Search Results (24)

Introduction: Chewing gum is a widespread, seemingly mundane behaviour that has been linked to diverse benefits such as improved cognitive performance, reduced stress, and enhanced alertness. While animal and human research indicate that mastication engages extensive sensorimotor networks and may also modulate higher-order cognitive and emotional processes, questions remain about the specific neural mechanisms involved. This review combines findings from neuroimaging studies—including fMRI, fNIRS, and EEG—that investigate how chewing gum alters brain activity in humans. Methods: Using a targeted search strategy, we screened the major databases (PubMed/Medline, Scopus, ResearchGate, Google Scholar, and Cochrane) from January 1980 to March 2025 for clinical studies published in English. Eligible studies explicitly measured brain activity during gum chewing using EEG, fNIRS, or fMRI. Results: After a title/abstract screening and a full-text review, thirty-two studies met the inclusion criteria for this review: 15 utilising fMRI, 10 using fNIRS, 2 using both fNIRS and EEG, and 5 employing EEG. Overall, the fMRI investigations consistently reported strong activation in bilateral motor and somatosensory cortices, the supplementary motor area, the insula, the cerebellum, and the thalamus, during gum chewing, with several studies also noting involvement of higher-order prefrontal and cingulate regions, particularly under stress conditions or when participants chewed flavoured gum. The fNIRS findings indicated that chewing gum increased oxygenated haemoglobin in the prefrontal cortex, reflecting heightened cortical blood flow; these effects were often amplified when the gum was flavoured or when participants were exposed to stressful stimuli, suggesting that both sensory and emotional variables can influence chewing-related cortical responses. Finally, the EEG studies documented transient increases in alpha and beta wave power during gum chewing, particularly when flavoured gum was used, and reported short-lived enhancements in vigilance or alertness, which tended to subside soon after participants ceased chewing. Conclusions: Neuroimaging data indicate that chewing gum reliably engages broad sensorimotor circuits while also influencing regions tied to attention, stress regulation, and possibly memory. Although these effects are often short-lived, the range of outcomes—from changes in cortical oxygenation to shifts in EEG power—underscores chewing gum’s capacity to modulate brain function beyond simple oral motor control. However, at this time, the neural changes associated with gum chewing cannot be directly linked to the positive behavioural and functional outcomes observed in studies that measure these effects without the use of neuroimaging techniques. Future research should address longer-term impacts, refine methods to isolate flavour or stress variables, and explore potential therapeutic applications for mastication-based interventions. Full article

Background and Objectives: Neuropsychiatric disorders, including schizophrenia, bipolar disorder, and major depression, constitute a leading global public health challenge due to their high prevalence, chronicity, and profound cognitive and functional impact. This systematic review explores the role of electroencephalography (EEG)-based cognitive biomarkers in improving the understanding, diagnosis, monitoring, and treatment of these conditions. It evaluates how EEG-derived markers can reflect neuro-cognitive dysfunction and inform personalized and scalable mental health interventions. Materials and Methods: A systematic review was conducted following PRISMA guidelines. The databases searched included PubMed, Scopus, PsycINFO, and Web of Science for peer-reviewed empirical studies published between 2014 and 2025. Inclusion criteria focused on EEG-based investigations in clinical populations with neuropsychiatric diagnoses, emphasizing studies that assessed associations with cognitive function, symptom severity, treatment response, or functional outcomes. Of the 447 initially identified records, 132 studies were included in the final synthesis. Results: This review identifies several EEG markers—such as mismatch negativity (MMN), P300, frontal alpha asymmetry, and theta/beta ratios—as reliable indicators of cognitive impairments across psychiatric populations. These biomarkers are associated with deficits in attention, memory, and executive functioning, and show predictive utility for treatment outcomes and disease progression. Methodological trends indicate an increasing use of machine learning and multimodal neuroimaging integration to enhance diagnostic specificity. While many studies exhibit moderate risk of bias, the overall findings support EEG biomarkers’ reproducibility and translational relevance. Conclusions: EEG-based cognitive biomarkers offer a valuable, non-invasive means of capturing the neurobiological underpinnings of psychiatric disorders. Their diagnostic and prognostic potential, as well as high temporal resolution and portability, supports their use in clinical and public health contexts. The field, however, requires further standardization, cross-validation, and investment in scalable applications. Advancing EEG biomarker research holds promise for precision psychiatry and proactive mental health strategies at the population level. Full article

The incidence of cutaneous squamous cell carcinoma (cSCC) is increasing, with UV radiation being the main cause. Other risk factors are age, sex, skin type and immunosuppression. Human papillomaviruses (HPVs) are associated with benign and malignant skin tumours. In contrast to anogenital and oropharyngeal carcinomas, which are caused by alpha papillomaviruses, the HPV types associated with cSCC belong to the beta-HPV genus. These viruses infect the skin epithelium and are widespread in skin samples from healthy people. It is assumed that HPV amplifies the DNA damage caused by UV radiation and disrupts the repair mechanisms of the cells, without remaining permanently detectable in the tumour tissue, the so-called hit-and-run theory. The HPV status of tumours appears to have a positive influence on prognosis and response to therapy due to increased immune infiltration, in particular by tissue-resident memory T cells and activation of immune effector cells. This favours responses to immunotherapies such as PD-1/PD-L1 inhibitors, whereas immunosuppression may promote a pro-carcinogenic effect. In conclusion, the role of beta HPV in the development of cSCC appears to be closely associated with the immune status of the host. Depending on the immune status, beta HPV can play either a protective or a tumour-promoting role, and in view of the increasing incidence of skin cancer worldwide, enhancing the immune response against virus-infected keratinocytes, e.g., through HPV vaccination, could represent a promising approach for the prevention and therapy of squamous cell carcinomas. Full article

Cognitive fatigue (CF) is a critical factor affecting performance and well-being. It can be altered in suboptimal sleep quality conditions, e.g., in patients suffering from obstructive sleep apnea who experience both intermittent hypoxia and sleep fragmentation (SF). Understanding the neurophysiological basis of SF in healthy individuals can provide insights to improve cognitive functioning in disrupted sleep conditions. In this electroencephalographical (EEG) study, we investigated in 16 healthy young participants the impact of experimentally induced SF on the neurophysiological correlates of CF measured before, during, and after practice on the TloadDback, a working memory task tailored to each individual’s maximal cognitive resources. The participants spent three consecutive nights in the laboratory two times, once in an undisrupted sleep (UdS) condition and once in an SF condition induced by non-awakening auditory stimulations, counterbalanced and performed the TloadDback task both in a high (HCL) and a low (LCL) cognitive load condition. EEG activity was recorded during wakefulness in the 5 min resting state immediately before and after, as well as during the 16 min of the TloadDback task practice. In the high cognitive load under a sleep-fragmentation (HCL/SF) condition, high beta power increased during the TloadDback, indicating heightened cognitive effort, and the beta and alpha power increased in the post- vs. pre-task resting state, suggesting a relaxation rebound. In the low cognitive load/undisturbed sleep (LCL/UdS) condition, low beta activity increased, suggesting a relaxed focus, as well as mid beta activity associated with active thinking. These findings highlight the dynamic impact of SF on the neurophysiological correlates of CF and underscore the importance of sleep quality and continuity to maintain optimal cognitive functioning. Full article

Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications. Full article

Electroencephalogram (EEG) studies have suggested compensatory brain overactivation in cognitively healthy (CH) older adults with pathological beta-amyloid(Aβ₄₂)/tau ratios during working memory and interference processing. However, the association between glutamatergic metabolites and brain activation proxied by EEG signals has not been thoroughly investigated. We aim to determine the involvement of these metabolites in EEG signaling. We focused on CH older adults classified under (1) normal CSF Aβ₄₂/tau ratios (CH-NATs) and (2) pathological Aβ₄₂/tau ratios (CH-PATs). We measured plasma glutamine, glutamate, pyroglutamate, and γ-aminobutyric acid concentrations using tandem mass spectrometry and conducted a correlational analysis with alpha frequency event-related desynchronization (ERD). Under the N-back working memory paradigm, CH-NATs presented negative correlations (r = ~−0.74–−0.96, p = 0.0001–0.0414) between pyroglutamate and alpha ERD but positive correlations (r = ~0.82–0.95, p = 0.0003–0.0119) between glutamine and alpha ERD. Under Stroop interference testing, CH-NATs generated negative correlations between glutamine and left temporal alpha ERD (r = −0.96, p = 0.037 and r = −0.97, p = 0.027). Our study demonstrated that glutamine and pyroglutamate levels were associated with EEG activity only in CH-NATs. These results suggest cognitively healthy adults with amyloid/tau pathology experience subtle metabolic dysfunction that may influence EEG signaling during cognitive challenge. A longitudinal follow-up study with a larger sample size is needed to validate these pilot studies. Full article

Making judgments of learning (JOLs) can reactively alter memory itself, a phenomenon termed the reactivity effect. The current study recorded electroencephalography (EEG) signals during the encoding phase of a word list learning task to explore the neurocognitive features associated with JOL reactivity. The behavioral results show that making JOLs reactively enhances recognition performance. The EEG results reveal that, compared with not making JOLs, making JOLs increases P200 and LPC amplitudes and decreases alpha and beta power. Additionally, the signals of event-related potentials (ERPs) and event-related desynchronizations (ERDs) partially mediate the reactivity effect. These findings support the enhanced learning engagement theory and the elaborative processing explanation to account for the JOL reactivity effect. Full article

A pathogenic mutation in presenilin-1 (PSEN1), His214Asn, was found in a male patient with memory decline at the age of 41 in Korea for the first time. The proband patient was associated with a positive family history from his father, paternal aunt, and paternal grandmother without genetic testing. He was diagnosed with early onset Alzheimer’s disease (EOAD). PSEN1 His214Asn was initially reported in an Italian family, where the patient developed phenotypes similar to the current proband patient. Magnetic resonance imaging (MRI) scans revealed a mild hippocampal atrophy. The amyloid positron emission tomography (amyloid-PET) was positive, along with the positive test results of the increased amyloid ß (Aβ) oligomerization tendency with blood. The PSEN1 His214 amino acid position plays a significant role in the gamma–secretase function, especially from three additional reported mutations in this residue: His214Asp, His214Tyr, and His214Arg. The structure prediction model revealed that PSEN1 protein His214 may interact with Trp215 of His-Trp cation-π interaction, and the mutations of His214 would destroy this interaction. The His-Trp cation-π interaction between His214 and Trp215 would play a crucial structural role in stabilizing the 4th transmembrane domain of PSEN1 protein, especially when aromatic residues were often reported in the membrane interface of the lipid–extracellular region of alpha helices or beta sheets. The His214Asn would alter the cleavage dynamics of gamma–secretase from the disappeared interactions between His214 and Trp215 inside of the helix, resulting in elevated amyloid production. Hence, the increased Aβ was reflected in the increased Aβ oligomerization tendency and the accumulations of Aβ in the brain from amyloid-PET, leading to EOAD. Full article

Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early detection of WM dysfunction is essential to predict the onset of MCI and AD. Artificial Intelligence (AI)-based algorithms are increasingly used to identify biomarkers for detecting subtle changes in loaded WM. This paper presents an approach using electroencephalograms (EEG), time-frequency signal processing, and a Deep Neural Network (DNN) to predict WM load in normal and MCI-diagnosed subjects. EEG signals were recorded using an EEG cap during working memory tasks, including block tapping and N-back visuospatial interfaces. The data were bandpass-filtered, and independent components analysis was used to select the best electrode channels. The Ensemble Empirical Mode Decomposition (EEMD) algorithm was then applied to the EEG signals to obtain the time-frequency Intrinsic Mode Functions (IMFs). The EEMD and DNN methods perform better than traditional machine learning methods as well as Convolutional Neural Networks (CNN) for the prediction of WM load. Prediction accuracies were consistently higher for both normal and MCI subjects, averaging 97.62%. The average Kappa score for normal subjects was 94.98% and 92.49% for subjects with MCI. Subjects with MCI showed higher values for beta and alpha oscillations in the frontal region than normal subjects. The average power spectral density of the IMFs showed that the IMFs (p = 0.0469 for normal subjects and p = 0.0145 for subjects with MCI) are robust and reliable features for WM load prediction. Full article

N-nitrosodiethylamine (NDEA) is a potential carcinogen known to cause liver tumors and chronic inflammation, diabetes, cognitive problems, and signs like Alzheimer’s disease (AD) in animals. This compound is classified as probably carcinogenic to humans. Usual sources of exposure include food, beer, tobacco, personal care products, water, and medications. AD is characterized by cognitive decline, amyloid-β (Aβ) deposit, tau hyperphosphorylation, and cell loss. This is accompanied by neuroinflammation, which involves release of microglial cytokines, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin 1β (IL-1β), by nuclear factor kappa B (NF-κB) upregulation; each are linked to AD progression. Weak PI3K/Akt insulin-signaling inhibits IRS-1 phosphorylation, activates GSK3β and promotes tau hyperphosphorylation. Metformin, an antihyperglycemic agent, has potent anti-inflammatory efficacy. It reduces proinflammatory cytokines such as IL-6, IL-1β, and TNF-α via NF-κB inhibition. Metformin also reduces reactive oxidative species (ROS) and modulates cognitive disorders reported due to brain insulin resistance links. Our study examined how NDEA affects spatial memory in Wistar rats. We found that all NDEA doses tested impaired memory. The 80 µg/kg dose of NDEA increased levels of Aβ1-42, TNF-α, and IL-6 in the hippocampus, which correlated with memory loss. Nonetheless, treatment with 100 mg/kg of metformin attenuated the levels of pro-inflammatory cytokines and Aβ1-42, and enhanced memory. It suggests that metformin may protect against NDEA-triggered memory issues and brain inflammation. Full article

The diagnosis of epilepsy primarily relies on the visual and subjective assessment of the patient’s electroencephalographic (EEG) or intracranial electroencephalographic (iEEG) signals. Neurophysiologists, based on their experience, look for characteristic discharges such as spikes and multi-spikes. One of the main challenges in epilepsy research is developing an automated system capable of detecting epileptic seizures with high sensitivity and precision. Moreover, there is an ongoing search for universal features in iEEG signals that can be easily interpreted by neurophysiologists. This article explores the possibilities, issues, and challenges associated with utilizing artificial intelligence for seizure detection using the publicly available iEEG database. The study presents standard approaches for analyzing iEEG signals, including chaos theory, energy in different frequency bands (alpha, beta, gamma, theta, and delta), wavelet transform, empirical mode decomposition, and machine learning techniques such as support vector machines. It also discusses modern deep learning algorithms such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our goal was to gather and comprehensively compare various artificial intelligence techniques, including both traditional machine learning methods and deep learning techniques, which are most commonly used in the field of seizure detection. Detection results were tested on a separate dataset, demonstrating classification accuracy, sensitivity, precision, and specificity of seizure detection. The best results for seizure detection were obtained with features related to iEEG signal energy (accuracy of 0.97, precision of 0.96, sensitivity of 0.99, and specificity of 0.96), as well as features related to chaos, Lyapunov exponents, and fractal dimension (accuracy, precision, sensitivity, and specificity all equal to 0.95). The application of CNN and LSTM networks yielded significantly better results (CNN: Accuracy of 0.99, precision of 0.98, sensitivity of 1, and specificity of 0.99; LSTM: Accuracy of 0.98, precision of 0.96, sensitivity of 1, and specificity of 0.99). Additionally, the use of the gradient-weighted class activation mapping algorithm identified iEEG signal fragments that played a significant role in seizure detection. Full article

Introduction: Post-stroke depression (PSD) has complex pathophysiology determined by various biological and psychological factors. Although it is a long-term complication of stroke, PSD is often underdiagnosed. Given the diagnostic role of quantitative electroencephalography (qEEG) in depression, it was investigated whether a possible marker of PSD could be identified by observing the evolution of the (Delta + Theta)/(Alpha + Beta) Ratio (DTABR), respectively the Delta/Alpha Ratio (DAR) values in post-stroke depressed patients (evaluated through the HADS-D subscale). Methods: The current paper analyzed the data of 57 patients initially selected from a randomized control trial (RCT) that assessed the role of N-Pep 12 in stroke rehabilitation. EEG recordings from the original trial database were analyzed using signal processing techniques, respecting the conditions (eyes open, eyes closed), and several cognitive tasks. Results: We observed two significant associations between the DTABR values and the HADS-D scores of post-stroke depressed patients for each of the two visits (V1 and V2) of the N-Pep 12 trial. We recorded the relationships in the Global (V1 = 30 to 120 days after stroke) and Frontal Extended (V2 = 90 days after stroke) regions during cognitive tasks that trained attention and working memory. For the second visit, the association between the analyzed variables was negative. Conclusions: As both our relationships were described during the cognitive condition, we can state that the neural networks involved in processing attention and working memory might go through a reorganization process one to four months after the stroke onset. After a period longer than six months, the process could localize itself at the level of frontal regions, highlighting a possible divergence between the local frontal dynamics and the subjective well-being of stroke survivors. QEEG parameters linked to stroke progression evolution (like DAR or DTABR) can facilitate the identification of the most common neuropsychiatric complication in stroke survivors. Full article

Tumor necrosis factor alpha (TNF-α) plays a vital role in Alzheimer’s disease (AD) pathology, and TNF-α inhibitors (TNFIs) modulate AD pathology. We fused the TNF-α receptor (TNFR), a biologic TNFI that sequesters TNF-α, to a transferrin receptor antibody (TfRMAb) to deliver the TNFI into the brain across the blood–brain barrier (BBB). TfRMAb-TNFR was protective in 6-month-old transgenic APP/PS1 mice in our previous work. However, the effects and safety following delayed chronic TfRMAb-TNFR treatment are unknown. Herein, we initiated the treatment when the male APP/PS1 mice were 10.7 months old (delayed treatment). Mice were injected intraperitoneally with saline, TfRMAb-TNFR, etanercept (non-BBB-penetrating TNFI), or TfRMAb for ten weeks. Biologic TNFIs did not alter hematology indices or tissue iron homeostasis; however, TfRMAb altered hematology indices, increased splenic iron transporter expression, and increased spleen and liver iron. TfRMAb-TNFR and etanercept reduced brain insoluble-amyloid beta (Aβ) 1-42, soluble-oligomeric Aβ, and microgliosis; however, only TfRMAb-TNFR reduced Aβ peptides, Thioflavin-S-positive Aβ plaques, and insoluble-oligomeric Aβ and increased plaque-associated phagocytic microglia. Accordingly, TfRMAb-TNFR improved spatial reference memory and increased BBB-tight junction protein expression, whereas etanercept did not. Overall, despite delayed treatment, TfRMAb-TNFR resulted in a better therapeutic response than etanercept without any TfRMAb-related hematology- or iron-dysregulation in aged APP/PS1 mice. Full article

Altered gut microbiota has been reported in individuals with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Previous research has suggested that specific bacterial species might be associated with the decline of cognitive function. However, the evidence was insufficient, and the results were inconsistent. To determine whether there is an alteration of gut microbiota in patients with MCI and AD and to investigate its correlation with clinical characteristics, the fecal samples from 94 cognitively normal controls (NC), 125 participants with MCI, and 83 patients with AD were collected and analyzed by 16S ribosomal RNA sequencing. The overall microbial compositions and specific taxa were compared. The clinical relevance was analyzed. There was no significant overall difference in the alpha and beta diversity among the three groups. Patients with AD or MCI had increased bacterial taxa including Erysipelatoclostridiaceae, Erysipelotrichales, Patescibacteria, Saccharimonadales, and Saccharimonadia, compared with NC group (p < 0.05), which were positively correlated with APOE 4 carrier status and Clinical Dementia Rating (correlation coefficient: 0.11~0.31, p < 0.05), and negatively associated with memory (correlation coefficient: −0.19~−0.16, p < 0.01). Our results supported the hypothesis that intestinal microorganisms change in MCI and AD. The alteration in specific taxa correlated closely with clinical manifestations, indicating the potential role in AD pathogenesis. Full article

The comprehensive knowledge regarding the immune response during coronavirus disease 2019 (COVID-19) vaccination is limited. The aim of this study was to longitudinally investigate not only the dynamic changes of peripheral lymphocyte subpopulations and cytokine levels but parallel changes of antibody levels against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Blood samples of 20 healthcare workers with two doses of COVID-19 vaccine were prospectively collected. The percentages of lymphocyte subpopulations from peripheral blood and cytokine production in lymphocytes with in vitro stimulation were assessed using eight-color flow cytometry. SARS-CoV-2 spike antibodies (anti-S Abs) and functional neutralizing antibodies (nAbs) were also measured. The relation between pre- and post-vaccination immunity was analyzed. There are 7 men and 13 women with a median age of 44.0 years (range: 25.7–59.5 years). The individuals had an increased percentage of lymphocytes at post-vaccination with statistical significance post first dose (p = 0.031). The levels of transitional cells (p = 0.001), such as plasmablasts (p < 0.001) and plasma cells (p = 0.031), were increased compared with pre-vaccination. Recent thymic emigrants of CD4+ T cells subsets were significantly higher at post-vaccination than those at pre-vaccination (p = 0.029). Intracellular levels of tumor necrosis factor-alpha, interferon-γ, interleukin (IL)-2, IL-21, transforming growth factor-beta and IL-17 produced by CD4+ T, CD8+ T, and natural killer cells were increased. All individual samples showed reactivity to anti-S Abs and the levels of nAbs were elevated after vaccination. The magnitude of adaptive immunity was associated with vaccine types and doses. Alterations of total memory B cells (p < 0.001), non-switched memory B cells (p = 0.016), and memory Treg cells (p < 0.001) were independent predictors for nAb levels. These findings might be helpful in elucidating the immune response of COVID-19 vaccination and in developing new strategies for immunization. Full article