Introduction:The ability to recognize emotional facial expressions relies on brain functional networks, particularly in the right hemisphere, and is impaired in neurodegenerative conditions such as Alzheimer's disease and Parkinson's disease. Given that this ability involves brain networks operating within hundreds of milliseconds, we tested the hypothesis of abnormalities in event-related spectral electroencephalographic coherence, with a focus on the right versus left hemispheres in these patients. Methods:Event-related theta (4-7 Hz) magnitude-squared coherence was calculated for both intra-hemispheric (frontal-temporal, frontal-parietal, central-temporal, and related pairs) and inter-hemispheric homologous electrode pairs (F3-F4, C3-C4, T7-T8, TP7-TP8, P3-P4, O1-O2). We enrolled 25 patients with amnestic mild cognitive impairment, 15 Parkinson's disease patients with mild cognitive impairment, 25 Alzheimer's disease patients with dementia, and 16 Parkinson's disease patients with dementia, along with 25 healthy elderly as controls. Participants were presented with three different facial expressions (angry, happy, and neutral) 60 times each in a pseudo-random order. Results:Theta (4-7 Hz) spectral coherence was higher in the right than the left hemisphere across all groups, with Parkinson's disease patients exhibiting the lowest values. Moreover, interhemispheric theta coherence was lower in Parkinson's disease patients with mild cognitive impairment and dementia compared to the amnestic mild cognitive impairment and healthy elderly groups. Discussion:These findings indicate that cortical functional connectivity related to emotional facial expression processing is more disrupted in Parkinson's disease than in Alzheimer's disease, at both mild cognitive impairment and dementia stages. This disruption affects not only the right hemisphere but also interhemispheric connectivity. Finally, it would occur at theta frequencies.

Amputation may disrupt body schema through impairments in attentional mechanisms within the central nervous system. This study examined behavioral performance and low-frequency oscillatory (LFO) activity in individuals with upper-limb amputations (ULA), lower-limb amputations (LLA), and healthy controls (HC). Participants included six ULA (5 Male, 1 Female), nine LLA (7 Male, 2 Female), and eleven HC (8 Male, 3 Female). During electroencephalography (EEG) recording, participants performed a hand laterality task with stimuli varying in laterality (right vs. left) and angular orientation (0° vs. 180°). EEG was recorded before and after lateralization training. Accuracy was higher for stimuli presented at 0° than at 180° (p < .05), and overall accuracy significantly improved following training (p < .001). ULA exhibited slower reaction times than LLA (p < .05). EEG results showed a greater increase in delta (1.5-3 Hz) power in ULA compared with HC (p < .05). Right-hand stimuli and those presented at 0° modulated delta phase responses (p < .05). Increased theta (4-7 Hz) power and phase responses after training suggest enhanced oscillatory entrainment through attentional engagement (p < .05). Overall, behavioral and electrophysiological findings provide insight into bodily attention mechanisms in amputees and have implications for neurorehabilitation. Future studies with larger and more homogeneous samples are needed to improve generalizability and clarify underlying mechanisms.

Brain clocks are promising tools for evaluating brain health. However, most current methods rely on structural neuroimaging. Functionally based approaches remain scarce, especially for assessing age-related neurodegenerative diseases. This study examines whether the brain age gap (BAG), the difference between chronological and predicted brain age, reflects neurodegeneration when estimated from electroencephalographic resting-state (rsEEG) α-oscillations, a well-established marker of brain functional aging. It also explores whether α-based brain clocks reflect sociodemographic diversity and structural inequality. The BAG was computed using spectral descriptors of α-activity in the rsEEG source space of 1228 healthy participants, individuals with mild cognitive impairment (MCI), and patients with Alzheimer's disease or behavioral variant frontotemporal dementia, residing in 10 countries with varying levels of structural inequality. BAGs are increased in MCI and dementia groups, particularly in posterior cortical regions. Structural inequality emerges as the strongest predictor of BAG, surpassing cognition, education, and sex. The findings indicate that an α-oscillation-based brain clock provides a sensitive functional marker of brain aging, capable of capturing neurodegenerative processes as well as the impact of social disparities. This scalable, accessible approach to brain health shows promise for translational use and population-wide screening in underserved, resource-limited settings.

Children with low birth weight (LBW) often exhibit poorer social and cognitive abilities than their normal birth weight (NBW) peers. These difficulties may persist into young adulthood and are accompanied by altered resting-state electroneurophysiological patterns. Considering the results of these studies, understanding the neural substrates of birth weight becomes more important. Here, we used EEG event-related oscillations to unravel differences between children born with low and normal birth weights during a visual memory encoding to explore further the birth weight effect on the brain oscillatory responses of 6-to-7-year-old children. This study used an observational, cross-sectional, between-group design comparing LBW/preterm (N = 15) and NBW (N = 17) children. EEGs of the children were recorded during the encoding phase of visual memory task. Additionally, all children were administered two subtests from the WISC-IV IQ assessment: working memory (WM) and processing speed (PS). We employed time-frequency analysis to investigate event-related brain oscillatory pattern to distinguish differentiation between LBW/preterm and NBW. Consistent with previous studies, our behavioral results demonstrated that LBW/preterm group scored worse on WM and PS than their NBW peers. EEG results highlighted a topological differentiation between LBW/preterm and NBW groups, with greater posterior event-related alpha and especially beta responses in the LBW/preterm group during item encoding. Importantly, posterior beta oscillatory responses negatively correlated with the PS score in NBW children. Based on our results, the differences in cognitive abilities in the LBW/ preterm group may be associated with posterior overactivity, characterized by increased eventrelated alpha and beta responses. This overactivity may reflect a greater reliance on perceptual processing strategies during memory encoding in early school-aged children born with low birth

BackgroundHuman cognition is derived from functional cortical long-range connectivity, as reflected by phase synchronization between electrode pairs of low-frequency electroencephalographic (EEG) activity <8 Hz related to cognitive tasks.MethodsWe tested the hypothesis that such an EEG marker, combined with machine learning, can discriminate between Parkinson’s disease (PD) with mild cognitive impairment (MCI) and dementia (D) and those with dementia with Lewy bodies (DLB). Event-related EEG delta (1–3.5 Hz) and theta (4–7 Hz) phase coherence were computed from EEG activity recorded during a visual oddball task in healthy controls (HC, N = 24) and PD-MCI (N = 20), PDD (N = 18), and DLB (N = 11) patients.ResultsUsing delta-band coherence as input, the comparison between HC and PD-MCI yielded an AUC of approximately 0.79 and an accuracy of 86.4%. Higher discriminative performance was observed for HC versus PDD, reaching an AUC near 0.92 with an overall accuracy of 94.6%. In the classification of HC versus DLB participants, the model achieved 83.3% sensitivity and 88.9% specificity, with an AUC around 0.77. Theta-band models showed comparable results, with average AUC values of about 0.75 for HC vs. DLB and slightly above 0.80 for HC vs. PDD, while classification of HC vs. PD-MCI remained in a moderate range.ConclusionThese findings suggest that event-related EEG phase coherence at <8 Hz is a promising EEG correlate of cognitive deficits in patients with PDD and DLB, offering insights into disrupted network dynamics of cortical activity related to cognitive processes and potential biomarkers for testing new drugs for cognitive enhancement and disease monitoring.

Brain clocks track the deviations between predicted brain age and chronological age (brain age gaps, BAGs). These BAGs can be used to measure accelerated aging, monitoring deviations from the healthy brain trajectories associated with brain diseases and different cumulative burdens. However, the underlying biophysical mechanisms associated with BAGs in aging and dementia remain unclear. Here, we combine source space connectivity (EEG) with generative brain modeling in healthy controls (HCs) from the global south and north, alongside Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) patients (N=1,399). BAGs in aging were influenced by geography (south>north), income (low>high), sex (female>male), and education (low>high), with larger BAGs in patients, especially females with AD. Biophysical modeling revealed BAGs related to hyperexcitability and structural disintegration in aging, while hypoexcitability and severe disintegration were linked to dementia. Our work sheds light on the biophysical mechanisms of accelerated aging and dementia in diverse populations.

IntroductionFrequent exposure to video gaming induces changes in cognitive and perceptual functions and alterations in neural structure and functioning. While frequent video gaming has been associated with positive effects in cognitive-perceptual domains, it may concurrently exert adverse impact on social–emotional functioning. This study used event-related EEG brain oscillations to investigate the effect of frequent playing video games on the visual and auditory working memory processes.MethodsThe study included 23 healthy young men participants, divided into frequent gamer and infrequent gamer groups based on their exposure to violent video games. An internet-based questionnaire was used for group classification, and the frequent gamer group consisted of participants who played more than 15 h of video games per week. EEG recordings were obtained during visual and auditory memory tasks, and participants’ anxiety levels were assessed using the State–Trait Anxiety Inventory. Event-related power spectrum and phase-locking analyses were conducted for delta, theta, and alpha frequencies.ResultsNo significant group differences were observed in behavioral performance and anxiety levels; however, there were notable electrophysiological differences. The frequent gamers exhibited lower and shorter visual delta responses compared to infrequent gamers. A left-hemisphere dominance for the frequent gamers was observed in auditory theta and alpha power, particularly in the parietal and occipital regions. Additionally, the frequent gamers showed reduced visual alpha power in posterior regions and less increase in auditory lower alpha phase-locking.DiscussionIn conclusion, the observed alterations in low-frequency event-related oscillations suggest that the frequent gamers employ distinct neurocognitive strategies during memory tasks. These strategies may reflect enhanced efficiency in specific domains such as attention and memory, despite similar behavioral performance.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Miners working underground face some risk factors that affect the nervous system-such as high noise, dark environment, chronic stress, and exposure to toxic gases. However, it is not known whether these risk factors affect the cognition of miners. In this study, the cognitive changes of miners were examined through event-related oscillations via electroencephalogram (EEG). Twenty underground miners and control groups, equal to each other in age, education level, and working duration, participated in this study. Neuropsychological tests were applied to all participants to examine their cognitive characteristics. Then, 20-channel EEG was recorded for electrophysiological changes during visual oddball paradigm. Event-related power spectrum and phase locking were analyzed in delta (0.5-3.5), theta (4-7), and alpha (8-13 Hz) frequency bands. It was determined that the delta responses that emerged during the target stimulus differed between the two groups in terms of phase locking (p < 0.05). Considering event-related alpha responses, a statistical difference was found regarding power spectrum and phase locking (p < 0.05). Moreover, the alpha power spectrum in the miners was found to be negatively statistically correlated with working duration (p < 0.05). This study determined that the event-related electrophysiological responses of the miners were negatively affected depending on the working conditions. In addition, neuropsychological assessment determined miners had deficiencies in learning and memory skills and many other cognitive functions such as attention, behavioral inhibition, and visual perception.

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.

Oddball task-related EEG delta and theta responses are associated with frontal executive functions, which are significantly impaired in patients with dementia due to Parkinson's disease (PDD) and Lewy bodies (DLB). The present study investigated the oddball task-related EEG delta and theta responses in patients with PDD, DLB, and Alzheimer's disease dementia (ADD). During visual and auditory oddball paradigms, EEG activity was recorded in 20 ADD, 17 DLB, 20 PDD, and 20 healthy (HC) older adults. Event-related EEG power spectrum and phase-locking analysis were performed at the delta (1-4 Hz) and theta (4-7 Hz) frequency bands for target and nontarget stimuli. Compared to the HC persons, dementia groups showed lower frontal and central delta and theta power and phase-locking associated with task performance and neuropsychological test scores. Notably, this effect was more significant in the PDD and DLB than in the ADD. In conclusion, oddball task-related frontal and central EEG delta and theta responses may reflect frontal supramodal executive dysfunctions in PDD and DLB patients.

We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer's and Parkinson's disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting-state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson's disease patients.

Underground mine workers face many risk factors at work sites that are known to affect the neural system. Observational studies report that these risk factors precede neuromuscular and neurodegenerative disorders, especially in old-age miners. Neurodegenerative disorders have electrophysiological, anatomical, and functional changes long before symptoms are seen in older adults. Therefore, this study investigated whether risks faced by miners at young ages were reflected in electrophysiological signals. Twenty-one underground miners and twenty-two above-ground workers matched with them in terms of age, education, and working duration were included in this study. Participants were recorded with a 20-channel EEG during the resting-state (eyes open and closed; EO-EC) and the perception of the International Affective Picture System Paradigm (IAPS). Time-frequency analyses were performed for alpha frequency. Rs-EEG results showed a statistically significant difference in alpha power between the EO and EC states in the control group. However, there was no statistical difference in alpha power between these two conditions in the miners. Additionally, we noted a more pronounced decrease in alpha responses in the posterior region during EC in the miners. The group's main effects were statistically significant in event-related alpha responses during emotional responses. Accordingly, event-related alpha responses of the miner group were lower than the control group in terms of both power spectrum and phase-locking. Underground mine workers are cognitively and emotionally affected by risks in the work environment. Electrophysiological changes seen in young underground workers may be a harbinger of neurodegenerative disorders in miners' old age. Our research findings may lead to the development of occupational neuroscience, social policies, and worker health, which are necessary to improve working conditions for mineworkers.

Here, we hypothesized that the reactivity of posterior resting-state electroencephalographic (rsEEG) alpha rhythms during the transition from eyes-closed to -open condition might be lower in patients with Parkinson's disease dementia (PDD) than in patients with Alzheimer's disease dementia (ADD). A Eurasian database provided clinical-demographic-rsEEG datasets in 73 PDD patients, 35 ADD patients, and 25 matched cognitively unimpaired (Healthy) persons. The eLORETA freeware was used to estimate cortical rsEEG sources. Results showed substantial (greater than -10%) reduction (reactivity) in the posterior alpha source activities from the eyes-closed to the eyes-open condition in 88% of the Healthy seniors, 57% of the ADD patients, and only 35% of the PDD patients. In these alpha-reactive participants, there was lower reactivity in the parietal alpha source activities in the PDD group than in the healthy control seniors and the ADD patients. These results suggest that PDD patients show poor reactivity of mechanisms desynchronizing posterior rsEEG alpha rhythms in response to visual inputs. That neurophysiological biomarker may provide an endpoint for (non) pharmacological interventions for improving vigilance regulation in those patients.

Here we tested the exploratory hypothesis that resting-state eyes-closed electroencephalographic (rsEEG) alpha rhythms may predict and be sensitive to the Alzheimer’s disease mild cognitive impairment (ADMCI) progression at a 6-month follow-up (a relevant feature for intervention clinical trials).Clinical, neuroimaging, and rsEEG datasets in 52 ADMCI and 60 matched healthy elderly (Healthy) seniors were available from an international archive (www.pdwaves.eu). The rsEEG frequency bands were individual delta, theta, and alpha (defined based on its posterior topography and “reactivity” to the eyes-open condition) or background frequency (BGF), as well as fixed beta (14-30 Hz) and gamma (30-40 Hz). Cortical source estimation was performed by eLORETA freeware.Results showed a substantial (> -10%) reduction in the posterior alpha source activities during the eyes-open condition in about 90% and 70% of the Healthy and ADMCI participants, respectively. In the younger ADMCI participants (mean age of 64.3±1.1) with “reactive” rsEEG alpha source activities, posterior alpha source activities during the eyes-closed condition predicted the global cognitive status at 6-month follow-up. In all younger ADMCI participants with “reactive” rsEEG alpha source activities, posterior alpha source activities during the eyes-closed condition reduced in magnitude at that follow-up. These effects could not be explained by neuroimaging and neuropsychological biomarkers of AD.These results suggest that in ADMCI patients, the true (“reactive”) posterior rsEEG alpha rhythms, when present, predict (in relation to younger age) and are quite sensitive to the effects of the disease progression on neurophysiological mechanisms underpinning vigilance regulation.

Here we tested the hypothesis of a relationship between the cortical default mode network (DMN) structural integrity and the resting state electroencephalographic (rsEEG) rhythms in patients with Alzheimer's disease with dementia (ADD). Clinical and instrumental datasets in 45 ADD patients and 40 normal elderly (Nold) persons originated from the PDWAVES Consortium (www.pdwaves.eu). Individual rsEEG delta, theta, alpha, and fixed beta and gamma bands were considered. Freeware platforms served to derive (1) the (gray matter) volume of the DMN, dorsal attention (DAN), and sensorimotor (SMN) cortical networks and (2) the rsEEG cortical eLORETA source activities. We found a significant positive association between the DMN gray matter volume, the rsEEG alpha source activity estimated in the posterior DMN nodes (parietal and posterior cingulate cortex), and the global cognitive status in the Nold and ADD participants. Compared with the Nold, the ADD group showed lower DMN gray matter, lower rsEEG alpha source activity in those nodes, and lower global cognitive status. This effect was not observed in the DAN and SMN. These results suggest that the DMN structural integrity and the rsEEG alpha source activities in the DMN posterior hubs may be related and predict the global cognitive status in ADD and Nold persons.