The Relationship of Cognitive Performance and the Theta-Alpha Power Ratio Is Age-Dependent: An EEG Study of Short Term Memory and Reasoning during Task and Resting-State in Healthy Young and Old Adults

Frontal Activity of Recent Suicide Attempters: EEG spectrum Power Performing Raven Task

Background: Deficits in problem-solving may be related to vulnerability to suicidal behavior. We aimed to identify the electroencephalographic (EEG) power spectrum associated with the performance of the Raven as a reasoning/problem-solving task among individuals with recent suicide attempts. Methods: This study with the case-control method, consisted of 61 participants who were assigned to three groups: Suicide attempt + Major Depressive Disorder (SA + MDD), Major Depressive Disorder (MDD), and Healthy Control (HC). All participants underwent clinical evaluations and problem-solving abilities. Subsequently, EEG signals were recorded while performing the Raven task. Results: The SA + MDD and MDD groups were significantly different from the HC group in terms of anxiety, reasons for life, and hopelessness. Regarding brain oscillations in performing the raven task, increased theta, gamma, and betha power extending over the frontal areas, including anterior prefrontal cortex, dlPFC, pre-SMA, inferior frontal cortex, and medial prefrontal cortex, was significant in SA + MDD compared with other groups. The alpha wave was more prominent in the left frontal, particularly in dlPFC in SA + MDD. Compared to the MDD group, the SA + MDD group had a shorter reaction time, while their response accuracy did not differ significantly. Conclusions: Suicidal patients have more frontal activity in planning and executive function than the two other groups. Nevertheless, it seems that reduced activity in the left frontal region, which plays a crucial role in managing emotional distress, can contribute to suicidal tendencies among vulnerable individuals. Limitation The small sample size and chosen difficult trials for the Raven task were the most limitations of the study.

Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG metrics. Last, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitatory-inhibitory balance, highlighting the explanatory power of our framework to generate mechanistic insights.

Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral-Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals

BACKGROUND/OBJECTIVES

The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. Traditionally, research in this domain has focused primarily on binary classifications of disorders. This study aims to classify five distinct states, including one control group and four categories of psychological disorders.

METHODS

Our investigation will utilize algorithms based on Granger causality and local graph structures to improve classification accuracy. Feature extraction from connectivity matrices was performed using local structure graphs. The extracted features were subsequently classified employing K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes classifiers.

RESULTS

The KNN classifier demonstrated the highest accuracy in the gamma band for the depression category, achieving an accuracy of 89.36%, a sensitivity of 89.57%, an F1 score of 94.30%, and a precision of 99.90%. Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band.

CONCLUSIONS

The proposed methodology provides a novel approach for analyzing EEG signals and holds potential applications in the classification of psychological disorders.

Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomes

Engaging in phone conversations or other cognitively challenging tasks while driving detrimentally impacts cognitive functions and has been associated with increased risk of accidents. Existing EEG methods have been shown to differentiate between load and no load, but not between different levels of cognitive load. Furthermore, it has not been investigated whether EEG measurements of load can be used to predict safety outcomes in critical events. EEG microstates analysis, categorizing EEG signals into a concise set of prototypical functional states, has been used in other task contexts with good results, but has not been applied in the driving context. Here, this gap is addressed by means of a driving simulation experiment. Three phone use conditions (no phone use, hands-free, and handheld), combined with two task difficulty levels (single- or double-digit addition and subtraction), were tested before and during a rear-end collision conflict. Both conventional EEG spectral power and EEG microstates were analyzed. The results showed that different levels of cognitive load influenced EEG microstates differently, while EEG spectral power remained unaffected. A distinct EEG pattern emerged when drivers engaged in phone tasks while driving, characterized by a simultaneous increase and decrease in two of the EEG microstates, suggesting a heightened focus on auditory information, potentially at a cost to attention reorientation ability. The increase and decrease in these two microstates follow a monotonic sequence from baseline to hands-free simple, hands-free complex, handheld simple, and finally handheld complex, showing sensitivity to task difficulty. This pattern was found both before and after the lead vehicle braked. Furthermore, EEG microstates prior to the lead vehicle braking improved predictions of safety outcomes in terms of minimum time headway after the lead vehicle braked, clearly suggesting that these microstates measure brain states which are indicative of impaired driving. Additionally, EEG microstates are more predictive of safety outcomes than task difficulty, highlighting individual differences in task effects. These findings enhance our understanding of the neural dynamics involved in distracted driving and can be used in methods for evaluating the cognitive load induced by in-vehicle systems.

The relationship between worry and academic performance: examining the moderating role of attention control

BACKGROUND

Worry is frequently associated with reduced cognitive performance, through consumption of attention control resources. Assessing attention control during acute worry may better reflect cognitive performance in real-world scenarios. This study examined whether attention control (assessed at rest and under acute worry) moderates the relationship between worry and academic performance.

METHODS

Worry (Penn State Worry Questionnaire) and academic performance (examination grades) were assessed in 87 undergraduates, with attention control (antisaccade performance) measured at baseline and following worry induction.

RESULTS

When assessed at rest, attention control did not moderate the relationship between trait worry and academic performance. However, under acute worry, attention control significantly moderated the relationship between worry and academic performance (p = .05, f2 = 0.14), such that at low levels of attention control under worry, higher trait worry was significantly associated with lower academic performance. At high levels of attention control under worry, however, the relationship between trait worry and academic performance was not significant.

CONCLUSIONS

Findings suggest that worry may shape performance according to attention control levels, with attention control's moderating role being more pronounced under conditions of acute worry. These results provide preliminary evidence that attention control assessed under worry may better predict real-world performance, compared to assessment at rest.

Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer's disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.

Complexity measures reveal age-dependent changes in electroencephalogram during working memory task

The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.

Brain health in diverse settings: How age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Role of visual brainwave entrainment on the resting state brainwaves of children with and without attention-deficit/hyperactivity disorder

The relationship between brainwave oscillations and Attention-Deficit/Hyperactivity Disorder (ADHD)-related cognitive challenges is a trending proposition in the field of Cognitive Neuroscience. Studies suggest the role of brainwave oscillations in the symptom expressions of ADHD-diagnosed children. Intervention studies have further suggested the scope of brain stimulation techniques in improving cognition. The current manuscript explored the effect of changes in the brainwaves post-sensory entrainment on cognitive performance of children. We calculated each participant's brainwave difference and ratios of theta, alpha, and beta power after the entrainment sessions. Further, we explored possible correlations between these values and the psychometric scores. The beta resting state showed the strongest association with selective attention performance of all participants. Theta-beta ratio (TBR) showed an inverse correlation with selective attention and working memory performances. The theta frequency was associated with decreased working performance in children without ADHD. Our findings also suggest a predominant role of TBR than the theta-alpha ratio in determining the cognitive performance of children with ADHD. The individual differences in the entrainment reception were attributed to the participant's age, IQ, and their innate baseline frequencies. The implications of our findings can initiate substantiating brainwave-based entrainment sessions as a therapeutic modality to improve cognition among children.

Differences in scalp-to-cortex tissues across age groups, sexes and brain regions: Implications for neuroimaging and brain stimulation techniques

Aging affects the scalp-to-cortex distance (SCD) and the comprising tissues. This is crucial for noninvasive neuroimaging and brain stimulation modalities as they rely on traversing from the scalp to the cortex or vice versa. The specific relationship between aging and these tissues has not been comprehensively investigated. We conducted a study on 250 younger and older adults to examine age-related differences in SCD and its constituent tissues. We identified region-specific differences in tissue thicknesses related to age and sex. Older adults exhibit larger SCD in the frontocentral regions compared to younger adults. Men exhibit greater SCD in the inferior scalp regions, while women show similar-to-greater SCD values in regions closer to the vertex compared to men. Younger adults and men have thicker soft tissue layers, whereas women and older adults exhibit thicker compact bone layers. CSF is considerably thicker in older adults, particularly in men. These findings emphasize the need to consider age, sex, and regional differences when interpreting SCD and its implications for noninvasive neuroimaging and brain stimulation.

The effect of vitamin D supplementation on the brain mapping and behavioral performance of children with ADHD: a double-blinded randomized controlled trials

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental diseases that are accompanied with EEG pattern changes and Low levels of 25-hydroxyvitamin D [25(OH)D]. Neurofeedback provides a feedback signal to alleviate brain wave abnormalities and offers an alternative therapy for ADHD. This study aimed to investigate the concomitant effects of Vitamin D3 supplementation and Neurofeedback on children with ADHD.

METHOD

This study was implemented on children with an established diagnosis of ADHD who received multisession Neurofeedback therapy. The intervention and control groups received 50000 IU vitamin D3 capsules and placebo respectively once a week for 2 months. The background rhythm was measured using quantitative EEG both before and at the end duration of the therapy.

RESULTS

All of the vitamin D3 treated children showed a significant increase in the 25(OH)D (46 ± 18, 28 ± 10 (ng/ml), p = 0.001) and serum calcium level (9.5 ± 0.5, 9.8 ± 0.3 (mg/dl), p = 0.003) compared to the baseline. There were a statistically significant decrease in the treatment group about theta relative power, theta/beta, and theta/alpha power ratios within two eyes conditions (p = 0.004). All the changes were significant within eye open state in the treatment group (2.4 ± 1.2, 1.7 ± 0.5, p = 0.01). There is a significant relationship between Connors scores and some brain waves improvement (in relative theta (r = 0.998) and theta-to-beta power difference score (r = 0.56) (p < 0.001).

CONCLUSION

Concomitant use of vitamin D3 supplementation and neurofeedback, increases the serum level of this vitamin and reveal favorable electrophysiological results in children with ADHD.Trial registration: Iranian Registry of Clinical Trials identifier: IRCT20200922048802N1..

Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research

The theta band is one of the most prominent frequency bands in the electroencephalography (EEG) power spectrum and presents an interesting paradox: while elevated theta power during resting state is linked to lower cognitive abilities in children and adolescents, increased theta power during cognitive tasks is associated with higher cognitive performance. Why does theta power, measured during resting state versus cognitive tasks, show differential correlations with cognitive functioning? This review provides an integrated account of the functional correlates of theta across different contexts. We first present evidence that higher theta power during resting state is correlated with lower executive functioning, attentional abilities, language skills, and IQ. Next, we review research showing that theta power increases during memory, attention, and cognitive control, and that higher theta power during these processes is correlated with better performance. Finally, we discuss potential explanations for the differential correlations between resting/task-related theta and cognitive functioning, and offer suggestions for future research in this area.

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer's disease (AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of AD and other forms of dementia. Electroencephalography (EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial for identifying MCI as they capture neuronal activities and connectivity patterns linked to cognitive functions. However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis. To address this challenge, a novel framework has been developed for identifying important frequency sub-bands within EEG signals for MCI detection. In the proposed scheme, the signals are first denoised using a stationary wavelet transformation and segmented into small time frames. Then, four frequency sub-bands are extracted from each segment, and spectrogram images are generated for each sub-band as well as for the full filtered frequency band signal segments. This process produces five different sets of images for five separate frequency bands. Afterwards, a convolutional neural network is used individually on those image sets to perform the classification task. Finally, the obtained results for the tested four sub-bands are compared with the results obtained using the full bandwidth. Our proposed framework was tested on two MCI datasets, and the results indicate that the 16-32 Hz sub-band range has the greatest impact on MCI detection, followed by 4-8 Hz. Furthermore, our framework, utilizing the full frequency band, outperformed existing state-of-the-art methods, indicating its potential for developing diagnostic tools for MCI detection.

Resting State Electrophysiological Profiles and Their Relationship with Cognitive Performance in Cognitively Unimpaired Older Adults: A Systematic Review

Background

Aging is a complex and natural process. The physiological decline related to aging is accompanied by a slowdown in cognitive processes, which begins shortly after individuals reach maturity. These changes have been sometimes interpreted as a compensatory sign and others as a fingerprint of deterioration.

Objective

In this context, our aim is to uncover the mechanisms that underlie and support normal cognitive functioning in the brain during the later stages of life.

Methods

With this purpose, a systematic literature search was conducted using PubMed, Scopus, and Web of Science databases, which identified 781 potential articles. After applying inclusion and exclusion criteria, we selected 12 studies that examined the brain oscillations patterns in resting-state conditions associated with cognitive performance in cognitively unimpaired older adults.

Results

Although cognitive healthy aging was characterized differently across studies, and various approaches to analyzing brain activity were employed, our review indicates a relationship between alpha peak frequency (APF) and improved performance in neuropsychological scores among cognitively unimpaired older adults.

Conclusions

A higher APF is linked with a higher score in intelligence, executive function, and general cognitive performance, and could be considered an optimal, and easy-to-assess, electrophysiological marker of cognitive health in older adults.

Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences. In this work, we present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas, from single-subject time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitation-inhibition balance, highlighting the explanatory power of our framework in generating mechanistic insights.

A Combination of Caffeine, TeaCrine, and Dynamine Improves the Neurophysiological and Performance Measures of Electronic (E)-Gamers

INTRODUCTION

Video games require precise motor skills, quick reaction times, and cognitive engagement. The tremendous growth of the electronic (e)-gaming industry has increased the demands for cognitive supplements (e.g., nootropics) to help e-athletes gain a competitive edge. The primary aim of this study was to evaluate the effects of combined caffeine + TeaCrine + Dynamine measures of neurophysiological and first-person shooter game performance in e-gamers.

METHODS

Using a randomized double-blinded, crossover design, we assessed the effects of an acute, single-dose treatment of caffeine (200 mg) vs. caffeine (200 mg) + TeaCrine (10 mg) + Dynamine (50 mg) (CTD) vs. Ppacebo (maltodextrin). Each participant was tested under all three conditions one week apart. Baseline and post-dose measures were tested one hour apart. Participants [n = 49 male (24.4 ±, 4.5 yr)] were amateur e-gamers who play a first-person video game for at least 10 hours/week. Gaming performance was assessed through a series of first-person shooter training games through AIMLAB (State Space Labs, Inc., New York, USA). These included Reflex Shot (RS) standard, speed, and precision. The neurophysiological activity was captured while participants played three games through a single-channel EEG.

RESULTS

In the standard game, the caffeine and the CTD conditions shot significantly more targets relative to the placebo, and both caffeine and the CTD condition had significantly greater targets post-dose compared to pre-dose. However, both the placebo and caffeine conditions had significantly slower reaction times post-dose compared to pre-dose. In the speed game, both the caffeine and placebo conditions shot a significantly greater number of targets, while the placebo and caffeine conditions had significantly more shots post-dose compared to pre-dose. Only the CTD condition had a significant increase in total kills post-dose compared to pre-dose. In the precision game, only the CTD condition significantly improved the number of kills per second post-dose, while only the caffeine condition had more shots post-dose. EEG data collected concomitantly with game playing showed that the CTD condition resulted in significantly lower alpha power compared to the placebo condition. The CTD group also showed increased theta activity post-dose during game playing compared to both the placebo caffeine conditions.

CONCLUSION

CTD appears to improve overall shooting gaming performance and neurophysiological measures of cognitive activity compared to caffeine and placebo. Collectively, these findings suggest that CTD assists with speed-accuracy tradeoffs where caffeine-only can lead to erratic play; thus, CTD may be particularly beneficial for shooting precision. The EEG data support this notion since the CTD exhibited lower alpha power suggesting increased cognitive flexibility and arousal and higher theta power suggesting greater cognitive control and decision-making under pressure.

An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding

Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. Previous studies on binary EEG pathology decoding have mainly used the Temple University Hospital (TUH) Abnormal EEG Corpus (TUAB) which contains approximately 3,000 manually labelled EEG recordings. To evaluate and eventually even improve the generalisation performance of machine learning methods for EEG pathology, decoding larger, publicly available datasets is required. A number of studies addressed the automatic labelling of large open-source datasets as an approach to create new datasets for EEG pathology decoding, but little is known about the extent to which training on larger, automatically labelled dataset affects decoding performances of established deep neural networks. In this study, we automatically created additional pathology labels for the Temple University Hospital (TUH) EEG Corpus (TUEG) based on the medical reports using a rule-based text classifier. We generated a dataset of 15,300 newly labelled recordings, which we call the TUH Abnormal Expansion EEG Corpus (TUABEX), and which is five times larger than the TUAB. Since the TUABEX contains more pathological (75%) than non-pathological (25%) recordings, we then selected a balanced subset of 8,879 recordings, the TUH Abnormal Expansion Balanced EEG Corpus (TUABEXB). To investigate how training on a larger, automatically labelled dataset affects the decoding performance of deep neural networks, we applied four established deep convolutional neural networks (ConvNets) to the task of pathological versus non-pathological classification and compared the performance of each architecture after training on different datasets. The results show that training on the automatically labelled TUABEXB dataset rather than training on the manually labelled TUAB dataset increases accuracies on TUABEXB and even for TUAB itself for some architectures. We argue that automatically labelling of large open-source datasets can be used to efficiently utilise the massive amount of EEG data stored in clinical archives. We make the proposed TUABEXB available open source and thus offer a new dataset for EEG machine learning research.

Neuropsychological Evaluation and Quantitative EEG in Patients with Frontotemporal Dementia, Alzheimer's Disease, and Mild Cognitive Impairment

This study analyzed the efficacy of EEG resting state and neuropsychological performances in discriminating patients with different forms of dementia, or mild cognitive impairment (MCI), compared with control subjects. Forty-four patients with dementia (nineteen patients with AD, and seven with FTD), eighteen with MCI, and nineteen healthy subjects, matched for age and gender, underwent an extensive neuropsychological test battery and an EEG resting state recording. Results showed greater theta activation in posterior areas in the Alzheimer's disease (AD) and Fronto-Temporal Dementia (FTD) groups compared with the MCI and control groups. AD patients also showed more delta band activity in the temporal-occipital areas than controls and MCI patients. By contrast, the alpha and beta bands did not discriminate among groups. A hierarchical clustering analysis based on neuropsychological and EEG data yielded a three-factor solution. The clusters differed for several neuropsychological measures, as well as for beta and theta bands. Neuropsychological tests were most sensitive in capturing an initial cognitive decline, while increased theta activity was uniquely associated with a substantial worsening of the clinical picture, representing a negative prognostic factor. In line with the Research Domains Framework (RDoC) perspective, the joint use of cognitive and neurophysiological data may provide converging evidence to document the evolution of cognitive skills in at-risk individuals.

Quantitative assessment of cognitive profile and brain asymmetry in the characterization of autism spectrum in children: A task-based EEG study

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.

Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.

Motivation in the metaverse: A dual-process approach to consumer choices in a virtual reality supermarket

Introduction

Consumer decision-making processes involve a complex interrelation between perception, emotion, and cognition. Despite a vast and diverse literature, little effort has been invested in investigating the neural mechanism behind such processes.

Methods

In the present work, our interest was to investigate whether asymmetrical activation of the frontal lobe of the brain could help to characterize consumer's choices. To obtain stronger experimental control, we devised an experiment in a virtual reality retail store, while simultaneously recording participant brain responses using electroencephalogram (EEG). During the virtual store test, participants completed two tasks; first, to choose items from a predefined shopping list, a phase we termed as "planned purchase". Second, subjects were instructed that they could also choose products that were not on the list, which we labeled as "unplanned purchase." We assumed that the planned purchases were associated with a stronger cognitive engagement, and the second task was more reliant on immediate emotional responses.

Results

By analyzing the EEG data based on frontal asymmetry measures, we find that frontal asymmetry in the gamma band reflected the distinction between planned and unplanned decisions, where unplanned purchases were accompanied by stronger asymmetry deflections (relative frontal left activity was higher). In addition, frontal asymmetry in the alpha, beta, and gamma ranges illustrate clear differences between choices and no-choices periods during the shopping tasks.

Discussion

These results are discussed in light of the distinction between planned and unplanned purchase in consumer situations, how this is reflected in the relative cognitive and emotional brain responses, and more generally how this can influence research in the emerging area of virtual and augmented shopping.

Quantitative electroencephalographic analysis of delirium tremens development following alcohol-withdrawal seizure based on a small number of male cases

INTRODUCTION

Seizures and delirium tremens (DTs) are recognized as severe alcohol-withdrawal symptoms. Prolonged admission and serious complications associated with alcohol withdrawal are responsible for increased costs and use of medical and social resources. This study investigated the predictive value of quantitative electroencephalography (QEEG) for developing alcohol-related DTs after alcohol-withdrawal seizure (AWS).

METHODS

We compared differences in QEEG in patients after AWS (n = 13). QEEG was performed in the intensive care unit within 48 h of admission, including in age- and sex-matched healthy controls. We also investigated the prognostic value of QEEG for the development of alcohol DTs after AWS in a retrospective, case-control study. The spectral power of each band frequency and the ratio of the theta to alpha band (TAR) in the electroencephalogram were analyzed using iSyncBrain^® (iMediSync, Inc., Korea).

RESULTS

The beta frequency and the alpha frequency band power were significantly higher and lower, respectively, in patients than in age- and sex-matched healthy controls. In AWS patients with DTs, the relative beta-3 power was lower, particularly in the left frontal area, and the TAR was significantly higher in the central channel than in those without DTs.

CONCLUSION

Quantitative EEG showed neuronal excitability and decreased cognitive activities characteristic of AWS associated with alcohol-withdrawal state, and we demonstrated that quantitative EEG might be a helpful tool for detecting patients at a high risk of developing DTs during an alcohol-dependence period.

Understanding creativity process through electroencephalography measurement on creativity-related cognitive factors

Introduction

Neurotechnology approaches, such as electroencephalography (EEG), can aid understanding of the cognitive processes behind creativity.

Methods

To identify and compare the EEG characteristics of creativity-related cognitive factors (remote association, common association, combination, recall, and retrieval), 30 participants were recruited to conduct an EEG induction study.

Results

From the event-related potential (ERP) results and spectral analysis, the study supports that creativity is related to the frontal lobe areas of the brain and common association is an unconscious process.

Discussion

The results help explain why some creativity-related cognitive factors are involved either more or less readily than others in the creative design process from workload aspects. This study identifies the part of the brain that is involved in the combination cognitive factor and detects the ERP results on cognitive factors. This study can be used by designers and researchers to further understand the cognitive processes of creativity.

Reliability of MUSE 2 and Tobii Pro Nano at capturing mobile application users' real-time cognitive workload changes

Introduction

Despite the importance of cognitive workload in examining the usability of smartphone applications and the popularity of smartphone usage globally, cognitive workload as one attribute of usability tends to be overlooked in Human-Computer Interaction (HCI) studies. Moreover, limited studies that have examined the cognitive workload aspect often measured some summative workloads using subjective measures (e.g., questionnaires). A significant limitation of subjective measures is that they can only assess the overall, subject-perceived cognitive workload after the procedures/tasks have been completed. Such measurements do not reflect the real-time workload fluctuation during the procedures. The reliability of some devices on a smartphone setting has not been thoroughly evaluated.

Methods

This study used mixed methods to empirically study the reliability of an eye-tracking device (i.e., Tobii Pro Nano) and a low-cost electroencephalogram (EEG) device (i.e., MUSE 2) for detecting real-time cognitive workload changes during N-back tasks.

Results

Results suggest that the EEG measurements collected by MUSE 2 are not very useful as indicators of cognitive workload changes in our setting, eye movement measurements collected by Tobii Pro Nano with mobile testing accessory are useful for monitoring cognitive workload fluctuations and tracking down interface design issues in a smartphone setting, and more specifically, the maximum pupil diameter is the preeminent indicator of cognitive workload surges.

Discussion

In conclusion, the pupil diameter measure combined with other subjective ratings would provide a comprehensive user experience assessment of mobile applications. They can also be used to verify the successfulness of a user interface design solution in improving user experience.

Closed-loop acoustic stimulation during an afternoon nap to modulate subsequent encoding

Sleep is able to contribute not only to memory consolidation, but also to post-sleep learning. The notion exists that either synaptic downscaling or another process during sleep increase post-sleep learning capacity. A correlation between augmentation of the sleep slow oscillation and hippocampal activation at encoding support the contribution of sleep to encoding of declarative memories. In the present study, the effect of closed-loop acoustic stimulation during an afternoon nap on post-sleep encoding of two verbal (word pairs, verbal learning and memory test) and non-verbal (figural pairs) tasks and on electroencephalogram during sleep and learning were investigated in young healthy adults (N = 16). Closed-loop acoustic stimulation enhanced slow oscillatory and spindle activity, but did not affect encoding at the group level. Subgroup analyses and comparisons with similar studies lead us to the tentative conclusion that further parameters such as time of day and subjects' cognitive ability influenced responses to closed-loop acoustic stimulation.

Effects of the Duration of a Resting-State EEG Recording in Healthy Aging and Mild Cognitive Impairment

INTRODUCTION

The recording of resting-state EEG may provide a means to predict early cognitive decline associated with mild cognitive impairment (MCI). Previous studies have typically used very short recording times to avoid a confound with drowsiness that may occur in longer recordings. The effects of a longer recording have not however been systematically examined.

METHODS

Eyes-closed resting-state EEG activity was recorded in 40 older adult participants (20 healthy older adults and 20 people with MCI). The recording period was a relatively long 6 minutes, divided into two equal 3-minute halves to determine if drowsiness will be more apparent as the recording progresses. The participants also completed standardized neuropsychological tasks that assessed global cognition (Montreal Cognitive Assessment) and memory (California Verbal Learning Test, Second Edition). A spectral analysis was performed on both short (2 seconds) and long (8 seconds) segments in both 3-minute halves.

RESULTS

No differences in power density for any of the EEG frequency bands were found between the 2 halves of the study for either group. There was little evidence of increased drowsiness in the second half of the study even when frequency resolution was increased with the 8-second segmentation. Theta power density was overall larger for people with MCI compared to healthy older adults. A negative correlation was also observed between theta power and global cognition in healthy older adults.

CONCLUSIONS

The present results indicate that longer resting-state EEG recording can be reliably employed without increased risk of drowsiness.

EEG-ERP Correlates of Cognitive Dysfunction in Polycystic Ovarian Syndrome

Background

Polycystic ovarian syndrome (PCOS) has been shown to affect the psychological and cognitive status of a woman. However, amidst various conflicting reports in this regard, very few studies attempted to assess these aspects objectively using electroencephalography (EEG) and event-related potential (ERP).

Purpose

To assess changes in neurocognitive and psychological parameters of PCOS women without any other comorbidities.

Methods

PCOS women aged 18 years to 35 years, diagnosed from obstetrics and gynecology OPD who are otherwise free of any other comorbidities, were assessed for psychological status (anxiety and depression using the State-Trait Anxiety Inventory and Beck Depression Inventory, respectively). Thereafter, a cognitive assessment was done subjectively by the Montreal Cognitive Assessment (MoCA) questionnaire and objectively by using EEG [absolute and relative power of alpha, beta, and theta waves along with theta/beta ratios (TBR) and theta/alpha ratio (TAR)] and P300 amplitude and latency of ERP during a visual oddball paradigm task in control ( n = 30) and PCOS ( n = 37) subjects.

Results

PCOS women showed significantly higher anxiety and depression scores along with low MoCA scores. Significantly reduced absolute alpha, increased frontal beta, and markedly increased theta (relative) power with increased TAR in the PCOS group were seen. Also, a significant reduction in P300 amplitude with prolonged latency during the visual oddball paradigm task was evident in them.

Conclusion

Reduced alpha and higher theta activity with increased TAR are indicative of poor neural processing ability. Reduced P300 amplitude with more latency also suggests a cognitive decline, which is corroborated by reduced MoCA scores. Our study objectively indicates the presence of subclinical cognitive impairment in PCOS patients even without any comorbidities.

Synergy through Integration of Wearable EEG and Virtual Reality for Mild Cognitive Impairment and Mild Dementia Screening

Virtual reality (VR) technologies have shown promising potential in the early diagnosis of dementia by enabling accessible and regular assessment. However, previous VR studies were restricted to the analysis of behavioral responses, so information about degenerated brain dynamics could not be directly acquired. To address this issue, we provide a cognitive impairment (CI) screening tool based on a wearable EEG device integrated into a VR platform. Subjects were asked to use a hardware setup consisting of a frontal six-channel EEG device mounted on a VR device and to perform four cognitive tasks in VR. Behavioral response profiles and EEG features were extracted during the tasks, and classifiers were trained on extracted features to differentiate subjects with CI from healthy controls (HCs). Notably, the performance of the patient classification consistently improved when EEG characteristics measured during cognitive tasks were additionally included in feature attributes than when only the task scores or resting-state EEG features were used, suggesting that our protocol provides discriminative information for screening. These results propose that the integration of EEG devices into a VR framework could emerge as a powerful and synergistic strategy for constructing an easily accessible EEG-based dementia screening tool.

Age-Related Differences in Resting-State EEG and Allocentric Spatial Working Memory Performance

During normal aging resting-state brain activity changes and working memory performance declines as compared to young adulthood. Interestingly, previous studies reported that different electroencephalographic (EEG) measures of resting-state brain activity may correlate with working memory performance at different ages. Here, we recorded resting-state EEG activity and tested allocentric spatial working memory in healthy young (20-30 years) and older (65-75 years) adults. We adapted standard EEG methods to record brain activity in mobile participants in a non-shielded environment, in both eyes closed and eyes open conditions. Our study revealed some age-group differences in resting-state brain activity that were consistent with previous results obtained in different recording conditions. We confirmed that age-group differences in resting-state EEG activity depend on the recording conditions and the specific parameters considered. Nevertheless, lower theta-band and alpha-band frequencies and absolute powers, and higher beta-band and gamma-band relative powers were overall observed in healthy older adults, as compared to healthy young adults. In addition, using principal component and regression analyses, we found that the first extracted EEG component, which represented mainly theta, alpha and beta powers, correlated with spatial working memory performance in older adults, but not in young adults. These findings are consistent with the theory that the neurobiological bases of working memory performance may differ between young and older adults. However, individual measures of resting-state EEG activity could not be used as reliable biomarkers to predict individual allocentric spatial working memory performance in young or older adults.

Aperiodic and Periodic Components of Ongoing Oscillatory Brain Dynamics Link Distinct Functional Aspects of Cognition across Adult Lifespan

Signal transmission in the brain propagates via distinct oscillatory frequency bands but the aperiodic component, 1/f activity, almost always co-exists which most of the previous studies have not sufficiently taken into consideration. We used a recently proposed parameterization model that delimits the oscillatory and aperiodic components of neural dynamics on lifespan aging data collected from human participants using magnetoencephalography (MEG). Since healthy aging underlines an enormous change in local tissue properties, any systematic relationship of 1/f activity would highlight their impact on the self-organized critical functional states. Furthermore, we have used patterns of correlation between aperiodic background and metrics of behavior to understand the domain general effects of 1/f activity. We suggest that age-associated global change in 1/f baseline alters the functional critical states of the brain affecting the global information processing impacting critically all aspects of cognition, e.g., metacognitive awareness, speed of retrieval of memory, cognitive load, and accuracy of recall through adult lifespan. This alteration in 1/f crucially impacts the oscillatory features peak frequency (PF) and band power ratio, which relates to more local processing and selective functional aspects of cognitive processing during the visual short-term memory (VSTM) task. In summary, this study leveraging on big lifespan data for the first time tracks the cross-sectional lifespan-associated periodic and aperiodic dynamical changes in the resting state to demonstrate how normative patterns of 1/f activity, PF, and band ratio (BR) measures provide distinct functional insights about the cognitive decline through adult lifespan.

Electroencephalography as a Biomarker for Functional Recovery in Spinal Cord Injury Patients

BACKGROUND

Functional changes after spinal cord injury (SCI) are related to changes in cortical plasticity. These changes can be measured with electroencephalography (EEG) and has potential to be used as a clinical biomarker.

METHOD

In this longitudinal study participants underwent a total of 30 sessions of robotic-assisted gait training (RAGT) over a course of 6 weeks. The duration of each session was 30 min. Resting state EEG was recorded before and after 30-session rehabilitation therapy. To measure gait, we used the Walking Index for Spinal Cord Injury Scale, 10-Meter- Walking Test, Timed-Up-and-Go, and 6-Min-Walking Test. Balance was measured using Berg Balance Scale.

RESULTS

Fifteen participants with incomplete SCI who had AIS C or D injuries based on American Spinal Cord Injury Association Impairment Scale classification were included in this study. Mean age was 35.7 years (range 17-51) and the mean time since injury was 17.08 (range 4-37) months. All participants showed clinical improvement with the rehabilitation program. EEG data revealed that high beta EEG activity in the central area had a negative correlation with gait (p = 0.049; β coefficient: -0.351; and adj-R 2: 0.23) and balance (p = 0.043; β coefficient: -0.158; and adj-R 2:0.24) measured at baseline, in a way that greater high beta EEG power was related to worse clinical function at baseline. Moreover, improvement in gait and balance had negative correlations with the change in alpha/theta ratio in the parietal area (Gait: p = 0.049; β coefficient: -0.351; adj-R 2: 0.23; Balance: p = 0.043; β coefficient: -0.158; and adj-R 2: 0.24).

CONCLUSION

In SCI, functional impairment and subsequent improvement following rehabilitation therapy with RAGT correlated with the change in cortical activity measured by EEG. Our results suggest that EEG alpha/theta ratio may be a potential surrogate marker of functional improvement during rehabilitation. Future studies are necessary to improve and validate these findings as a neurophysiological biomarker for SCI rehabilitation.

A Comparison of Traditional and Serious Game-Based Digital Markers of Cognition in Older Adults with Mild Cognitive Impairment and Healthy Controls

Background:

Electroencephalography (EEG) has been used to assess brain activity while users are playing an immersive serious game.

Objective:

To assess differences in brain activation as measured with a non-intrusive wearable EEG device, differences in game performance and correlations between EEG power, game performance and global cognition, between cognitively impaired and non-impaired older adults, during the administration of a novel self-administered serious game-based test, the Virtual Supermarket Test (VST).

Methods:

43 older adults with subjective cognitive decline (SCD) and 33 older adults with mild cognitive impairment (MCI) were recruited from day centers for cognitive disorders. Global cognition was assessed with the Montreal Cognitive Assessment (MoCA). Brain activity was measured with a non-intrusive wearable EEG device in a resting state condition and while they were administered the VST.

Results:

During resting state condition, the MCI group showed increased alpha, beta, delta, and theta band power compared to the SCD group. During the administration of the VST, the MCI group showed increased beta and theta band power compared to the SCD group. Regarding game performance, alpha, beta, delta, and theta rhythms were positively correlated with average duration, while delta rhythm was positively correlated with mean errors. MoCA correlated with alpha, beta, delta, and theta rhythms and with average game duration and mean game errors indicating that elevated EEG rhythms in MCI may be associated with an overall cognitive decline.

Conclusion:

VST performance can be used as a digital biomarker. Cheap commercially available wearable EEG devices can be used for obtaining brain activity biomarkers.

Beyond physiotherapy and pharmacological treatment for fibromyalgia syndrome: tailored tACS as a new therapeutic tool

Fibromyalgia syndrome (FMS) is a complex pain disorder, characterized by diffuse pain and cognitive disturbances. Abnormal cortical oscillatory activity may be a promising biomarker, encouraging non-invasive neurostimulation techniques as a treatment. We aimed to modulate abnormal slow cortical oscillations by delivering transcranial alternating current stimulation (tACS) and physiotherapy to reduce pain and cognitive symptoms. This was a double-blinded, randomized, crossover trial conducted between February and September 2018 at the Rehabilitation Unit of a teaching Hospital (NCT03221413). Participants were randomly assigned to tACS or random noise stimulation (RNS), 5 days/week for 2 weeks followed by ad hoc physiotherapy. Clinical and cognitive assessments were performed at T₀ (baseline), T₁ (after stimulation), T₂ (1 month after stimulation). Electroencephalogram (EEG) spectral topographies recorded from 15 participants confirmed slow-rhythm prevalence and provided tACS tailored stimulation parameters and electrode sites. Following tACS, EEG alpha1 ([8-10] Hz) activity increased at T₁ (p = 0.024) compared to RNS, pain symptoms assessed by Visual Analog Scale decreased at T₁ (T₁ vs T₀ p = 0.010), self-reported cognitive skills and neuropsychological scores improved both at T₁ and T₂ (Patient-Reported Outcomes in Cognitive Impairment, T₀-T₂, p = 0.024; Everyday memory questionnaire, T₁ compared to RNS, p = 0.012; Montréal Cognitive Assessment, T₀ vs T₁, p = 0.048 and T₀ vs T₂, p = 0.009; Trail Making Test B T₀-T₂, p = 0.034). Psychopathological scales and other neuropsychological scores (Trail Making Test-A; Total Phonemic Fluency; Hopkins Verbal Learning Test-Revised; Rey-Osterrieth Complex Figure) improved both after tACS and RNS but earlier improvements (T₁) were registered only after tACS. These results support tACS coupled with physiotherapy in treating FMS cognitive symptoms, pain and subclinical psychopathology.

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuroelectrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes, such as in center frequency or bandwidth, and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features and find that multiple disparate features influence ratio measures. For example, the commonly applied θ/β ratio is most reflective of differences in aperiodic activity, and not oscillatory θ or β power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach.

Association of Sleep Electroencephalography-Based Brain Age Index With Dementia

IMPORTANCE

Dementia is an increasing cause of disability and loss of independence in the elderly population yet remains largely underdiagnosed. A biomarker for dementia that can identify individuals with or at risk for developing dementia may help close this diagnostic gap.

OBJECTIVE

To investigate the association between a sleep electroencephalography-based brain age index (BAI), the difference between chronological age and brain age estimated using the sleep electroencephalogram, and dementia.

DESIGN, SETTING, AND PARTICIPANTS

In this retrospective cross-sectional study of 9834 polysomnograms, BAI was computed among individuals with previously determined dementia, mild cognitive impairment (MCI), or cognitive symptoms but no diagnosis of MCI or dementia, and among healthy individuals without dementia from August 22, 2008, to June 4, 2018. Data were analyzed from November 15, 2018, to June 24, 2020.

EXPOSURE

Dementia, MCI, and dementia-related symptoms, such as cognitive change and memory impairment.

MAIN OUTCOMES AND MEASURES

The outcome measures were the trend in BAI when moving from groups ranging from healthy, to symptomatic, to MCI, to dementia and pairwise comparisons of BAI among these groups.

FINDINGS

A total of 5144 sleep studies were included in BAI examinations. Patients in these studies had a median (interquartile range) age of 54 (43-65) years, and 3026 (59%) were men. The patients included 88 with dementia, 44 with MCI, 1075 who were symptomatic, and 2336 without dementia. There was a monotonic increase in mean (SE) BAI from the nondementia group to the dementia group (nondementia: 0.20 [0.42]; symptomatic: 0.58 [0.41]; MCI: 1.65 [1.20]; dementia: 4.18 [1.02]; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that a sleep-state electroencephalography-based BAI shows promise as a biomarker associated with progressive brain processes that ultimately result in dementia.

Hemodynamic latency is associated with reduced intelligence across the lifespan: an fMRI DCM study of aging, cerebrovascular integrity, and cognitive ability

Changes in neurovascular coupling are associated with both Alzheimer's disease and vascular dementia in later life, but this may be confounded by cerebrovascular risk. We hypothesized that hemodynamic latency would be associated with reduced cognitive functioning across the lifespan, holding constant demographic and cerebrovascular risk. In 387 adults aged 18-85 (mean = 48.82), dynamic causal modeling was used to estimate the hemodynamic response function in the left and right V1 and V3-ventral regions of the visual cortex in response to a simple checkerboard block design stimulus with minimal cognitive demands. The hemodynamic latency (transit time) in the visual cortex was used to predict general cognitive ability (Full-Scale IQ), controlling for demographic variables (age, race, education, socioeconomic status) and cerebrovascular risk factors (hypertension, alcohol use, smoking, high cholesterol, BMI, type 2 diabetes, cardiac disorders). Increased hemodynamic latency in the visual cortex predicted reduced cognitive function (p < 0.05), holding constant demographic and cerebrovascular risk. Increased alcohol use was associated with reduced overall cognitive function (Full Scale IQ 2.8 pts, p < 0.05), while cardiac disorders (Full Scale IQ 3.3 IQ pts; p < 0.05), high cholesterol (Full Scale IQ 3.9 pts; p < 0.05), and years of education (2 IQ pts/year; p < 0.001) were associated with higher general cognitive ability. Increased hemodynamic latency was associated with reduced executive functioning (p < 0.05) as well as reductions in verbal concept formation (p < 0.05) and the ability to synthesize and analyze abstract visual information (p < 0.01). Hemodynamic latency is associated with reduced cognitive ability across the lifespan, independently of other demographic and cerebrovascular risk factors. Vascular health may predict cognitive ability long before the onset of dementias.

On the time-course of functional connectivity: theory of a dynamic progression of concussion effects

The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.

A Systematic Review on the Cognitive Benefits and Neurophysiological Correlates of Exergaming in Healthy Older Adults

Human aging is associated with structural and functional brain deteriorations and a corresponding cognitive decline. Exergaming (i.e., physically active video-gaming) has been supposed to attenuate age-related brain deteriorations and may even improve cognitive functions in healthy older adults. Effects of exergaming, however, vary largely across studies. Moreover, the underlying neurophysiological mechanisms by which exergaming may affect cognitive and brain function are still poorly understood. Therefore, we systematically reviewed the effects of exergame interventions on cognitive outcomes and neurophysiological correlates in healthy older adults (>60 years). After screening 2709 studies (Cochrane Library, PsycINFO, Pubmed, Scopus), we found 15 eligible studies, four of which comprised neurophysiological measures. Most studies reported within group improvements in exergamers and favorable interaction effects compared to passive controls. Fewer studies found superior effects of exergaming over physically active control groups and, if so, solely for executive functions. Regarding individual cognitive domains, results showed no consistence. Positive effects on neurophysiological outcomes were present in all respective studies. In summary, exergaming seems to be equally or slightly more effective than other physical interventions on cognitive functions in healthy older adults. Tailored interventions using well-considered exergames and intervention designs, however, may result in more distinct effects on cognitive functions.

Incremental benefits of EEG informed fMRI in the study of disorders related to meso-corticolimbic dopamine pathway dysfunction: A systematic review of recent literature

Functional magnetic resonance imaging (fMRI) is a non-invasive imaging modality that enables the assessment of neural connectivity and oxygen utility of the brain using blood oxygen level dependent (BOLD) imaging sequence. Electroencephalography (EEG), on the other hands, looks at cortical electrical impulses of the brain thus detecting brainwave patterns during rest and thought processing. The combination of these two modalities is called fMRI with simultaneous EEG (fMRI-EEG), which has emerged as a new tool for experimental neuroscience assessments and has been applied clinically in many settings, most commonly in epilepsy cases. Recent advances in imaging has led to fMRI-EEG being utilized in behavioural studies which can help in giving an objective assessment of ambiguous cases and help in the assessment of response to treatment by providing a non-invasive biomarker of the disease processes. We aim to review the role and interpretation of fMRI-EEG in studies pertaining to psychiatric disorders and behavioral abnormalities.

Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience

Maturation and aging are important life periods that are linked to drastic brain reorganization processes which are essential for mental health. However, the development of generalized theories for delimiting physiological and pathological brain remodeling through life periods linked to healthy states and resilience on one side or mental dysfunction on the other remains a challenge. Furthermore, important processes of preservation and compensation of brain function occur continuously in the cerebral brain networks and drive physiological responses to life events. Here, we review research on brain reorganization processes across the lifespan, demonstrating brain circuits remodeling at the structural and functional level that support mental health and are parallelized by physiological trajectories during maturation and healthy aging. We show evidence that aberrations leading to mental disorders result from the specific alterations of cerebral networks and their pathological dynamics leading to distinct excitability patterns. We discuss how these series of large-scale responses of brain circuits can be viewed as protective or malfunctioning mechanisms for the maintenance of mental health and resilience.

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

Age-Related Differences in Pro-active Driving Behavior Revealed by EEG Measures

Healthy aging is associated with a decline in cognitive functions. This may become an issue when complex tasks have to be performed like driving a car in a demanding traffic situation. On the other hand, older people are able to compensate for age-related deficits, e.g., by deploying extra mental effort and other compensatory strategies. The present study investigated the interplay of age, task workload, and mental effort using EEG measures and a proactive driving task, in which 16 younger and 16 older participants had to keep a virtual car on track on a curvy road. Total oscillatory power and relative power in Theta and Alpha bands were analyzed, as well as event-related potentials (ERPs) to task-irrelevant regular and irregular sound stimuli. Steering variability and Theta power increased with increasing task load (i.e., with shaper bends of the road), while Alpha power decreased. This pattern of workload and mental effort was found in both age groups. However, only in the older group a relationship between steering variability and Theta power occurred: better steering performance was associated with higher Theta power, reflecting higher mental effort. Higher Theta power while driving was also associated with a stronger increase in reported subjective fatigue in the older group. In the younger group, lower steering variability came along with lower ERP responses to deviant sound stimuli, reflecting reduced processing of task-irrelevant environmental stimuli. In sum, better performance in proactive driving (i.e., more alert steering behavior) was associated with increased mental effort in the older group, and higher attentional focus on the task in the younger group, indicating age-specific strategies in the way younger and older drivers manage demanding (driving) tasks.