ObjectiveAlzheimer's disease (AD) often presents visual hallucinations (VH) in late stages. Visual evoked potentials (VEPs) are noninvasive electrophysiological measures that reflect the functional integrity of the visual conduction pathway. This study uses visual evoked potentials (VEP) to assess visual pathway dysfunction and evaluates VEP as a biomarker for disease progression.MethodsA retrospective study of 112 AD patients (2016-2024) was conducted, categorizing individuals into VH and non-VH groups based on the presence of visual hallucinations. VEP testing assessed P100 latency and amplitude. Baseline characteristics and VEP parameters were compared between groups, and correlations with disease duration were analyzed.ResultsNo significant differences were observed between the two groups in terms of age, sex, years of education, homocysteine (HCY) levels, or Mini-Mental State Examination (MMSE) scores (p > 0.05). However, disease duration was significantly longer in the VH group than in the non-VH group (p = 0.00). VEP findings revealed a significantly prolonged P100 latency (p = 0.01) and reduced P100 amplitude (p = 0.00) in the VH group. Correlation analysis indicated a positive correlation between P100 latency and disease duration (r = 0.21, p = 0.03) and a negative correlation between P100 amplitude and disease duration (r = -0.34, p = 0.00), suggesting progressive impairment of the visual conduction pathway over the course of the disease.ConclusionAD patients with visual hallucinations exhibit more severe impairments in the integrity of the visual conduction pathway than those without hallucinations, as evidenced by prolonged P100 latency and decreased amplitude. These changes are closely associated with disease duration.

ObjectiveSince the pioneering work of Hans Berger in 1929 introducing the utility of human electroencephanlography (EEG) in psychiatry, a considerable amount of work has been devoted to the identification of pathophysiological mechanisms of mental diseases. However, how electrophysiology may be useful in clinical psychiatric settings is still matter of debate. Here we provide a summary of current emerging data and perspectives regarding the promising utility of various EEG tools in the treatment of mental diseases.Methods and ResultsIn this report we focus on new insights reported through the use of various EEG tools (quantitative EEG, QEEG; cognitive event-related potentials, ERPs) and some new EEG-based methods (Mobile Brain/Body Imaging or Artificial Intelligence algorithms) suggesting that their use might be helpful at the clinical level in the management of various forms of mental diseases.ConclusionGiven the encouraging results highlighting how these electrophysiological tools may be used with regard to mental disorders, continued efforts to better implement these EEG tools into psychiatric clinical settings remains one of the most pressing challenges for neurophysiologists.

Acceptance, nonjudgmental awareness of the present-moment experiences, is a central component of mindfulness. This study used a pretest-posttest design to examine whether a brief mindfulness intervention (MI) could increase self-reported acceptance and reduce affective reactivity to errors, as indexed by error-related negativity (ERN), error positivity (Pe), and post-error slowing (PES). Meditation-naïve participants (n = 121, ages 18-31 years, 69% female) were randomly assigned to either a mindfulness group, which engaged in 10 min of guided mindful breathing, or a control group, which listened to a Ted talk on green living. Both groups completed a Flanker task before and after the intervention to elicit errors under time pressure. Results showed that participants in the mindfulness group reported greater acceptance following the intervention; however, no corresponding changes were observed in ERN or PES. Instead, both groups showed practice effects, with faster reaction times and larger Pe amplitudes reflecting increased response certainty. These findings suggest that while a brief MI may enhance subjective acceptance, it may not be sufficient to alter neural or behavioral markers of affective error reactivity. Longer or more intensive mindfulness training may be required to influence these deeper cognitive and emotional processes.

This study examined the relationship between alpha activity fluctuations in resting-state electroencephalography (EEG) and the Beck Depression Inventory (BDI) and State-Trait Anxiety Inventory (STAI) scores. A novel approach was introduced using second-order derivatives of the alpha envelope to identify potential functional biomarkers for depression and anxiety conditions. Two 30-s eyes-closed epochs of 64-channel EEG data were collected from open dataset of 113 college-aged participants with the BDI and STAI scores. Metrics including mean positive (Ap) and negative (An) second-order derivatives, the Ap-An ratio, root mean square (RMS), and peak frequency of the alpha envelope were extracted. Correlations between these EEG metrics and scores on the BDI and STAI were analyzed. BDI (Spearman's rank correlation, rs = 0.253-0.304,) and STAI (rs = 0.222-0.339) scores showed significant but weak positive correlations with the Ap-An ratio, in the left frontal regions ( < .05, FDR-corrected). No significant correlation was found between envelope amplitude and either score. The Ap-An ratio at the frontal, temporal, and central electrodes, and peak alpha frequency at the electrodes including the parietal and occipital regions, were significantly higher in participants with BDI scores above 10 compared to those with scores of 10 or below ( < .05, FDR, Mann-Whitney U test). These findings suggest that the second-order derivatives of alpha envelope may serve as functional biomarkers for psychiatric disorders, differently from the frequency and amplitude. Further research is needed to confirm whether these EEG features reflect regional neural activity, such as excitatory and inhibitory activities.

BackgroundNeurological complications during Extracorporeal membrane oxygenation (ECMO) can result in long-term cognitive deficits. This exploratory study aimed to determine the association between continuous EEG (cEEG) findings and neurological outcome in adult patients undergoing ECMO,MethodsThis retrospective cohort study analyzed EEG characteristics, and clinical outcomes in adult ECMO patients at a tertiary care center. We included all adult ECMO patients from January 1, 2015, to July 31, 2019 including patients in whom cEEG was initiated ASAP with ECMO orders. EEG data were evaluated for association with clinical outcome. Clinical outcomes were assessed using the Glasgow Outcome Scale (GOS), modified Rankin Scale (mRS), and Cerebral Performance Category (CPC) scale at discharge and 6 months.ResultsAmong 329 ECMO encounters, 214 (65%) included cEEG monitoring. Low EEG voltage was associated with poor outcome (CPC, 52.4% vs 64% p = 0.001). EEG reactivity was associated with outcome at 6 months (CPC, 54.8% reactive and good outcome vs 67% unreactive and poor outcomes p = 0.005). The presence of predominant background frequency, normal voltage, was associated with good outcome while higher Mayo EEG grade with poor outcomes. In the limited subset of patients with Sequential Organ Failure Assessment (SOFA) Score, no significant differences were noted between patients with different Mayo EEG grades, EEG reactivity, or background changes. Higher SOFA scores were associated with poor outcomes.ConclusionscEEG monitoring may provide prognostic information for adult ECMO patients. It remains unclear if the EEG findings are solely reflective of underlying severity of illness or not.

This study investigated perceptual differences between adolescents with Autism Spectrum Disorder (ASD) and neurotypical individuals using a multidimensional approach involving quantitative electroencephalography (qEEG), biomechanical analysis with Movement Element Decomposition (MED), and facial microexpression tracking via FaceReader software. The study included 22 adolescents (8 with ASD and 14 controls), evaluated under four experimental conditions: rest (eyes open and closed) and exposure to visual/auditory stimuli. Findings indicated increased Delta band activity in the ASD group, absence of Alpha band reactivity, greater postural instability, altered oscillation patterns, and a predominance of neutral emotional expressions. The results suggest that individuals with ASD exhibit distinct patterns of sensory, motor, and emotional processing, highlighting the potential of these tools as biomarkers for diagnosis and intervention.

IntroductionTranscranial direct current stimulation (tDCS) is investigated as an adjunct treatment in schizophrenia, but electroencephalographic (EEG) studies have produced inconsistent findings.ObjectiveTo review the literature and elucidate the effects of tDCS on EEG variables in schizophrenia. Method: This is a systematic scoping review according to PRISMA guidelines, consulting four databases: PubMed (MEDLINE), Cochrane Library, Web of Science and ScienceDirect. It was structured following PIO framework (Population, Intervention, Outcome): P: schizophrenia; I: tDCS; O: any EEG variable. For data synthesis, each time a variable was investigated, it was counted as an occurrence.ResultsA total of twenty-five papers were included, totaling forty-two occurrences: twenty-five were event-related potentials and seventeen were based on spectral/power, connectivity or coherence variables. Most papers applied 20 min of 2 mA stimulation (76%), in a bicephalic montage. The most investigated variable was the MMN, followed by N100, P300, EEG coherence, gamma activity, beta and alpha power. N100 was the variable that responded most to tDCS stimulation, with 80% response rate. Gamma activity had 67% response, MMN showed 60%, coherence, alpha and beta power 50%. All papers investigating P300 reported no significant results. Other EEG parameters were investigated only once.ConclusionEEG changes induced by tDCS in schizophrenia predominantly affected the sensory-auditory potential N100, had a lesser impact on pre-attentive potential MMN, and showed no observable effect on higher-order cognitive potentials, such as P300. The modulatory effects of tDCS on cognition are still unclear. This review was registered at the Open Science Framework (osf.io/7yzrj).

BackgroundAlzheimer's disease is a neurodegenerative condition characterized by the accumulation of misfolded proteins disrupting connectivity between brain regions. Electroencephalography provides optimal temporal resolution for assessing neuronal communication.ObjectiveTo detect and compare the localization of brain rhythms and the directional flow of oscillatory activity among default mode network nodes during the resting state in patients with amnestic mild cognitive impairment (aMCI) and healthy older adults (HOA).MethodsWe recruited 94 aMCI patients and 66 HOA. We conducted functional localization and connectivity analyses using scalp recordings of neuronal activity, estimated by eLORETA approach. We calculated the effective connectivity by applying the isolated effective coherence method, allowing the frequency decomposition of the directional flow of oscillatory activity between pairs of brain regions. Eight brain regions from the default mode network were selected.ResultsAlthough trends in spectral power were noted, no statistically significant differences were found between groups. Concerning iCOH analysis, both groups showed increased information flow from the posterior to the anterior nodes. Specifically, the precuneus was dominant in transmitting information to the anterior nodes of the DMN. Furthermore, aMCI patients had lower effective connectivity values than HOA.ConclusionsiCOH analysis effectively profiles default mode nodes during the resting state, adding information on both localization and directionality of information flow, as well as the involved EEG oscillations. Furthermore, it is well-suited to detect between-group connectivity differences, suggesting its usefulness as a biomarker in the prodromal clinical stage of AD.

BackgroundsIdentifying state biomarkers in major depressive disorder (MDD) is critical for understanding neurobiological underpinnings of disorder. Quantitative electroencephalography (qEEG) has emerged as a promising tool for distinguishing stable versus dynamic neural alterations associated with MDD.MethodsThis study included 70 patients diagnosed with MDD and 98 healthy controls (HC). Resting-state qEEG recordings were obtained at three time points: baseline(T0), early treatment(T1), and late treatment(T2). Patients were categorized as responders(≥50%HDRS-21) or non-responders. Changes in absolute band power across delta, theta, alpha, beta, and gamma frequencies were compared with HCs. Associations between qEEG activity with HDRS and HARS scores at each time point were calculated.ResultsResponders showed longitudinal reductions in delta power with normalization toward HCs. Gamma activity increased marginally over time. Non-responders exhibited stable and elevated delta and alpha power that persisted across sessions. Decreased fronto-central delta and increased left fronto-central gamma power were also associated with improvement in depression and anxiety.ConclusionMDD Responders demonstrated state-dependent electrophysiological normalization, while non-responders show stable pattern with unchanged depressive state. These findings highlight the utility of qEEG state-markers in monitoring clinical improvement in depression.

ObjectiveMost existing studies on cognitive rehabilitation in epilepsy focus on patients undergoing epilepsy surgery or classify interventions based on epilepsy type. This study aimed to determine whether antiseizure medications (ASMs) cause cognitive dysfunction in epilepsy patients by using neuropsychological assessments and auditory event-related potentials (ERPs), and whether cognitive rehabilitation can reduce this potential impact.Materials and MethodsThe study included patients scheduled to begin ASM monotherapy. All participants first underwent a face-to-face Montreal Cognitive Assessment (MoCA). Auditory ERPs including P300 and N200 latencies, and N2 to P3 peak-to-peak amplitudes were recorded in the electrophysiology laboratory. Patients were randomly divided into two groups: Group A (no cognitive rehabilitation) and Group B (received cognitive rehabilitation). After two months, both MoCA and auditory ERP measurements were repeated, and the results were statistically analyzed.ResultsIn Group A, patients using carbamazepine (CBZ), zonisamide (ZNS), or valproic acid (VPA) showed a statistically significant decline in MoCA scores and auditory ERP results ( < .05), suggesting a protective role of rehabilitation. For topiramate (TPM), cognitive decline was weakly significant even with rehabilitation ( = .031).

Neurofeedback, a form of biofeedback using electroencephalography, enables individuals to self-regulate brain activity through operant conditioning. This technique shows promise as a non-invasive intervention for neuropsychiatric disorders like post-traumatic stress disorder (PTSD) and may improve cognitive functions such as attention and working memory. However, limited research, particularly using Z-Score neurofeedback, exists on its effects on PTSD-related symptoms, cognitive function, and identifying treatment-specific EEG markers. In this study, twenty-one individuals diagnosed with PTSD (17 females; mean age = 26.02 [ = 9.51]) received a diagnostic interview using the MINI Neuropsychiatric Interview and completed self-report measures on PTSD, depression, and insomnia symptoms. Participants completed 5-min eyes-open and eyes-closed EEG recordings and received 10 20-min Z-scoring neurofeedback sessions. Results indicated significant reductions in PTSD and insomnia symptoms, with the most pronounced effects observed in intrusion, negative alterations in cognition and mood, and arousal/reactivity symptoms. Additionally, executive attention improved post-treatment. Alterations in cognition and mood were negatively correlated with alpha power globally and positively correlated with beta power in the parietal region. Beta power at T3 significantly decreased following neurofeedback training. These findings provide further support for neurofeedback as a viable intervention for PTSD, with implications for both symptom reduction and cognitive enhancement. Future studies are needed to investigate individual differences in treatment response and assess long-term outcomes to improve the clinical applicability of this approach.

ObjectiveDepressive symptoms and cognitive impairment are two common complications of cerebral small vascular disease (CSVD). This study aimed to investigate the P300 representation in CSVD patients with depressive symptoms and its relationship with depressive symptoms.MethodsWe selected 242 patients with CSVD (depression: n = 56; non-depression: n = 186) and 30 healthy controls. The Self-Rating Depression Scale and Self-Rating Anxiety Scale scales were used to assess depressive and anxiety symptoms.The latency and amplitude of P300 components were measured using event-related potential (ERP) technique to assess cognitive dysfunction. Cognitive function was evaluated using Mini-mental state examination and Event-Related Potential P300 waves latency & amplitude. Finally, logistic regression model was used to analyze the relationship between P300 representation and depressive symptoms in CSVD patients.ResultsCompared with NPSD group and Control group, the latency of P300 (P3a and P3b wave groups) in PSD group was longer and the amplitude was lower. Multivariate Logistic regression analysis showed that temporal lobe infarction (OR = 10.878, 95% CI = 2.890-40.939), brainstem infarction (OR = 4.185, 95% CI = 1.544-11.341), SAS score (OR = 1.275, 95% CI = 1.174-1.385),and P3b amplitude (OR = 0.779, 95% CI = 0.635-0.957) were independently correlated with depressive symptoms in CSVD patients ( < .05).ConclusionCSVD patients with depressive symptoms had worse cognitive function, and abnormalities in P300 waves amplitude and latency were more pronounced. The amplitude of P3b in patients with CSVD is decreased, which is significantly correlated with the occurrence of depression.

Facial expressions play a vital role in non-verbal communication, conveying a wide range of emotions and messages. Although prior research achieved notable advances through architecture design or dataset-specific optimization, few studies have integrated multiple advanced techniques into a unified facial expression recognition (FER) pipeline. Addressing this gap, we propose a comprehensive approach that combines (i) multiple pre-trained CNNs, (ii) MTCNN-based face detection for improved facial region localization, and (iii) Grad-CAM-based interpretability. While MTCNN enhances the quality of face localization, it may slightly affect classification accuracy by focusing on cleaner yet more challenging samples. We evaluate four pre-trained models - DenseNet121, ResNet-50, ResNet18, and MobileNetV2 - on two datasets: Raf-DB and Cleaned-FER2013. The proposed pipeline demonstrates consistent improvements in interpretability and overall system robustness. The results emphasize the strength of integrating face detection, transfer learning, and interpretability techniques within a single framework can significantly enhance the transparency and reliability of FER systems. Combining FER with EEG-based systems significantly enhances the emotional intelligence of brain-computer interfaces, enabling more adaptive and personalized user experiences. With this approach the paper bridges the gap between affective computing and cognitive neuroscience, aligning closely EEG-centered interaction methodologies. Besides understanding the relationship between facial expressions of emotions and EEG signals will be an important study for literature.

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a severe autoimmune encephalitis that often demonstrates a favorable response to immunotherapy, including rituximab. While disease outcomes have been widely documented, longitudinal characterization of brain activity changes following treatment remains limited. Electroencephalography (EEG) source localization provides a non-invasive approach for assessing regional brain dynamics. We report a case of a 17-year-old male patient with anti-NMDAR encephalitis who underwent serial EEG recordings before and after rituximab administration, with source power spectral density analysis performed. Symptom improvement following rituximab corresponded with reductions in cortical and subcortical delta power alongside increases in cortical alpha power, while transient symptom exacerbation was associated with elevated delta and diminished alpha activity in the cortex. Cerebellar activity alterations were not observed alongside symptom variations. Moreover, pre-treatment EEG revealed extensive delta band activity in the right hemisphere, with right-sided hypermetabolism observed on F-FDG PET/CT. These findings underscore the potential of source-localized EEG as a promising tool for region-specific monitoring of brain activity in NMDAR encephalitis, warranting rigorous validation in larger patient cohorts.

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder affecting cognitive and behavioral functions, resulting in ongoing inattention, hyperactivity, and impulsivity. Early and accurate diagnosis is essential, but traditional methods mainly depend on questionnaire-based assessments, detailed interviews with individuals and their families, and reviews of medical history. These are then scored using standardized scales like the Conners Rating Scale, Vanderbilt ADHD Diagnostic Parent Rating Scale, and Adult ADHD Self-Report Scale. However, these methods are often subjective, time-consuming, and costly, which limits their usefulness for early diagnosis. The proposed approach seeks to improve ADHD diagnosis by using machine learning techniques applied to electroencephalogram (EEG) data. Two classifiers, Random Forest and AdaBoost, are used to identify complex patterns in EEG data. Feature selection is performed with the Reptile Search Algorithm combined with an autoencoder for feature extraction, which improves data representation and model accuracy. The performance of this approach is evaluated based on accuracy, precision, recall, F1-score, AUC, and statistical significance at a 95% confidence level. Random Forest outperformed AdaBoost, achieving 92.36% in precision, recall, accuracy, and F1-score, while AdaBoost reached 89.78% in these metrics. Random Forest showed better effectiveness than AdaBoost in distinguishing ADHD cases, with an ROC AUC score of 0.93 and higher diagnostic accuracy. The study demonstrates that machine learning offers a promising, objective, and reliable tool for diagnosis, providing effective alternatives to traditional ADHD assessments for timely intervention and improved treatment management.

ObjectivesChildhood Occipital Visual Epilepsy (COVE) is a self-limited epileptic syndrome that typically begins in late childhood or adolescence characterized by brief visual seizures. The recent 2022 International League Against Epilepsy (ILAE) classification distinguishes COVE from photosensitive occipital lobe epilepsy (POLE), emphasizing the absence of photic-induced seizures in COVE. In this study, we aimed to describe the clinical and electrophysiological features of patients with COVE diagnosed according to the new ILAE criteria.MethodsThis retrospective cohort study analyzed 30 patients diagnosed with COVE at a tertiary epilepsy center between 1988 and 2023. Patients were selected based on ILAE 2022 criteria, and all cases with intermittent photic stimulation (IPS)-induced seizures were excluded.ResultsMost patients (93%) presented with elementary visual hallucinations, such as colorful lights. Orofacial seizures occurred in 7%, and 37% had nocturnal seizures. EEG abnormalities were primarily occipital and resolved in 85% of cases over time. Generalized spike-wave discharges (GSWDs) were rare (5%), and only one patient developed juvenile myoclonic epilepsy during follow-up. At final follow-up, 77% of patients achieved seizure freedom, and 47% discontinued medication.ConclusionCOVE is an epileptic syndrome associated with a favorable prognosis. By excluding photosensitivity in light of the newly proposed diagnostic criteria from the ILAE, future research should focus on a more homogenous group of COVE patients to enhance understanding of this syndrome. Accurate classification using updated ILAE criteria allows for clearer clinical delineation and more reliable outcome predictions.

BackgroundSchizophrenia affects millions globally, with up to 30% showing resistance to standard antipsychotics. Clozapine is effective for treatment resistant schizophrenia (TRS), but its use is often delayed. This study explores Quantitative electroencephalogram (QEEG) as a tool to predict clozapine response in Indian TRS patients, aiming to support early, personalized treatment.AimThis study aims to predict treatment response to clozapine in TRS patients using quantitative electroencephalogram (QEEG) by assessing and comparing baseline and 6 weeks QEEG patterns and their changes in responders versus non-responders.Methods39 clozapine-naïve TRS patients were recruited at tertiary care hospital in North India and assessed using BPRS, GASS-C and EEG at baseline, 3 weeks and 6 weeks. EEG data were processed and analyzed for frequency band power to compare responders (≥20% BPRS improvement) and non-responders.ResultsOf the 39 patients included, 36 completed the study, with 67% classified as responders and 33% as non-responders. Responders showed significantly higher right temporal delta power at 3 and 6 weeks, with ROC analysis at 6 weeks yielding an Area under curve of 0.757 ( = .014). Statistically significant increases in delta and theta power were observed in responders.ConclusionsIncreased right temporal delta power was seen in responders, but changes were insufficient to reliably predict outcomes.

P3a is an event-related potential that reflects the involuntary orienting of attention to salient stimuli. Abnormalities in P3a have been described in schizophrenia, but it is not known when they arise over the course of illness and whether they are progressive. Previous longitudinal studies of P3a have been inconclusive because of the heterogeneity in the diagnosis of psychotic patients, lack of follow-up data on controls, and relatively short follow-up periods. P3a, elicited by novel sounds, was assessed in 21 patients with first-episode schizophrenia and 36 healthy controls at baseline and reassessed in 14 patients and 23 controls after an average follow-up of six years. The longitudinal evaluation showed that the P3a amplitude was reduced in patients compared to controls at baseline but did not differ between groups at follow-up. Although P3a was reduced over the six-year interval in both groups, the reduction was greater in controls compared to patients. Longitudinal findings suggest that the P3a amplitude deficit is present at the onset of schizophrenia. Normalization of P3a amplitudes in patients at follow-up may reflect the premature aging effect on P3a at the onset of illness, a floor effect in P3a amplitudes of both groups at follow-up, or the reversal of the P3a deficit in patients over time. Interestingly, at baseline, the P3a amplitude in patients without follow-up data did not differ from controls and was greater than in patients with follow-up data. Baseline findings indicate a heterogeneity within the first-episode schizophrenia group.

IntroductionFacial emotion recognition is impaired in schizophrenia and contributes to profound social impairments. Healthy adults exhibit larger N170 amplitudes to emotional compared to neutral faces. Preliminary evidence suggests an inability to modulate N170 amplitude by emotional expression during chronic stages of the illness. The present investigation examined N170 modulation by emotion among patients with chronic (ChSz) and first hospitalized (FHSz) schizophrenia.MethodsEEG was recorded from 26 FHSz and 28 ChSz participants as well as 19 young (YC) and 21 older (OC) matched controls. Participants were asked to detect neutral faces among happy, angry, disgusted, fearful, and sad faces. N170 amplitudes were measured from P9/P10 electrodes. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS).ResultsN170 amplitude modulation by facial emotion was observed across FHSz and YC ( < .001), though the typical right-hemisphere lateralization of this response observed in YC ( = .001) was absent in FHSz ( = .56). In contrast to OC ( = .009), ChSz did not exhibit N170 modulation by emotion ( = .32). Among ChSz, N170 modulation (mean N170 across emotional expressions minus N170 to neutral faces) at P9 were inversely correlated with PANSS negative scores (r = -.53).DiscussionResults suggests a progressive impairment of emotional facial expression processing as indexed by N170 modulation across illness stage. While losing the hemispheric specialization of face processing, FHSz exhibited preserved N170 amplitude modulation by facial emotion in contrast to ChSz. This deficit was also associated with negative symptoms, implicating progressive pathology of N170 generators in persistent and debilitating symptoms of the disorder.

This manuscript examines the pivotal role of neuroinflammation in the central nervous system (CNS), particularly considering the impact of the COVID-19 pandemic. Neuroinflammation serves as a defense mechanism against various insults, including toxins, infections, and trauma. However, if left untreated, neuroinflammation can become chronic, leading to significant symptomatic and structural brain damage. Notably, neuroinflammation can mimic psychological disorders, complicating diagnosis and treatment. Current diagnostic methods for neuroinflammation-such as lumbar punctures, MRIs, brain biopsies, blood tests, and PET scans-are often hindered by inaccuracy, invasiveness, and cost. This study posits that electroencephalography (EEG), particularly identifying spindling excessive beta (SEB) activity, offers a promising, non-invasive, and cost-effective alternative for detecting neuroinflammation. This study investigates the relationship between SEB activity and neuroinflammation, focusing on traumatic brain injury (TBI). Through statistical analysis of EEG data from 1,233 psychiatric patients, we identified and compared two groups: 75 non-benzodiazepine-using adults without TBI and 79 non-benzodiazepine using adults with TBI exhibiting SEB activity. We identified a significant prevalence of SEB in individuals with refractory psychiatric conditions, underscoring the significance of this biomarker for neuroinflammation. Furthermore, we examine the therapeutic implications of reducing SEB through interventions such as guanfacine combined with N-Acetyl Cysteine (NAC), photobiomodulation, and hyperbaric oxygen therapy, all of which have demonstrated efficacy in mitigating neuroinflammation. These findings suggest that EEG could play a transformative role in the early detection and management of neuroinflammatory conditions, paving the way for more personalized and effective treatments for mental health disorders.

BackgroundAttention deficit hyperactivity disorder (ADHD) is generally characterized as a neurodevelopmental disorder with age-specific cognitive deficits. Despite progressive symptoms, neurophysiological correlates of ADHD developmental trajectories remain underexplored. Event-related potentials (ERPs), previously showing ADHD-related abnormalities, offer a promising but underutilized method to investigate the dynamic changes of neurophysiology during ADHD development. This study aims to investigate age-specific abnormalities in auditory ERPs in children with ADHD and explore their implications for developmental cognitive deficits.MethodsA total of 631 medication-naive children with ADHD (4-15 years) and 109 age- and sex-matched typically developing controls were recruited. Participants were divided into five age groups (4-6, 7-8, 9-10, 11-13 and 14-15 years). Auditory ERPs (N100, P200, N200, P300) were recorded using the oddball paradigm at frontal (Fz), central (Cz), and parietal (Pz) midline electrodes. Group differences in component latencies and amplitudes were analyzed using corrected statistical tests.ResultsSignificant age-specific ERP abnormalities were observed: 4-6 years: Prolonged P200 latency at Fz ( = 2.98, df = 113,  = 0.003, Cohen's  = 0.47 [0.12-0.82]), Cz ( = 2.18, df = 113,  = 0.034, Cohen's  = 0.42 [0.05-0.79]), and Pz ( = 2.25, df = 113,  = 0.028, Cohen's  = 0.45 [0.08-0.82]) and P300 latency at Pz ( = 2.51, df = 113,  = 0.013, Cohen's  = 0.51 [0.14-0.88]) under target stimuli; reduced P200 amplitude at Cz ( = -2.53, df = 113,  = 0.013, Cohen's  = 0.63 [0.25-1.01]) and N100 amplitude at Pz ( = -2.12, df = 113,  = 0.039, Cohen's  = 0.59 [0.21-0.97]) under non-target stimuli. 7-8 years: Prolonged N100 latency at Fz ( = 2.75, df = 256,  = 0.006, Cohen's  = 0.56 [0.21-0.91]), Cz ( = 2.82, df = 256,  = 0.005, Cohen's  = 0.59 [0.24-0.94]), and Pz ( = 2.91, df = 256,  = 0.004, Cohen's  = 0.61 [0.26-0.96]) and N200 latency at Fz ( = 2.52, df = 256,  = 0.010, Cohen's  = 0.47 [0.12-0.82]), Cz ( = 2.09, df = 256,  = 0.037, Cohen's  = 0.42 [0.07-0.77]), and Pz ( = 2.15, df = 256,  = 0.030, Cohen's  = 0.44 [0.09-0.79]) under target stimuli. 9-10 years: Increased N100 amplitude at Pz ( = 2.28, df = 195,  = 0.030, Cohen's  = 0.53 [0.06-1.00]) under target stimuli; increased P200 amplitude at Fz ( = 2.89, df = 195,  = 0.002, Cohen's  = 0.67 [0.20-1.14]), Cz ( = 2.06, df = 195,  = 0.042, Cohen's  = 0.49 [0.02-0.96]), and Pz ( = 2.28, df = 195,  = 0.030, Cohen's  = 0.55 [0.08-1.02]) under non-target stimuli. 11-13 years: Prolonged P300 latency at Pz ( = 2.45, df = 129,  = 0.016, Cohen's  = 0.51 [0.13-0.89]) under target stimuli. 14-15 years: No significant differences in any ERP component (all  > 0.05).ConclusionsADHD children exhibit stage-specific ERP abnormalities, reflecting developmental deficits in inhibitory control (4-6 years), sensory attention allocation (7-8 years), irrelevant information filtering (9-10 years), and working memory maturation (11-13 years). These findings highlight the potential of ERPs as non-invasive biomarkers for age-tailored ADHD diagnosis and intervention.