EEG default mode network in the human brain: Spectral regional field powers

Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals

Purpose

This study aims to investigate using eyes-open (EO) and eyes-closed (EC) resting-state EEG data to diagnose cognitive impairment using machine learning methods, enhancing timely intervention and cost-effectiveness in dementia research.

Participants and Methods

A total of 890 participants aged 40-90 were included in the study, comprising 269 healthy controls (HC), 356 individuals with mild cognitive impairment (MCI), and 265 with Alzheimer's disease (AD) from a cohort study. Resting-state EEG (rEEG) signals were recorded and transformed into relative power spectral density (PSD) data for analysis. The processed PSD data, representing 19 scalp regions, were then input into a Random Forest (RF) machine learning classifier to identify distinctive EEG patterns across the groups. Statistical comparisons between the groups were conducted using one-way ANOVA, applied to the relative PSD features extracted from the EEG data, to assess significant differences in EEG activity across the diagnostic categories.

Results

The study found that rEEG-based categorization effectively differentiates between cognitively impaired individuals and healthy individuals. The EO rEEG achieved the highest performance metrics across various models. For HC vs MCI (combined hemisphere), the accuracy, sensitivity, specificity, and AUC were 92%, 99%, 83%, and 96%, respectively. For HC vs AD (parietal, temporal, occipital), these metrics were 95%, 96%, 94%, and 99%. The HC vs CASE (MCI + AD) (combined hemisphere) results were 90%, 99%, 73%, and 92%. The metrics for HC vs MCI vs AD (frontal, parietal, temporal) were 89%, 88%, 94%, and 96%.

Conclusion

The study demonstrates that EO rEEG can effectively distinguish between cognitive impairment and healthy states, leading to early diagnosis, cost-effective treatment, and better clinical outcomes for dementia patients. EO and EC rEEG models trained with relative PSD, particularly from parietal, temporal, occipital, and central scalp regions, can significantly assist clinicians in practice.

Cortical Hypoactivation of Frontal Areas Modulates Resting Electroencephalography Microstates in Children With Attention-Deficit/Hyperactivity Disorder

BACKGROUND

In the current study, we examined electroencephalography (EEG) microstate alterations and their neural generators during resting state in children with attention-deficit/hyperactivity disorder (ADHD) to explore a potential state biomarker.

METHODS

A total of 76 participants, 38 with combined-type ADHD and 38 neurotypical children, took part in the study. Five-minute resting (eyes-open) 128 channel EEG data were acquired, and 2 minutes of clean EEG data were analyzed for microstates, its sources, and connectivity in both groups. Between-groups comparisons were done for microstate parameters using modified k-means clustering with Cartool software. Furthermore, the cortical sources and functional connectivity of significant microstate maps were explored using LORETA software. Subsequently microstate parameters were correlated with the behavioral scores from the Conners' Parent Rating Scale.

RESULTS

Among the microstate parameters examined, children with ADHD displayed significant differences (p < .05) in time frames and time coverage of map B (decreased) and transition probability of map D (increased). Interestingly, source analysis of both microstate maps showed hypoactivation of frontal areas predominantly while functional connectivity showed hyperconnectivity between the medial frontal gyrus and anterior cingulate gyrus (executive function area) for map B and hypoconnectivity between the medial frontal gyrus and middle temporal gyrus (both are suggested to be part of default mode network areas) for map D. Further, cross-spectral density values of map B were found to be correlated with executive function scores from the Conners' questionnaire.

CONCLUSIONS

EEG microstate features, together with source and connectivity measures, could help differentiate children with ADHD from neurotypical children. The hypoactivation of predominantly frontal areas and their connectivity was found to determine microstate maps.

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection

Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants' biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.

Transforming of scalp EEGs with different channel locations by REST for comparative study

OBJECTIVE

The diversity of electrode placement systems brought the problem of channel location harmonization in large-scale electroencephalography (EEG) applications to the forefront. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform EEGs into a common electrode distribution with computational zero reference at infinity offline.

METHODS

Simulation and eye-closed resting-state EEG datasets were used to investigate the performance of REST for EEG signals and power configurations.

RESULTS

REST produced small errors (the root mean square error (RMSE): 0.2936-0.4583; absolute errors: 0.2343-0.3657) and high correlations (>0.9) between the estimated signals and true ones. The comparison of configuration similarities in power among various electrode distributions revealed that REST induced infinity reference could maintain a perfect performance similar (>0.9) to that of true one.

CONCLUSION

These results demonstrated that REST transformation could be adopted to resolve the channel location harmonization problem in large-scale EEG applications.

Prior EEG marks focused and mind-wandering mental states across trials

Whether spontaneous or induced by a tedious task, the transition from a focused mental state to mind wandering is a complex one, possibly involving adjacent mental states and extending over minutes or even hours. This complexity cannot be captured by relying solely on subjective reports of mind wandering. To characterize the transition in a mind-wandering-inducing tone counting task, in addition we collected subjective reports of thought generation along with task performance as a measure of cognitive control and EEG measures, namely auditory probe evoked potentials (AEP) and ongoing 8-12 Hz alpha-band amplitude. We analyzed the cross-correlations between timeseries of these observations to reveal their contributions over time to the occurrence of task-focused and mind-wandering states. Thought generation and cognitive control showed overall a yoked dynamics, in which thought production increased when cognitive control decreased. Prior to mind wandering however, they became decoupled after transient increases in cognitive control-related alpha amplitude. The decoupling allows transitory mental states beyond the unidimensional focused/wandering continuum. Time lags of these effects were on the order of several minutes, with 4-10 min for that of alpha amplitude. We discuss the implications for mind wandering and related mental states, and for mind-wandering prediction applications.

Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment

Background

Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI.

Methods

A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages.

Results

The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone.

Conclusion

Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.

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).

Towards an electroencephalographic measure of awareness based on the reactivity of oscillatory macrostates to hearing a subject's own name

We proposed that the brain's electrical activity is composed of a sequence of alternating states with repeating topographic spectral distributions on scalp electroencephalogram (EEG), referred to as oscillatory macrostates. The macrostate showing the largest decrease in the probability of occurrence, measured as a percentage (reactivity), during sensory stimulation was labelled as the default EEG macrostate (DEM). This study aimed to assess the influence of awareness on DEM reactivity (DER). We included 11 middle cerebral artery ischaemic stroke patients with impaired awareness having a median Glasgow Coma Scale (GCS) of 6/15 and a group of 11 matched healthy controls. EEG recordings were carried out during auditory 1 min stimulation epochs repeating either the subject's own name (SON) or the SON in reverse (rSON). The DEM was identified across three SON epochs alternating with three rSON epochs. Compared with the patients, the DEM of controls contained more posterior theta activity reflecting source dipoles that could be mapped in the posterior cingulate cortex. The DER was measured from the 1 min quiet baseline preceding each stimulation epoch. The difference in mean DER between the SON and rSON epochs was measured by the salient EEG reactivity (SER) theoretically ranging from -100% to 100%. The SER was 12.4 ± 2.7% (Mean ± standard error of the mean) in controls and only 1.3 ± 1.9% in the patient group (P < 0.01). The patient SER decreased with the Glasgow Coma Scale. Our data suggest that awareness increases DER to SON as measured by SER.

[Electroencephalography: features of the obtained data and its applicability in psychiatry]

Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model

This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity.

Associations between age-related differences in occipital alpha power and the broadband parameters of the EEG power spectrum: A cross-sectional cohort study

In adulthood, neurological structure and function are often affected by aging, with negative implications for daily life as well as laboratory-based tasks. Some of these changes include decreased efficiency modulating cortical activity and lower signal-to-noise ratios in neural processing (as inferred from surface electroencephalography). To better understand mechanisms influencing age-related changes in cortical activity, we explored the effects of aging on narrow-band alpha power (7.5-12.5 Hz) and broadband/aperiodic components that span a wider range (1.5-30.5 Hz) over the occipital region during eyes-open and eyes-closed wakeful rest in 19 healthy young adults (18-35 years) and 21 community-dwelling older adults (59+ years). Older adults exhibited a smaller change in alpha power across conditions compared to younger adults. Older adults also showed flatter aperiodic slopes in both conditions. These changes in narrow-band alpha are consistent with previous work and suggest that older adults may have a reduced ability to modulate state-specific activity. Differences in the aperiodic slope suggest age-related changes in the signal-noise-ratio in cortical oscillations. However, the relationship between narrow-band alpha modulation and the aperiodic slope was unclear, warranting further investigation into how these variables relate to each other in the aging process. In summary, aging is associated with a broadband flattening of the EEG power spectrum and reduced state-specific modulation of narrow-band alpha power, but these changes appear to be (at least partially) independent of each other. The present findings suggest that separate mechanisms may underlie age-related differences in aperiodic power and narrow-band oscillations.

Theta oscillatory power decreases in humans are associated with spatial learning in a virtual water maze task

Theta oscillations (4-8 Hz) in humans play a role in navigation processes, including spatial encoding, retrieval and sensorimotor integration. Increased theta power at frontal and parietal midline regions is known to contribute to successful navigation. However, the dynamics of cortical theta and its role in spatial learning are not fully understood. This study aimed to investigate theta oscillations via electroencephalogram (EEG) during spatial learning in a virtual water maze. Participants were separated into a learning group (n = 25) who learned the location of a hidden goal across 12 trials, or a time-matched non-learning group (n = 25) who were required to simply navigate the same arena, but without a goal. We compared all trials, at two phases of learning, the trial start and the goal approach. We also compared the first six trials with the last six trials within-groups. The learning group showed reduced low-frequency theta power at the frontal and parietal midline during the start phase and largely reduced theta combined with a short increase at both midlines during the goal-approach phase. These patterns were not found in the non-learning group, who instead displayed extensive increases in low-frequency oscillations at both regions during the trial start and at the parietal midline during goal approach. Our results support the theory that theta plays a crucial role in spatial encoding during exploration, as opposed to sensorimotor integration. We suggest our findings provide evidence for a link between learning and a reduction of theta oscillations in humans.

Effect of total sleep deprivation on effective EEG connectivity for young male in resting-state networks in different eye states

Background

Many studies have investigated the effect of total sleep deprivation (TSD) on resting-state functional networks, especially the default mode network (DMN) and sensorimotor network (SMN), using functional connectivity. While it is known that the activities of these networks differ based on eye state, it remains unclear how TSD affects them in different eye states. Therefore, we aimed to examine the effect of TSD on DMN and SMN in different eye states using effective functional connectivity via isolated effective coherence (iCoh) in exact low-resolution brain electromagnetic tomography (eLORETA).

Methods

Resting-state electroencephalogram (EEG) signals were collected from 24 male college students, and each participant completed a psychomotor vigilance task (PVT) while behavioral data were acquired. Each participant underwent 36-h TSD, and the data were acquired in two sleep-deprivation times (rested wakefulness, RW: 0 h; and TSD: 36 h) and two eye states (eyes closed, EC; and eyes open, EO). Changes in neural oscillations and effective connectivity were compared based on paired t-test.

Results

The behavioral results showed that PVT reaction time was significantly longer in TSD compared with that of RW. The EEG results showed that in the EO state, the activity of high-frequency bands in the DMN and SMN were enhanced compared to those of the EC state. Furthermore, when compared with the DMN and SMN of RW, in TSD, the activity of DMN was decreased, and SMN was increased. Moreover, the changed effective connectivity in the DMN and SMN after TSD was positively correlated with an increased PVT reaction time. In addition, the effective connectivity in the different network (EO-EC) of the SMN was reduced in the β band after TSD compared with that of RW.

Conclusion

These findings indicate that TSD impairs alertness and sensory information input in the SMN to a greater extent in an EO than in an EC state.

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures

Introduction

The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks.

Methods

We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC).

Results

PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM).

Discussion

Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.

Electrophysiological correlates of dispositional mindfulness: A quantitative and complexity EEG study

While growing evidence supports that dispositional mindfulness relates to psychological health and cognitive enhancement, to date there have been only a few attempts to characterize its neural underpinnings. In the present study, we aimed at exploring the electrophysiological (EEG) signature of dispositional mindfulness using quantitative and complexity measures of EEG during resting state and while performing a learning task. Hundred twenty participants were assessed with the Five Facet Mindfulness Questionnaire and underwent 5 min eyes-closed resting state and 5 min at task EEG recording. We hypothesized that high mindfulness individuals would show patterns of brain activity related to (a) lower involvement of the default mode network (DMN) at rest (reduced frontal gamma power) and (b) a state of 'task readiness' reflected in a more similar pattern from rest to task (reduced overall q-EEG power at rest but not at task), as compared to their low mindfulness counterparts. Dispositional mindfulness was significantly linked to reduced frontal gamma power at rest and lower overall power during rest but not at task. In addition, we found a trend towards higher entropy during task performance in mindful individuals, which has recently been reported during mindfulness meditation. Altogether, our results add to those from expert meditators to show that high (dispositional) mindfulness seems to have a specific electrophysiological pattern characteristic of less involvement of the DMN and mind-wandering processes.

Effects of inhaling essential oils of Citrus limonum L., Santalum album, and Cinnamomum camphora on human brain activity

INTRODUCTION

Essential oil inhalation has various effects on the human body. However, its effects on cognitive function and the neural basis remain unclear. We aimed to investigate the effects of inhaling lemon, sandalwood, and kusunoki essential oils on human brain activity and memory function using multichannel electroencephalography and brain source activity estimation.

METHODS

Participants performed a letter 2-back working memory task during electroencephalography measurements before and after essential oil inhalation. Brain activation, task difficulty, concentration degree, and task performance were compared among the essential oils and a fragrance-free control.

RESULTS

Task performance significantly improved after lemon essential oil inhalation. Lemon essential oil inhalation resulted in delta and theta band activation in the prefrontal cortex, including the anterior cingulate gyrus and orbitofrontal cortex, superior temporal gyrus, parahippocampal gyrus, and insula. During inhalation, persistent alpha band activation was observed in the prefrontal cortex, including the anterior cingulate gyrus. Sandalwood essential oil inhalation led to beta and gamma band activation in the prefrontal cortex, including the anterior cingulate gyrus.

CONCLUSION

Our findings demonstrate that different essential oils have specific effects on brain activity related to emotion and memory processing.

Multiple functions of the angular gyrus at high temporal resolution

Here, the functions of the angular gyrus (AG) are evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies. 65 TMS/TES and 52 EEG/MEG studies were examined in this review. TMS/TES literature points to a causal role in semantic processing, word and number processing, attention and visual search, self-guided movement, memory, and self-processing. EEG/MEG studies reported AG effects at latencies varying between 32 and 800 ms in a wide range of domains, with a high probability to detect an effect at 300-350 ms post-stimulus onset. A three-phase unifying model revolving around the process of sensemaking is then suggested: (1) early AG involvement in defining the current context, within the first 200 ms, with a bias toward the right hemisphere; (2) attention re-orientation and retrieval of relevant information within 200-500 ms; and (3) cross-modal integration at late latencies with a bias toward the left hemisphere. This sensemaking process can favour accuracy (e.g. for word and number processing) or plausibility (e.g. for comprehension and social cognition). Such functions of the AG depend on the status of other connected regions. The much-debated semantic role is also discussed as follows: (1) there is a strong TMS/TES evidence for a causal semantic role, (2) current EEG/MEG evidence is however weak, but (3) the existing arguments against a semantic role for the AG are not strong. Some outstanding questions for future research are proposed. This review recognizes that cracking the role(s) of the AG in cognition is possible only when its exact contributions within the default mode network are teased apart.

Electrophysiological and behavioral effects of unilateral and bilateral rTMS; A randomized clinical trial on rumination and depression

BACKGROUND

Rumination is significantly frequent in major depressive disorder (MDD). However, not a lot of studies have investigated the effects of repetitive transcranial magnetic stimulation (rTMS) on rumination.

METHODS

61 participants with a minimum Hamilton Depression Rating Scale (HAM-D) score of 20 were randomly assigned to sham, bilateral stimulation (BS) or unilateral stimulation (US) groups. EEG, The Ruminative Response Scale (RRS), and HAM-D were administered before and after the 20 sessions of rTMS. Phase locked values (PLV) were calculated as a measure of connectivity.

RESULTS

There was a significant decrease in HAM-D scores in both BS and US. In responders, BS and US differed significantly in RRS total scores, with greater reduction in BS. PLV significantly changed in the default mode network (DMN) in delta, theta, alpha, and beta in BS, in responders of which PLV decreased in the DMN in beta and gamma. Positive correlations between PLV and brooding in delta and theta, and negative correlations between PLV and reflection were found in theta, alpha, and beta. In US, connectivity in the DMN increased in beta, and PLV increased in theta and beta, and decreased in alpha and beta in its responders. Positive correlations between PLV and brooding in the delta and theta, as well as negative correlations between PLV and reflection in theta were observed in the DMN.

CONCLUSION

US and BS resulted in different modulations in the DMN, however, both could alleviate both rumination and depression. Reductions in the beta and alpha frequency bands in the DMN can be considered as potential EEG-based markers of response to bilateral and unilateral rTMS, respectively.

Functional brain changes using electroencephalography after a 24-week multidomain intervention program to prevent dementia

Quantitative electroencephalography (QEEG) has proven useful in predicting the response to various treatments, but, until now, no study has investigated changes in functional connectivity using QEEG following a lifestyle intervention program. We aimed to investigate neurophysiological changes in QEEG after a 24-week multidomain lifestyle intervention program in the SoUth Korean study to PrEvent cognitive impaiRment and protect BRAIN health through lifestyle intervention in at-risk elderly people (SUPERBRAIN). Participants without dementia and with at least one modifiable dementia risk factor, aged 60–79 years, were randomly assigned to the facility-based multidomain intervention (FMI) (n = 51), the home-based multidomain intervention (HMI) (n = 51), and the control group (n = 50). The analysis of this study included data from 44, 49, and 34 participants who underwent EEG at baseline and at the end of the study in the FMI, HMI, and control groups, respectively. The spectrum power and power ratio of EEG were calculated. Source cortical current density and functional connectivity were estimated by standardized low-resolution brain electromagnetic tomography. Participants who received the intervention showed increases in the power of the beta1 and beta3 bands and in the imaginary part of coherence of the alpha1 band compared to the control group. Decreases in the characteristic path lengths of the alpha1 band in the right supramarginal gyrus and right rostral middle frontal cortex were observed in those who received the intervention. This study showed positive biological changes, including increased functional connectivity and higher global efficiency in QEEG after a multidomain lifestyle intervention.

Clinical trial registration

[https://clinicaltrials.gov/ct2/show/NCT03980392] identifier [NCT03980392].

Resting state electroencephalographic rhythms are affected by immediately preceding memory demands in cognitively unimpaired elderly and patients with mild cognitive impairment

Experiments on event-related electroencephalographic oscillations in aged people typically include blocks of cognitive tasks with a few minutes of interval between them. The present exploratory study tested the effect of being engaged on cognitive tasks over the resting state cortical arousal after task completion, and whether it differs according to the level of the participant’s cognitive decline. To investigate this issue, we used a local database including data in 30 healthy cognitively unimpaired (CU) persons and 40 matched patients with amnestic mild cognitive impairment (aMCI). They had been involved in 2 memory tasks for about 40 min and underwent resting-state electroencephalographic (rsEEG) recording after 5 min from the task end. eLORETA freeware estimated rsEEG alpha source activity as an index of general cortical arousal. In the CU but not aMCI group, there was a negative correlation between memory tasks performance and posterior rsEEG alpha source activity. The better the memory tasks performance, the lower the posterior alpha activity (i.e., higher cortical arousal). There was also a negative correlation between neuropsychological test scores of global cognitive status and alpha source activity. These results suggest that engagement in memory tasks may perturb background brain arousal for more than 5 min after the tasks end, and that this effect are dependent on participants global cognitive status. Future studies in CU and aMCI groups may cross-validate and extend these results with experiments including (1) rsEEG recordings before memory tasks and (2) post-tasks rsEEG recordings after 5, 15, and 30 min.

Cues to body-related distortions and hallucinations? Spontaneous sensations correlate with EEG oscillatory activity recorded at rest in the somatosensory cortices

Body awareness may arise in the total absence of sensory input, as suggested by the spontaneous occurrence of normal and pathological (i.e., hallucinatory) bodily sensations. These phenomena may arise due to back-projections from higher-order cortical areas to the primary (SI) and secondary (SII) somatosensory cortices, and would appear to be reflected in cortical oscillatory activity in both SI and SII. Here, we set to investigate the relationship of SI and SII in SPS. Healthy participants underwent an EEG recording session at rest, and then completed an experiment on the perception of spontaneous sensations occurring on the hands. Cortical oscillatory activity was extracted from specified ROIs in the somatosensory cortices. The findings showed that (i) SPS perceived in the fingers correlated positively with alpha-band oscillations recorded in SI, and that (ii) SPS perceived in the palm correlated positively with gamma-band oscillations and negatively with beta-band oscillations recorded in SII. Apart from supporting the idea that the somatosensory cortices are involved in bodily awareness even in the absence of sensory input, these findings also suggest that default oscillatory activity in the somatosensory cortices reflects individual differences in bodily awareness. The results are interpreted in terms of neural and cognitive processes that may give rise to bodily awareness and modulate it, and their importance in understanding body perception distortions and bodily delusions and hallucinations is discussed.

The natural frequencies of the resting human brain: An MEG-based atlas

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results provided empirical confirmation of the canonical frequency bands and revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Critically, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.

Tackling the Electro-Topography of the Selves Through the Sphere Model of Consciousness

In the current hypothesis paper, we propose a novel examination of consciousness and self-awareness through the neuro-phenomenological theoretical model known as the Sphere Model of Consciousness (SMC). Our aim is to create a practical instrument to address several methodological issues in consciousness research. We present a preliminary attempt to validate the SMC via a simplified electrophysiological topographic map of the Self. This map depicts the gradual shift from faster to slower frequency bands that appears to mirror the dynamic between the various SMC states of Self. In order to explore our hypothesis that the SMC's different states of Self correspond to specific frequency bands, we present a mini-review of studies examining the electrophysiological activity that occurs within the different states of Self and in the context of specific meditation types. The theoretical argument presented here is that the SMC's hierarchical organization of three states of the Self mirrors the hierarchical organization of Focused Attention, Open Monitoring, and Non-Dual meditation types. This is followed by testable predictions and potential applications of the SMC and the hypotheses derived from it. To our knowledge, this is the first integrated electrophysiological account that combines types of Self and meditation practices. We suggest this electro-topographic framework of the Selves enables easier, clearer conceptualization of the connections between meditation types as well as increased understanding of wakefulness states and altered states of consciousness.

A Fast Detection Method of Break Points in Effective Connectivity Networks

There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop fast, scalable methods for detecting and localizing in time such changes and also identify their drivers, thus enabling neuroscientists to hypothesize about potential mechanisms. This paper presents a fast method for detecting break points in exceedingly long time series neurogimaging data, based on vector autoregressive (Granger causal) models. It uses a multi-step strategy based on a regularized objective function that leads to fast identification of candidate break points, followed by clustering steps to select the final set of break points and subsequent estimation with false positives control of the underlying Granger causal networks. The latter provide insights into key changes in network connectivity that led to the presence of break points. The proposed methodology is illustrated on synthetic data varying in their length, dimensionality, number of break points, strength of signal and also applied to EEG data related to visual tasks.

Systemic physiology augmented functional near-infrared spectroscopy hyperscanning: a first evaluation investigating entrainment of spontaneous activity of brain and body physiology between subjects

Significance: Functional near-infrared spectroscopy (fNIRS) enables measuring the brain activity of two subjects while they interact, i.e., the hyperscanning approach. Aim: In our exploratory study, we extended classical fNIRS hyperscanning by adding systemic physiological measures to obtain systemic physiology augmented fNIRS (SPA-fNIRS) hyperscanning while blocking and not blocking the visual communication between the subjects. This approach enables access brain-to-brain, brain-to-body, and body-to-body coupling between the subjects simultaneously. Approach: Twenty-four pairs of subjects participated in the experiment. The paradigm consisted of two subjects that sat in front of each other and had their eyes closed for 10 min, followed by a phase of 10 min where they made eye contact. Brain and body activity was measured continuously by SPA-fNIRS. Results: Our study shows that making eye contact for a prolonged time causes significant changes in brain-to-brain, brain-to-body, and body-to-body coupling, indicating that eye contact is followed by entrainment of the physiology between subjects. Subjects that knew each other generally showed a larger trend to change between the two conditions. Conclusions: The main point of this study is to introduce a new framework to investigate brain-to-brain, body-to-body, and brain-to-body coupling through a simple social experimental paradigm. The study revealed that eye contact leads to significant synchronization of spontaneous activity of the brain and body physiology. Our study is the first that employed the SPA-fNIRS approach and showed its usefulness to investigate complex interpersonal physiological changes.

Electroencephalographic Signature of Negative Self Perceptions in Medical Students

Frontal alpha asymmetry (fAA) is purported to be a neurophysiological marker for anxiety and depression. Higher left frontal alpha EEG voltage is associated with lower left and higher right frontal cerebral cortical activation, indicative of right-sided fAA. This pilot study tests the hypothesis that greater left-sided frontal alpha voltage is associated with negative thoughts about oneself. A group of eight healthy 28-41-year-old right-handed male medical students were subjected to an extensive interactive self-report inventory (ISI) evaluating perceptions of their psychosocial interactions. Quantitative EEG (qEEG) was performed with eyes closed. Computations of fAA and related parameters were based on measurements in the alpha bandwidth (8-13 Hz) at the left frontal F7 and right frontal F8 scalp electrodes. fAA was the percent difference between mean voltages at F8 minus that at F7. Significance of associations between fAA and the ISI scores was determined by Pearson’s product-moment correlation coefficient, at P≤0.05. “Depressed” scores were positively correlated with right-sided fAA (P=0.01). “Relaxed” (P=0.05), “regulated” (P=0.02), “cooperative” (P=0.05) and “dependent scores” (P=0.004) were negatively correlated with right-sided fAA. These findings imply that right-sided fAA may be associated with more perceptions of “depressed” psychosocial interactions involving negative thoughts about oneself, as well as, more reliance on others (“dependence” score), less sharing (“cooperative” ISI score), less trust (“regulated” ISI score) and less initiative (“relaxed” ISI score). These results support the hypothesis that right-sided fAA may identify individuals with a predilection for negative thoughts about themselves and other negatively-valenced perceptions of their psychosocial interactions.

EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction

Classifying emotional states is critical for brain–computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.

Nasal Air Puff Promotes Default Mode Network Activity in Mechanically Ventilated Comatose Patients: A Noninvasive Brain Stimulation Approach

OBJECTIVES

Coma state and loss of consciousness are associated with impaired brain activity, particularly gamma oscillations, that integrate functional connectivity in neural networks, including the default mode network (DMN). Mechanical ventilation (MV) in comatose patients can aggravate brain activity, which has decreased in coma, presumably because of diminished nasal airflow. Nasal airflow, known to drive functional neural oscillations, synchronizing distant brain networks activity, is eliminated by tracheal intubation and MV. Hence, we proposed that rhythmic nasal air puffing in mechanically ventilated comatose patients may promote brain activity and improve network connectivity.

MATERIALS AND METHODS

We recorded electroencephalography (EEG) from 15 comatose patients (seven women) admitted to the intensive care unit because of opium poisoning and assessed the activity, complexity, and connectivity of the DMN before and during the nasal air-puff stimulation. Nasal cavity air puffing was done through a nasal cannula controlled by an electrical valve (open duration of 630 ms) with a frequency of 0.2 Hz (ie, 12 puff/min).

RESULTS

Our analyses demonstrated that nasal air puffing enhanced the power of gamma oscillations (30-100 Hz) in the DMN. In addition, we found that the coherence and synchrony between DMN regions were increased during nasal air puffing. Recurrence quantification and fractal dimension analyses revealed that EEG global complexity and irregularity, typically seen in wakefulness and conscious state, increased during rhythmic nasal air puffing.

CONCLUSIONS

Rhythmic nasal air puffing, as a noninvasive brain stimulation method, opens a new window to modifying the brain connectivity integration in comatose patients. This approach may potentially influence comatose patients' outcomes by increasing brain reactivity and network connectivity.

WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

A method to assess the default EEG macrostate and its reactivity to stimulation

OBJECTIVE

The default mode network (DMN) is deactivated by stimulation. We aimed to assess the DMN reactivity impairment by routine EEG recordings in stroke patients with impaired consciousness.

METHODS

Binocular light flashes were delivered at 1 Hz in 1-minute epochs, following a 1-minute baseline (PRE). The EEG was decomposed in a series of binary oscillatory macrostates by topographic spectral clustering. The most deactivated macrostate was labeled the default EEG macrostate (DEM). Its reactivity (DER) was quantified as the decrease in DEM occurrence probability during stimulation. A normalized DER index (DERI) was calculated as DER/PRE. The measures were compared between 14 healthy controls and 32 comatose patients under EEG monitoring following an acute stroke.

RESULTS

The DEM was mapped to the posterior DMN hubs. In the patients, these DEM source dipoles were 3-4 times less frequent and were associated with an increased theta activity. Even in a reduced 6-channel montage, a DER below 6.26% corresponding to a DERI below 0.25 could discriminate the patients with sensitivity and specificity well above 80%.

CONCLUSION

The method detected the DMN impairment in post-stroke coma patients.

SIGNIFICANCE

The DEM and its reactivity to stimulation could be useful to monitor the DMN function at bedside.

Human resting-state EEG and radiofrequency GSM mobile phone exposure: the impact of the individual alpha frequency

PURPOSE

With the extensive use of mobile phone (MP), several studies have been realized to investigate the effects of radiofrequency electromagnetic fields (RF-EMF) exposure on brain activity at rest via electroencephalography (EEG), and the most consistent effect has been seen on the alpha band power spectral density (PSD). However, some studies reported an increase or a decrease of the PSD, while others showed no effect. It has been suggested that these differences might partly be due to a variability of the physiological state of the brain between subjects. So, the aim of this study was to investigate the alpha band modulation, exploring the impact of the alpha band frequency ranges applied in the PSD analysis.

MATERIALS AND METHODS

Twenty-one healthy volunteers took part to the study with a double-blind, randomized and counterbalanced crossover design, during which eyes-open (EO) and eyes-closed (EC) resting-state EEG was recorded. The exposure system was a sham or a real GSM (global system for mobile) 900 MHz MP (pulse modulated at 217 Hz, mean power of 250 mW and 2 W peak, with a maximum specific absorption rate of 0.70 W/kg on 1 g tissue). The experimental protocol presented a baseline recording phase without MP exposure, an exposure phase during which the exposure system was placed against the left ear, and the post-exposure phase without MP. EEG data from baseline and exposure phases were analyzed and PSD was computed for the alpha band in the fixed range of 8-12 Hz and for the individual alpha band frequency range (IAF).

RESULTS

Results showed a trend in decrease or increase of EEG power of both alpha oscillations during exposure in relation to EC and EO recording conditions, respectively, but not reaching statistical significance. Findings did not provide evidence for a different sensitivity to RF-EMF MP related to individual variability in the frequency of the alpha band.

CONCLUSION

In conclusion, these results did not show alpha band activity modulation during resting-state under RF-EMF. It might be argued the need of a delay after the exposure in order to appreciate an EEG spectral power modulation related to RF-EMF exposure.

Emotional processing of sadness and disgust evoked by disaster scenes

OBJECTIVE

Disaster scenes produce long-term negative feelings in those who experience them. Previous studies have focused on mitigating disaster impacts through directed forgetting or conscious suppression. However, the initial emotional processing of disaster scenes is not fully understood, hindering the comprehension of long-term disaster impacts. This study aims to investigate how pictures of disaster scenes evoking disgust and sadness are processed via cortical electrical activity.

METHODS

Pictures of grief and mutilation from disasters were used to evoke sadness and disgust, respectively. Event-related desynchronization (ERD) and event-related potentials (ERPs) were used to quantify the intensity and time-course of emotional processing.

RESULTS

The information processing of emotional pictures was stronger than neutral pictures, represented by greater declines of alpha ERD. In the posterior ERP components of N1 and EPN, amplitudes for emotional pictures were larger than those for neutral pictures, which reflected the effects of arousal on visual perception. In the anterior ERP components of P2, P3, and LPP, disgust pictures showed higher attention attraction and enhanced encoding memory processing.

CONCLUSIONS

Disgust disaster scenarios induced long-term prominent LPP, which may correspond with the long-term negative impacts of the disaster.

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease

OBJECTIVE

Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD.

METHODS

We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores.

RESULTS

In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores.

CONCLUSION

Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status

Compared with healthy older adults, patients with Alzheimer's disease show decreased alpha and beta power as well as increased delta and theta power during resting state electroencephalography (rsEEG). Findings for mild cognitive impairment (MCI), a stage of increased risk of conversion to dementia, are less conclusive. Cognitive status of 213 non-demented high-agers (mean age, 82.5 years) was classified according to a neuropsychological screening and a cognitive test battery. RsEEG was measured with eyes closed and open, and absolute power in delta, theta, alpha, and beta bands were calculated for nine regions. Results indicate no rsEEG power differences between healthy individuals and those with MCI. There were also no differences present between groups in EEG reactivity, the change in power from eyes closed to eyes open, or the topographical pattern of each frequency band. Overall, EEG reactivity was preserved in 80+-year-olds without dementia, and topographical patterns were described for each frequency band. The application of rsEEG power as a marker for the early detection of dementia might be less conclusive for high-agers.

Mapping the functional brain state of a world champion freediver in static dry apnea

Voluntary apnea showcases extreme human adaptability in trained individuals like professional free divers. We evaluated the psychological and physiological adaptation and the functional cerebral changes using electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) to 6.5 min of dry static apnea performed by a world champion free diver. Compared to resting state at baseline, breath holding was characterized by increased EEG power and functional connectivity in the alpha band, along with decreased delta band connectivity. fMRI connectivity was increased within the default mode network (DMN) and visual areas but decreased in pre- and postcentral cortices. While these changes occurred in regions overlapping with cerebral signatures of several meditation practices, they also display some unique features that suggest an altered somatosensory integration. As suggested by self-reports, these findings could reflect the ability of elite free divers to create a state of sensory dissociation when performing prolonged apnea.

Infant functional networks are modulated by state of consciousness and circadian rhythm

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

Rich Club Characteristics of Alcohol-Naïve Functional Brain Networks Predict Future Drinking Phenotypes in Rhesus Macaques

Purpose: A fundamental question for Alcohol use disorder (AUD) is how and when naïve brain networks are reorganized in response to alcohol consumption. The current study aimed to determine the progression of alcohol’s effect on functional brain networks during transition from the naïve state to chronic consumption.

Procedures: Resting-state brain networks of six female rhesus macaque (Macaca mulatta) monkeys were acquired using magnetoencephalography (MEG) prior to alcohol exposure and after free-access to alcohol using a well-established model of chronic heavy alcohol consumption. Functional brain network metrics were derived at each time point.

Results: The average connection frequency (p < 0.024) and membership of the Rich Club (p < 0.022) changed significantly over time. Metrics describing network topology remained relatively stable from baseline to free-access drinking. The minimum degree of the Rich Club prior to alcohol exposure was significantly predictive of future free-access drinking (r = −0.88, p < 0.001).

Conclusions: Results suggest naïve brain network characteristics may be used to predict future alcohol consumption, and that alcohol consumption alters functional brain networks, shifting hubs and Rich Club membership away from previous regions in a non-systematic manner. Further work to refine these relationships may lead to the identification of a high-risk drinking phenotype.

An EEG investigation of alpha and beta activity during resting states in adults with Williams syndrome

Background

Williams syndrome (WS) is neurodevelopmental disorder characterised by executive deficits of attention and inhibitory processing. The current study examined the neural mechanisms during resting states in adults with WS in order to investigate how this subserves the attention and inhibitory deficits associated with the syndrome.

Method

Adopting electroencephalography (EEG) methodology, cortical electrical activity was recorded from eleven adults with WS aged 35 + years during Eyes Closed (EC) and Eyes Open (EO) resting states, and compared to that of thirteen typically developing adults matched for chronological age (CA) and ten typically developing children matched for verbal mental ability (MA). Using mixed-design analyses of variance (ANOVA), analyses focused on the full alpha (8–12.5 Hz), low-alpha (8–10 Hz), upper-alpha (10–12.5 Hz), and beta (13–29.5 Hz) bands, as these are thought to have functional significance with attentional and inhibitory processes.

Results

No significant difference in alpha power were found between the WS and CA groups across all analyses, however a trend for numerically lower alpha power was observed in the WS group, consistent with other developmental disorders characterised by attentional/inhibitory deficits such as Attention Deficit Hyperactivity Disorder (ADHD). In contrast, comparable beta power between the WS and CA groups during both EC/EO conditions suggests that their baseline EEG signature is commensurate with successful attentional processing, though this needs to be interpreted with caution due to the small sample size. Analyses also revealed an unusual trend for low variability in the EEG signature of the WS group, which contradicts the heterogeneity typically observed behaviourally.

Conclusions

This novel finding of low variability in the EEG spectra in the WS group has been previously associated with poor behavioural performance in ADHD and is highly informative, highlighting future research needs to also consider how the role of low variability in the EEG profile of WS manifests in relation to their behavioural and cognitive profiles.

Electroencephalography resting-state networks in people with Stroke

INTRODUCTION

The purpose of this study was to characterize resting-state cortical networks in chronic stroke survivors using electroencephalography (EEG).

METHODS

Electroencephalography data were collected from 14 chronic stroke and 11 neurologically intact participants while they were in a relaxed, resting state. EEG power was normalized to reduce bias and used as an indicator of network activity. Correlations of orthogonalized EEG activity were used as a measure of functional connectivity between cortical regions.

RESULTS

We found reduced cortical activity and connectivity in the alpha (p < .05; p = .05) and beta (p < .05; p = .03) bands after stroke while connectivity in the gamma (p = .031) band increased. Asymmetries, driven by a reduction in the lesioned hemisphere, were also noted in cortical activity (p = .001) after stroke.

CONCLUSION

These findings suggest that stroke lesions cause a network alteration to more local (higher frequency), asymmetric networks. Understanding changes in cortical networks after stroke could be combined with controllability models to identify (and target) alternate brain network states that reduce functional impairment.

An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4-7 Hz, alpha: 8-13 Hz, low beta: 14-20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

A novel method to simultaneously record spinal cord electrophysiology and electroencephalography signals

The brain and the spinal cord together make up the central nervous system (CNS). The functions of the human brain have been the focus of neuroscience research for a long time. However, the spinal cord is largely ignored, and the functional interaction of these two parts of the CNS is only partly understood. This study developed a novel method to simultaneously record spinal cord electrophysiology (SCE) and electroencephalography (EEG) signals and validated its performance using a classical resting-state study design with two experimental conditions: eyes-closed (EC) and eyes-open (EO). We recruited nine postherpetic neuralgia patients implanted with a spinal cord stimulator, which was modified to record SCE signals simultaneously with EEG signals. For both EEG and SCE, similar differences were found in delta- and alpha-band oscillations between the EC and EO conditions, and the spectral power of these frequency bands was able to predict EC/EO behaviors. Moreover, causal connectivity analysis suggested a top-down regulation in delta-band oscillations from the brain to the spinal cord. Altogether, this study demonstrates the validity of simultaneous SCE-EEG recording and shows that the novel method is a valuable tool to investigate the brain-spinal interaction. With this method, we can better unite knowledge about the brain and the spinal cord for a deeper understanding of the functions of the whole CNS.

Default mode network activity in depression subtypes

Depression continues to carry a major disease burden worldwide, with limitations on the success of traditional pharmacological or psychological treatments. Recent approaches have therefore focused upon the neurobiological underpinnings of depression, and on the "individualization" of depression symptom profiles. One such model of depression has divided the standard diagnostic criteria into four "depression subtypes", with neurological and behavioral pathways. At the same time, attention has been focused upon the region of the brain known as the "default mode network" (DMN) and its role in attention and problem-solving. However, to date, no review has been published of the links between the DMN and the four subtypes of depression. By searching the literature studies from the last 20 years, 62 relevant papers were identified, and their findings are described for the association they demonstrate between aspects of the DMN and the four depression subtypes. It is apparent from this review that there are potential positive clinical and therapeutic outcomes from focusing upon DMN activation and connectivity, via psychological therapies, transcranial magnetic stimulation, and some emerging pharmacological models.

Characterization of Source-Localized EEG Activity During Sustained Deep-Tissue Pain

Musculoskeletal pain is a clinical condition that is characterized by ongoing pain and discomfort in the deep tissues such as muscle, bones, ligaments, nerves, and tendons. In the last decades, it was subject to extensive research due to its high prevalence. Still, a quantitative description of the electrical brain activity during musculoskeletal pain is lacking. This study aimed to characterize intracranial current source density (CSD) estimations during sustained deep-tissue experimental pain. Twenty-three healthy volunteers received three types of tonic stimuli for three minutes each: computer-controlled cuff pressure (1) below pain threshold (sustained deep-tissue no-pain, SDTnP), (2) above pain threshold (sustained deep-tissue pain, SDTP) and (3) vibrotactile stimulation (VT). The CSD in response to these stimuli was calculated in seven regions of interest (ROIs) likely involved in pain processing: contralateral anterior cingulate cortex, contralateral primary somatosensory cortex, bilateral anterior insula, contralateral dorsolateral prefrontal cortex, posterior parietal cortex and contralateral premotor cortex. Results showed that participants exhibited an overall increase in spectral power during SDTP in all seven ROIs compared to both SDTnP and VT, likely reflecting the differences in the salience of these stimuli. Moreover, we observed a difference is CSD due to the type of stimulus, likely reflecting somatosensory discrimination of stimulus intensity. These results describe the different contributions of neural oscillations within these brain regions in the processing of sustained deep-tissue pain.

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

Effective Connectivity for Default Mode Network Analysis of Alcoholism

Introduction: With the recent technical advances in brain imaging modalities such as magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging (fMRI), researchers' interests have inclined over the years to study brain functions through the analysis of the variations in the statistical dependence among various brain regions. Through its wide use in studying brain connectivity, the low temporal resolution of the fMRI represented by the limited number of samples per second, in addition to its dependence on brain slow hemodynamic changes, makes it of limited capability in studying the fast underlying neural processes during information exchange between brain regions. Materials and Methods: In this article, the high temporal resolution of the electroencephalography (EEG) is utilized to estimate the effective connectivity within the default mode network (DMN). The EEG data are collected from 20 subjects with alcoholism and 25 healthy subjects (controls), and used to obtain the effective connectivity diagram of the DMN using the Partial Directed Coherence algorithm. Results: The resulting effective connectivity diagram within the DMN shows the unidirectional causal effect of each region on the other. The variations in the causal effects within the DMN between controls and alcoholics show clear correlation with the symptoms that are usually associated with alcoholism, such as cognitive and memory impairments, executive control, and attention deficiency. The correlation between the exchanged causal effects within the DMN and symptoms related to alcoholism is discussed and properly analyzed. Conclusion: The establishment of the causal differences between control and alcoholic subjects within the DMN regions provides valuable insight into the mechanism by which alcohol modulates our cognitive and executive functions and creates better possibility for effective treatment of alcohol use disorder.