investigated. The BD (n = 35) and non-BD (n = 35) groups were selected based on scores of the Korean version of the Alcohol use disorders (AUDs) Identification Test and the Alcohol Use Questionnaire. EEG was performed for 6 min while the participants rested with eyes closed. The theta, lower-alpha, and upper alpha powers did not differ between the BD and non-BD groups. Concerning FC, the BD group exhibited stronger theta coherence than that of the non-BD group, and in the lower and upper alpha bands, the BD group showed stronger coherence in some areas but weaker coherence in others compared with the non-BD group. However, these significant results were not observed after Bonferroni correction. The BD group showed significantly lower delayed recall scores on the RCFT than did the non-BD group. A positive correlation between the left prefrontal-parietal-occipital midline connection and performance on the delayed recall of the RCFT was observed in the BD group. The present results could suggest that binge drinkers have alterations in brain FC, which may be related to their visual memory deficits.

Electro-Encephalography and Electro-Oculography in Aeronautics: A Review Over the Last Decade (2010-2020)

Electro-encephalography (EEG) and electro-oculography (EOG) are methods of electrophysiological monitoring that have potentially fruitful applications in neuroscience, clinical exploration, the aeronautical industry, and other sectors. These methods are often the most straightforward way of evaluating brain oscillations and eye movements, as they use standard laboratory or mobile techniques. This review describes the potential of EEG and EOG systems and the application of these methods in aeronautics. For example, EEG and EOG signals can be used to design brain-computer interfaces (BCI) and to interpret brain activity, such as monitoring the mental state of a pilot in determining their workload. The main objectives of this review are to, (i) offer an in-depth review of literature on the basics of EEG and EOG and their application in aeronautics; (ii) to explore the methodology and trends of research in combined EEG-EOG studies over the last decade; and (iii) to provide methodological guidelines for beginners and experts when applying these methods in environments outside the laboratory, with a particular focus on human factors and aeronautics. The study used databases from scientific, clinical, and neural engineering fields. The review first introduces the characteristics and the application of both EEG and EOG in aeronautics, undertaking a large review of relevant literature, from early to more recent studies. We then built a novel taxonomy model that includes 150 combined EEG-EOG papers published in peer-reviewed scientific journals and conferences from January 2010 to March 2020. Several data elements were reviewed for each study (e.g., pre-processing, extracted features and performance metrics), which were then examined to uncover trends in aeronautics and summarize interesting methods from this important body of literature. Finally, the review considers the advantages and limitations of these methods as well as future challenges.

Neural Oscillations: Understanding a Neural Code of Pain

Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.

Slow electroencephalographic oscillations and behavioral measures as predictors of high executive processing in early postmenopausal females: A discriminant analysis approach

Decline in cognitive function is frequent in early postmenopause. There are postmenopausal females who show high performance while others display low performance in executive function, modulated by the prefrontal cortex. These differences have led to confusing and inconclusive results, which have not been explained entirely by the decline in estrogens, which affect the prefrontal cortex functions. An analysis of brain function and the application of a discriminant analysis can help to clarify the deficits in executive function shown by some postmenopausal females. The objective was to examine electroencephalographic recording during the performance of an executive function test in early postmenopausal females, ten with a high level of performance and ten with a low level of performance. Absolute power of delta, theta, alpha1, alpha2, beta1 and beta2 and the numbers of completed categories, trials, perseverative errors and overall errors were submitted to stepwise discriminant analysis to identify predictor variables. Four predictors emerged as significant of group membership based on cognitive performance, with the high-performance group characterized by more completed categories, more delta power, less theta power and more alpha1 power. These findings suggest that postmenopausal females classified in the high-performance group displayed appropriate temporary activation in slow oscillations during executive processing.

Integrated Development Environment for EEG-Driven Cognitive-Neuropsychological Research

Background: EEG-driven research is paramount in cognitive-neuropsychological studies, as it provides a non-invasive window to the underlying neural mechanisms of cognition and behavior. A myriad collection of software and hardware frameworks has been developed to alleviate some of the technical barriers involved in EEG-driven research. Methods: we propose an integrated development environment which encompasses the entire technical “data-collection pipeline” of cognitive-neuropsychological research, including experiment design, data acquisition, data exploration and analysis in a state-of-the-art user interface. Our framework is based on a unique integration between a python-based web framework, time-oriented databases and object-based data schemes. Results: we demonstrated our framework with the recording and analysis of an n-Back task completed by 15 elderly (ages 50 to 80) participants. This case study demonstrates the highly utilized nature of our integrated framework with a challenging target population. Furthermore, our results may provide new insights into the correlation between brain activity and working memory performance in elderly people, who are prone to experience accelerated decline in executive prefrontal cortex functioning. Conclusion: our framework extends the range of EEG-driven experimental methods for assessing cognition available for cognitive-neuroscientists, allowing them to concentrate on the creative part of their work instead of technical aspects.

Resting EEG in alpha and beta bands predicts individual differences in attentional breadth

Whether individuals focus their attention on the global level (the forest), or local elements that make up the stimulus (the trees) remains relatively stable over a period of at least 10 days in multiple global/local measures. Greater attentional approach tendencies and vigilance, which are likely reflected by lower alpha and higher beta power, are associated with narrowed attentional breadth. The current study investigated whether individual differences in the propensity for individuals to focus on the global or local levels (attentional breadth) can be predicted based on EEG power in alpha and its neighbouring frequency bands during a preceding rest period. Greater levels of posterior alpha and preponderance of alpha-to-beta power at rest were associated with greater attentional breadth during the subsequent Navon letters task. These results suggest that neural indices of attentional approach when not engaged in a goal-orientated task are associated with individual differences in attentional breadth.

Resting-state prefrontal EEG biomarkers in correlation with MMSE scores in elderly individuals

We investigated whether cognitive decline could be explained by resting-state electroencephalography (EEG) biomarkers measured in prefrontal regions that reflect the slowing of intrinsic EEG oscillations. In an aged population dwelling in a rural community (total = 496, males = 165, females = 331), we estimated the global cognitive decline using the Mini-Mental State Examination (MMSE) and measured resting-state EEG parameters at the prefrontal regions of Fp1 and Fp2 in an eyes-closed state. Using a tertile split method, the subjects were classified as T3 (MMSE 28-30, N = 162), T2 (MMSE 25-27, N = 179), or T1 (MMSE ≤ 24, N = 155). The EEG slowing biomarkers of the median frequency, peak frequency and alpha-to-theta ratio decreased as the MMSE scores decreased from T2 to T1 for both sexes (-5.19 ≤ t-value ≤ -3.41 for males and -7.24 ≤ t-value ≤ -4.43 for females) after adjusting for age and education level. Using a double cross-validation procedure, we developed a prediction model for the MMSE scores using the EEG slowing biomarkers and demographic covariates of sex, age and education level. The maximum intraclass correlation coefficient between the MMSE scores and model-predicted values was 0.757 with RMSE = 2.685. The resting-state EEG biomarkers showed significant changes in people with early cognitive decline and correlated well with the MMSE scores. Resting-state EEG slowing measured in the prefrontal regions may be useful for the screening and follow-up of global cognitive decline in elderly individuals.

Oscillatory EEG Changes During Arithmetic Learning in Children

Most studies have investigated brain activation changes after the course of arithmetic learning, and the question remains whether these changes are detectable during the course of learning, i.e., before memory consolidation. Twenty-four fifth graders solved multiplication problems while ongoing electroencephalography (EEG) was recorded. The arithmetic training revealed reduced errors together with a power increase in theta (4-7 Hz) but not in lower alpha (8-10 Hz) or upper alpha (10-13 Hz) bands. We conclude that increases in theta power subserved a shift from slow, procedural strategies to more efficient, automated procedural and retrieval strategies, which led to more efficient performance.

Human personality reflects spatio-temporal and time-frequency EEG structure

The reliable and objective assessment of intelligence and personality has been a topic of increasing interest of contemporary neuroscience and psychology. It is known that intelligence can be measured by estimating the mental speed or velocity of information processing. This is usually measured as a reaction time during elementary cognitive task processing, while personality is often assessed by means of questionnaires. On the other hand, human personality affects the way a subject accomplishes elementary cognitive tasks and, therefore, some personality features can define intelligence. It is expected that these features, as well as mental abilities in performing cognitive tasks are associated with the brain's electrical neural activity. Although several studies reported correlation between event-related potentials, mental ability and intelligence, there is a lack of information about time-frequency and spatio-temporal structures of neural activity which characterize this relation. In the present work, we analyzed human electroencephalograms (EEG) recorded during the performance of elementary cognitive tasks using the Schulte test, which is a paper-pencil based instrument for assessing elementary cognitive ability or mental speed. According to particular features found of the EEG structure, we divided the subjects into three groups. For subjects in each group, we applied the Sixteen Personality Factor Questionnaire (16PF) to assess the their personality traits. We demonstrated that each group exhibited a different score on the personality scale, such as warmth, reasoning, emotional stability and dominance. Summing up, we found a link between EEG features, mental abilities and personality traits. The obtained results can be of great interest for testing human personality to create automatized intelligent programs which combine simple tests and EEG measurements for real estimation of human personality traits and mental abilities.

Is the Theta/Beta EEG Marker for ADHD Inherently Flawed?

OBJECTIVE

Despite advances in our understanding of ADHD as a neurodevelopmental disorder, robust biomarkers are yet to be established in clinical practice. More than 40 years of electroencephalography (EEG)-based research has culminated in the recent Food and Drug Administration (FDA) approval of the theta/beta (EEG power) ratio (TBR) as a diagnostic marker of ADHD.

METHOD

This review article focuses on resting-state EEG power research in ADHD.

RESULTS

Inconsistent findings in the literature and suggestions of reduced specificity threaten the utility of TBR as a biomarker of ADHD. The use of fixed EEG bands may be a significant limitation, particularly in youth, and alternative approaches are needed.

CONCLUSION

We propose that a personalized theta-to-alpha cut point or "transition frequency" is a better frame of reference for the measurement of TBR. Such an approach is better placed to test maturational lag and cortical hypoarousal models of ADHD and may in turn have greater utility in supporting diagnosis.

Brain Networks Related to Beta Oscillatory Activity during Episodic Memory Retrieval

Evidence from fMRI has consistently located a widespread network of frontal, parietal, and temporal lobe regions during episodic retrieval. However, the temporal limitations of the fMRI methodology have made it difficult to assess the transient network dynamics by which these distributed regions coordinate activity. Recent evidence suggests that beta oscillations (17-20 Hz) are important for top-down control for memory suppression. However, the spatial limitations of the EEG methodology make it difficult to assess the relationship between these oscillatory signals and the distributed networks identified with fMRI. This study used simultaneous EEG/fMRI to identify networks related to beta oscillations during episodic retrieval. Participants studied adjectives and either imagined a scene (Place Task) or judged its pleasantness (Pleasant Task). During the recognition test, participants decided which task was performed with each word ("Old Place Task" or "Old Pleasant Task") or "New." EEG results revealed that posterior beta power was greater for new than old words. fMRI results revealed activity in a frontal, parietal network that was greater for old than new words, consistent with prior studies. Although overall beta power increases correlated with decreased activity within a predominantly parietal network, within the right dorsolateral and ventrolateral pFC, beta power correlated with BOLD activity more under conditions requiring more cognitive control and EEG/fMRI effects in the right frontal cortex correlated with BOLD activity in a frontoparietal network. Therefore, using simultaneous EEG and fMRI, the present results suggest that beta oscillations are related to postretrieval control operations in the right frontal cortex and act within a broader postretrieval control network.

Acute effects of radiofrequency electromagnetic field emitted by mobile phone on brain function

Due to its attributes, characteristics, and technological resources, the mobile phone (MP) has become one of the most commonly used communication devices. Historically, ample evidence has ruled out the substantial short-term impact of radiofrequency electromagnetic field (RF-EMF) emitted by MP on human cognitive performance. However, more recent evidence suggests potential harmful effects associated with MP EMF exposure. The aim of this review is to readdress the question of whether the effect of MP EMF exposure on brain function should be reopened. We strengthen our argument focusing on recent neuroimaging and electroencephalography studies, in order to present a more specific analysis of effects of MP EMF exposure on neurocognitive function. Several studies indicate an increase in cortical excitability and/or efficiency with EMF exposure, which appears to be more prominent in fronto-temporal regions and has been associated with faster reaction time. Cortical excitability might also underpin disruption to sleep. However, several inconsistent findings exist, and conclusions regarding adverse effects of EMF exposure are currently limited. It also should be noted that the crucial scientific question of the effect of longer-term MP EMF exposure on brain function remains unanswered and essentially unaddressed. Bioelectromagnetics. 38:329-338, 2017. © 2017 Wiley Periodicals, Inc.

EEG correlates of working memory performance in females

Background

The study investigates oscillatory brain activity during working memory (WM) tasks. The tasks employed varied in two dimensions. First, they differed in complexity from average to highly demanding. Second, we used two types of tasks, which required either only retention of stimulus set or retention and manipulation of the content. We expected to reveal EEG correlates of temporary storage and central executive components of WM and to assess their contribution to individual differences.

Results

Generally, as compared with the retention condition, manipulation of stimuli in WM was associated with distributed suppression of alpha1 activity and with the increase of the midline theta activity. Load and task dependent decrement of beta1 power was found during task performance. Beta2 power increased with the increasing WM load and did not significantly depend on the type of the task. At the level of individual differences, we found that the high performance (HP) group was characterized by higher alpha rhythm power. The HP group demonstrated task-related increment of theta power in the left anterior area and a gradual increase of theta power at midline area. In contrast, the low performance (LP) group exhibited a drop of theta power in the most challenging condition. HP group was also characterized by stronger desynchronization of beta1 rhythm over the left posterior area in the manipulation condition. In this condition, beta2 power increased in the HP group over anterior areas, but in the LP group over posterior areas.

Conclusions

WM performance is accompanied by changes in EEG in a broad frequency range from theta to higher beta bands. The most pronounced differences in oscillatory activity between individuals with high and low WM performance can be observed in the most challenging WM task.

Rhythmic Activity and Individual Variability in Recognition Memory: Theta Oscillations Correlate with Performance whereas Alpha Oscillations Correlate with ERPs

During study trials of a recognition memory task, alpha (∼10 Hz) oscillations decrease, and concurrently, theta (4–8 Hz) oscillations increase when later memory is successful versus unsuccessful (subsequent memory effect). Likewise, at test, reduced alpha and increased theta activity are associated with successful memory (retrieval success effect). Here we take an individual-differences approach to test three hypotheses about theta and alpha oscillations in verbal, old/new recognition, measuring the difference in oscillations between hit trials and miss trials. First, we test the hypothesis that theta and alpha oscillations have a moderately mutually exclusive relationship; but no support for this hypothesis was found. Second, we test the hypothesis that theta oscillations explain not only memory effects within participants, but also individual differences. Supporting this prediction, durations of theta (but not alpha) oscillations at study and at test correlated significantly with d′ across participants. Third, we test the hypothesis that theta and alpha oscillations reflect familiarity and recollection processes by comparing oscillation measures to ERPs that are implicated in familiarity and recollection. The alpha-oscillation effects correlated with some ERP measures, but inversely, suggesting that the actions of alpha oscillations on memory processes are distinct from the roles of familiarity- and recollection-linked ERP signals. The theta-oscillation measures, despite differentiating hits from misses, did not correlate with any ERP measure; thus, theta oscillations may reflect elaborative processes not tapped by recollection-related ERPs. Our findings are consistent with alpha oscillations reflecting visual inattention, which can modulate memory, and with theta oscillations supporting recognition memory in ways that complement the most commonly studied ERPs.

The relationship between baseline EEG spectra power and memory performance in older African Americans endorsing cognitive concerns in a community setting

The finding that some older individuals report declines in aspects of cognitive functioning is becoming a frequently used criteria to identify elderly at risk for mild cognitive impairment (MCI) and dementia. Once concerns are identified in a community setting, however, effective means are necessary to pinpoint those individuals who should go on to more complex and costly diagnostic evaluations (e.g., functional imaging). We tested 44 African American volunteers endorsing cognitive concerns (37 females, 7 males) age≥65years with CogState battery subtests and recorded resting-state EEG, with eyes closed. After current source density (CSD) transformations of EEG recordings we obtained spectral power for delta, theta, alpha, and beta frequency bands. We characterized CogState One Card Back Learning (OCL, memory) with diffusion model parameters drift rate, boundary and non-decision time (NDT). Forward regression models showed that lower OCL drift rate, slower accumulation of information needed for decision making was linked to increased absolute and relative delta at occipital region. Lower drift rate was also linked to decrease in OCL theta power at parietal region, with no findings for ONB. Results show that cortical resting, eyes closed EEG rhythms are related to memory in African American seniors endorsing cognitive concerns. This study further supports the use of EEG as an easily accessible, cost-effective, culture-fair, and noninvasive clinical measurement that could provide potentially reliable diagnostic (and perhaps prognostic) information to differentiate at-risk from stable African American seniors.

Evidence for genetic regulation of the human parieto-occipital 10-Hz rhythmic activity

Several functional and morphological brain measures are partly under genetic control. The identification of direct links between neuroimaging signals and corresponding genetic factors can reveal cellular-level mechanisms behind the measured macroscopic signals and contribute to the use of imaging signals as probes of genetic function. To uncover possible genetic determinants of the most prominent brain signal oscillation, the parieto-occipital 10-Hz alpha rhythm, we measured spontaneous brain activity with magnetoencephalography in 210 healthy siblings while the subjects were resting, with eyes closed and open. The reactivity of the alpha rhythm was quantified from the difference spectra between the two conditions. We focused on three measures: peak frequency, peak amplitude and the width of the main spectral peak. In accordance with earlier electroencephalography studies, spectral peak amplitude was highly heritable (h(2)  > 0.75). Variance component-based analysis of 28 000 single-nucleotide polymorphism markers revealed linkage for both the width and the amplitude of the spectral peak. The strongest linkage was detected for the width of the spectral peak over the left parieto-occipital cortex on chromosome 10 (LOD = 2.814, nominal P < 0.03). This genomic region contains several functionally plausible genes, including GRID1 and ATAD1 that regulate glutamate receptor channels mediating synaptic transmission, NRG3 with functions in brain development and HRT7 involved in the serotonergic system and circadian rhythm. Our data suggest that the alpha oscillation is in part genetically regulated, and that it may be possible to identify its regulators by genetic analyses on a realistically modest number of samples.

Resting EEG and ERPs findings in methadone-substituted opiate users: a review

Methadone has been used to treat opiate dependence since the mid-1960s. Despite its clinical effectiveness there is evidence from neuropsychological studies demonstrating that its long-term use might have negative effects on cognition. Nevertheless, it remains uncertain whether the observed cognitive impairments in patients undertaking methadone maintenance treatment (MMT) are solely attributable to the pharmacological effects of methadone, as suggested by some researchers. Determining the effects of MMT on neuropsychological function using electroencephalography (EEG) combined with event-related potentials (ERP) has been used infrequently. However EEG and ERP provide a means of closely examining information processing to determine whether MMT induces any deficits. The purpose of this review was to investigate whether psychophysiological evidence supports cognitive impairment in association with MMT by focusing on research using EEG and ERPs. The findings of EEG studies to date appear not support the notion that cognitive impairments are attributable to the specific pharmacological effects of methadone suggested by some neuropsychological studies. However, due to the methodological deficits and limited number of the studies, any conclusion based on the findings of the existing EEG studies should be avoided.

Observing videos of a baby crying or smiling induces similar, but not identical, electroencephalographic responses in biological and adoptive mothers

It is well-known that adoptive mothers respond to cues from their babies in similar ways to biological mothers, and that cortical processing is critical for adequate motive-emotional maternal responses. This study used electroencephalographic activity (EEG) to characterize prefrontal, parietal and temporal functioning in biological mothers (BM), adoptive mothers (AM), and non-mothers (NM), while viewing videos of a baby smiling or crying. The BM presented higher absolute power (AP) in the delta and theta bands (associated with pleasant, positive emotional experiences) in the frontal and parietal areas under all conditions. In response to the smiling video, both types of mothers presented a lower AP in alpha1 in the three cortices (indicative of increased attention) and, mainly in temporal areas, a higher AP in the fast frequencies (beta and gamma, reflecting increased alertness to sensory stimuli and cognitive processing). This EEG pattern in the BM and AM could reflect the greater attention and, probably, the positive mood caused by the smiling video, showing that both are sensitive to these pleasant stimuli. When viewing the video of a baby crying, the AM had higher AP in the fast frequencies (temporal and parietal areas), indicating that they were more reactive to this unpleasant video, while the NM presented only a lower AP in alpha1 in all cortices, a finding that could be associated with the general activation induced by these unpleasant stimuli as a consequence of their lack of maternal experience. These findings should help improve our understanding of the neural mechanisms involved in the processing of sensorial stimuli that establish affective-emotional links during motherhood.

Extracting latent brain states--Towards true labels in cognitive neuroscience experiments

Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the threshold of perception is measured, the error distribution deviates from uniformity due to the structure in the underlying experimental set-up. When we base our analysis on the behavioral labels as usually done, then we ignore this problem of systematic and structured (non-uniform) label noise and are likely to arrive at wrong conclusions in our data analysis. This paper contributes a remedy to this important scenario: we present a novel approach for a) measuring label noise and b) removing structured label noise. We demonstrate its usefulness for EEG data analysis using a standard d2 test for visual attention (N=20 participants).

Oscillatory theta activity during memory formation and its impact on overnight consolidation: a missing link?

Sleep has been shown to promote memory consolidation driven by certain oscillatory patterns, such as sleep spindles. However, sleep does not consolidate all newly encoded information uniformly but rather "selects" certain memories for consolidation. It is assumed that such selection depends on salience tags attached to the new memories before sleep. However, little is known about the underlying neuronal processes reflecting presleep memory tagging. The current study sought to address the question of whether event-related changes in spectral theta power (theta ERSP) during presleep memory formation could reflect memory tagging that influences subsequent consolidation during sleep. Twenty-four participants memorized 160 word pairs before sleep; in a separate laboratory visit, they performed a nonlearning control task. Memory performance was tested twice, directly before and after 8 hr of sleep. Results indicate that participants who improved their memory performance overnight displayed stronger theta ERSP during the memory task in comparison with the control task. They also displayed stronger memory task-related increases in fast sleep spindle activity. Furthermore, presleep theta activity was directly linked to fast sleep spindle activity, indicating that processes during memory formation might indeed reflect memory tagging that influences subsequent consolidation during sleep. Interestingly, our results further indicate that the suggested relation between sleep spindles and overnight performance change is not as direct as once believed. Rather, it appears to be mediated by processes beginning during presleep memory formation. We conclude that theta ERSP during presleep memory formation reflects cortico-hippocampal interactions that lead to a better long-term accessibility by tagging memories for sleep spindle-related reprocessing.

Identifying Granger causal relationships between neural power dynamics and variables of interest

Power modulations of oscillations in electro- and magnetoencephalographic (EEG/MEG) signals have been linked to a wide range of brain functions. To date, most of the evidence is obtained by correlating bandpower fluctuations to specific target variables such as reaction times or task ratings, while the causal links between oscillatory activity and behavior remain less clear. Here, we propose to identify causal relationships by the statistical concept of Granger causality, and we investigate which methods are bests suited to reveal Granger causal links between the power of brain oscillations and experimental variables. As an alternative to testing such causal links on the sensor level, we propose to linearly combine the information contained in each sensor in order to create virtual channels, corresponding to estimates of underlying brain oscillations, the Granger-causal relations of which may be assessed. Such linear combinations of sensor can be given by source separation methods such as, for example, Independent Component Analysis (ICA) or by the recently developed Source Power Correlation (SPoC) method. Here we compare Granger causal analysis on power dynamics obtained from i) sensor directly, ii) spatial filtering methods that do not optimize for Granger causality (ICA and SPoC), and iii) a method that directly optimizes spatial filters to extract sources the power dynamics of which maximally Granger causes a given target variable. We refer to this method as Granger Causal Power Analysis (GrangerCPA). Using both simulated and real EEG recordings, we find that computing Granger causality on channel-wise spectral power suffers from a poor signal-to-noise ratio due to volume conduction, while all three multivariate approaches alleviate this issue. In real EEG recordings from subjects performing self-paced foot movements, all three multivariate methods identify neural oscillations with motor-related patterns at a similar performance level. In an auditory perception task, the application of GrangerCPA reveals significant Granger-causal links between alpha oscillations and reaction times in more subjects compared to conventional methods.

The effects of theta transcranial alternating current stimulation (tACS) on fluid intelligence

The objective of the study was to explore the influence of transcranial alternating current stimulation (tACS) on resting brain activity and on measures of fluid intelligence. Theta tACS was applied to the left parietal and left frontal brain areas of healthy participants after which resting electroencephalogram (EEG) data was recorded. Following sham/active stimulation, the participants solved two tests of fluid intelligence while their EEG was recorded. The results showed that active theta tACS affected spectral power in theta and alpha frequency bands. In addition, active theta tACS improved performance on tests of fluid intelligence. This influence was more pronounced in the group of participants that received stimulation to the left parietal area than in the group of participants that received stimulation to the left frontal area. Left parietal tACS increased performance on the difficult test items of both tests (RAPM and PF&C) whereas left frontal tACS increased performance only on the easy test items of one test (RAPM). The observed behavioral tACS influences were also accompanied by changes in neuroelectric activity. The behavioral and neuroelectric data tentatively support the P-FIT neurobiological model of intelligence.

Encoding-related EEG oscillations during memory formation are modulated by mood state

Mood states have a strong impact on how we process incoming information. It has been proposed that positive mood facilitates elaborative, relational encoding, whereas negative mood promotes a more careful, stimulus-driven encoding style. Previous electrophysiological studies have linked successful information encoding to power increases in slow (<8 Hz) delta/theta and fast (>30 Hz) gamma oscillations, as well as to power decreases in midrange (8-30 Hz) alpha/beta oscillations. Whether different mood states modulate encoding-related oscillations has not been investigated yet. In order to address this question, we used an experimental mood induction procedure and recorded electroencephalograms from 20 healthy participants while they performed a free recall memory task after positive and negative mood induction. We found distinct oscillatory patterns in positive and negative mood. Successful encoding in positive mood was accompanied by widespread power increases in the delta band, whereas encoding success in negative mood was specifically accompanied by frontal power decreases in the beta band. On the behavioral level, memory performance was enhanced in positive mood. Our findings show that mood differentially modulates the neural correlates of successful information encoding and thus contribute to an understanding of how mood shapes different processing styles.

Robust, automatic real-time monitoring of the time course of the individual alpha frequency in the time and frequency domain

We analyzed three different approaches to automatic real-time monitoring of the time course of individual alpha frequencies (IAFs) of the human electro-encephalograms. Fast Fourier transform and wavelet transform were compared to classical automated cycle counting in the time domain. With fast Fourier and wavelet transform, test results with healthy adult subjects, demented and psychiatric patients revealed typical short-term variations of the instantaneous IAFs of about ± 2 Hz. When cycles were counted in the time domain, however, variations of only ± 1 Hz were recorded. Thus, IAF measurement in the time domain appears to be particularly suitable. We also observed long-term IAF trends that typically amounted to about ± 0.5 to ± 1.0 Hz. Therefore, our hypothesis is that the IAF does not constitute an intra-individual constant but varies with time and cognitive state. Our fully automatic real-time signal-processing procedure includes pre-processing for artifact detection and for localization of segments with synchronized alpha oscillations where the IAF should preferably be measured.

Resting-state neuronal oscillatory correlates of working memory performance

Purpose

Working memory (WM) represents the brain’s ability to maintain information in a readily available state for short periods of time. This study examines the resting-state cortical activity patterns that are most associated with performance on a difficult working-memory task.

Methods

Magnetoencephalographic (MEG) band-passed (delta/theta (1–7 Hz), alpha (8–13 Hz), beta (14–30 Hz)) and sensor based regional power was collected in a population of adult men (18–28 yrs, n = 24) in both an eyes-closed and eyes-open resting state. The normalized power within each resting state condition as well as the normalized change in power between eyes closed and open (zECO) were correlated with performance on a WM task. The regional and band-limited measures that were most associated with performance were then combined using singular value decomposition (SVD) to determine the degree to which zECO power was associated with performance on the three-back verbal WM task.

Results

Changes in power from eyes closed to open revealed a significant decrease in power in all band-widths that was most pronounced in the posterior brain regions (delta/theta band). zECO right posterior frontal and parietal cortex delta/theta power were found to be inversely correlated with three-back working memory performance. The SVD evaluation of the most correlated zECO metrics then provided a singular measure that was highly correlated with three-back performance (r = −0.73, p<0.0001).

Conclusion

Our results indicate that there is an association between WM performance and changes in resting-state power (right posterior frontal and parietal delta/theta power). Moreover, an SVD of the most associated zECO measures produces a composite resting-state metric of regional neural oscillatory power that has an improved association with WM performance. To our knowledge, this is the first investigation that has found that changes in resting state electromagnetic neural patterns are highly associated with verbal working memory performance.

Coupling between resting cerebral perfusion and EEG

While several studies have investigated interactions between the electroencephalography (EEG) and functional magnetic resonance imaging BOLD signal fluctuations, less is known about the associations between EEG oscillations and baseline brain haemodynamics, and few studies have examined the link between EEG power outside the alpha band and baseline perfusion. Here we compare whole-brain arterial spin labelling perfusion MRI and EEG in a group of healthy adults (n = 16, ten females, median age: 27 years, range 21-48) during an eyes closed rest condition. Correlations emerged between perfusion and global average EEG power in low (delta: 2-4 Hz and theta: 4-7 Hz), middle (alpha: 8-13 Hz), and high (beta: 13-30 Hz and gamma: 30-45 Hz) frequency bands in both cortical and sub-cortical regions. The correlations were predominately positive in middle and high-frequency bands, and negative in delta. In addition, central alpha frequency positively correlated with perfusion in a network of brain regions associated with the modulation of attention and preparedness for external input, and central theta frequency correlated negatively with a widespread network of cortical regions. These results indicate that the coupling between average EEG power/frequency and local cerebral blood flow varies in a frequency specific manner. Our results are consistent with longstanding concepts that decreasing EEG frequencies which in general map onto decreasing levels of activation.