Theta Power Research Articles

Characterization of neuronal oscillations in the prelimbic cortex, nucleus accumbens and CA1 hippocampus during object retrieval task in rats predisposed to early life stress

BackgroundEarly life stress (ELS) during the stress hypo-responsive period (SHRP) alters the curiosity-like behavior later during adolescence. Previous studies have shown maternal separation (MS) stress-induced heightened curiosity and associated risk-taking behavior in the object retrieval task (ORT). However, the neural correlates of curiosity in adolescent rats predisposed to early life stress remain unexplored. Hence, the present study aimed to investigate the neural oscillatory patterns and network characteristics in the regions implicated in curiosity behavior, such as the Prelimbic cortex (PL), Nucleus Accumbens (NAc), and CA1 of the Hippocampus. The local field potentials data were analysed to understand the neural activity patterns in these areas during the risky zone crossing and object retrieval phase of the ORT in MS rats and compared with the normal control (NC) group.ResultsIn comparison to NC, MS rats showed a reduction in the theta power at 8–12 Hz, beta power at 12–20 Hz, and gamma power at 20–40 Hz range in the PL during risky zone crossing time. MS rats also showed reduced cross-correlation between PL-CA1 and reduced theta coherence between NAc-CA1 during risky zone crossing. During the object retrieval phase, the MS rats showed reduced peak cross-correlation between PL-CA1 and PL-NAc. Behaviourally, MS rats displayed an increased preference for the curiosity platform and retrieved more hidden objects, thus accounting for a higher curiosity index than controls.ConclusionIn summary, a reduced synchronization between the PL, NAc, and CA1 during the object retrieval task indicates how early MS stress during a critical developmental period impacts the limbic circuit connectivity. This corresponded with enhanced curiosity index in adolescent MS rats, predicting an altered intrinsic motivation and hence a higher susceptibility to substance use disorders during adolescence.

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, and separated the aperiodic and oscillatory components of the signals to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

Effect of antipsychotic on mismatch negativity amplitude and evoked theta power in drug-naïve patients with schizophrenia

BackgroundRecurrent observations have indicated the presence of deficits in mismatch negativity (MMN) among schizophrenia. There is evidence suggesting a correlation between increased dopaminergic activity and reduced MMN amplitude, but there is no consensus on whether antipsychotic medications can improve MMN deficit in schizophrenia.MethodsWe conducted clinical assessments, cognitive function tests, and EEG data collection and analysis on 31 drug-naïve patients with schizophrenia. Comprehensive evaluation tools such as PANSS and MCCB. MMN amplitude was analyzed by event-related potential (ERP) approaches, evoked theta power was analyzed by event-related spectral perturbation (ERSP) approaches.ResultsOur findings indicate that antipsychotic treatment significantly improved clinical symptoms, as evidenced by reductions in PANSS positive, negative, general symptoms, and total scores (all p < 0.001). Cognitive function improvements were observed in language learning, working memory, and overall MCCB scores (p < 0.05), although other cognitive domains showed no significant changes. However, no significant improvements were noted in MMN amplitude and evoke theta power after four weeks of antipsychotic treatment (p > 0.05).ConclusionThese results suggest that while antipsychotic medications effectively alleviate clinical symptoms, their impact on MMN amplitude and evoke theta power deficit is limited in the short term. Moreover, the amelioration of cognitive impairment in individuals with schizophrenia is not readily discernible, and it cannot be discounted that the enhancement observed in language acquisition and working memory may be attributed to a learning effect. These findings underscore the complexity of the neurobiological mechanisms involved and highlight the need for further research to optimize individualized treatment strategies for schizophrenia.Trial RegistrationChiCTR2000038961, October 10, 2020.

Novelty triggers time-dependent theta oscillatory dynamics in cortical-hippocampal-midbrain circuitry

Rapid adaptation to novel environments is crucial for survival, and this ability is impaired in many neuropsychiatric disorders. Understanding neural adaptation to novelty exposure therefore has therapeutic implications. Here, I found that novelty induces time-dependent theta (4-12Hz) oscillatory dynamics in brain circuits including the medial prefrontal cortex (mPFC), ventral hippocampus (vHPC), and ventral tegmental area (VTA), but not dorsal hippocampus (dHPC), as mice adapt to a novel environment. Local field potential (LFP) recordings were performed while mice were freely behaving in a novel or a familiar arena for 10 min. Initially, mice exhibited increased exploratory behavior upon exposure to novelty, which gradually decreased to levels observed in mice exposed to the familiar arena. Over the same time course, the mPFC, vHPC, and VTA displayed progressively increasing theta power through novelty exposure. Additionally, theta coherence and theta phase synchrony measures demonstrated that novelty weakened the connectivity between these areas, which then gradually strengthened to the level observed in the familiar group. Conversely, mice exposed to the familiar arena showed steady and consistent behavior as well as theta dynamics in all areas. Treatment with a dopamine D1-receptor (D1R) antagonist in the vHPC disrupted neurophysiological adaptation to novelty specifically in the vHPC-mPFC and vHPC-VTA circuits, without affecting behavior. Thus, novelty induces distinct theta dynamics that are not readily dictated by behavior in the mPFC, vHPC, and VTA circuits, a process mediated by D1Rs in the vHPC. The observed time-dependent circuit dynamics in the key learning and memory circuit would provide new insights for treating neuropsychiatric disorders that often show impaired novelty processing.

Emotion regulation strategies explain associations of theta and Beta with positive affect.

Maladaptive emotion regulation (ER) strategies are a transdiagnostic construct in psychopathology. ER depends on cognitive control, so brain activity associated with cognitive control, such as frontal theta and beta, may be a factor in ER. This study investigated the association of theta and beta power with positive affect and whether emotion regulation strategies explain this association. One hundred and twenty-one undergraduate students (mean age = 20.74, SD = 5.29; 73% women) completed self-report questionnaires, including the Emotion Regulation Questionnaire and the Positive and Negative Affect Schedule. Spectral analysis was performed on resting state frontal electroencephalogram activity that was collected for eight 1-min periods of alternating open and closed eyes. Relative beta and theta band power were extracted relative to global field power at frontal channels. Regression analysis revealed that positive affect is significantly predicted by theta power (β = 0.24, p = .007) and beta power (β = -0.33, p < .0001). There was an indirect effect of beta power on positive affect via reappraisal, but not suppression. Additionally, theta power significantly predicted suppression, but no indirect effect was observed between theta power and positive affect. These findings are consistent with a prior study reporting a positive and negative relationship between theta and beta power, respectively, and positive affect induction. This study elucidates how modulation of theta and beta bands link to ER strategies.

Differences in the Lateralization of Theta and Alpha Power During n-Back Task Performance Between Older and Young Adults in the Context of the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) Model

Hemispheric Asymmetry Reduction in Old Adults (HAROLD) is one of the most well-known models of compensatory brain involvement in older adults. Most evidence supports its occurrence from the perspective of PET and fMRI studies, with a deficiency in electroencephalographic research in this domain. Therefore, we aimed to investigate the possibility of identifying the HAROLD pattern in older adults’ power of theta and alpha. The study sample comprised 50 older adults and 60 young adults performing n-back tasks while recording EEG signals. The level of cognitive performance and the theta and alpha power for pairs of symmetrical electrodes in the prefrontal, frontal, and parietal areas were analyzed. Older adults exhibited inferior cognitive performance compared to young adults and heightened theta power in the right hemisphere within the prefrontal and parietal areas. However, they also demonstrated increased alpha power in the right frontal pole, which contradicts the compensatory effects of theta power. Moreover, the two indicated phenomena of lateralization of theta and alpha power in older adults were unrelated to individual cognitive performance. The results make it challenging to discern whether the revealed age-related differences in theta and alpha power lateralization denote compensation, dedifferentiation, or nonselective recruitment as neutral features of brain activity in old adults.

Neural dynamics underlying coordination between training-induced habitual and goal-directed responses.

Understanding the neurocognitive mechanisms that govern the coordination of habitual and goal-directed behaviors is important, because impaired coordination will cause various behavioral disorders. However, inducing habitual responses in human beings through repetitive stimuli-response training in a laboratory setting is a challenge. Well-trained sports athletes, who have automatic perception-action associations toward expertise-related stimuli, provide a natural sample to address this critical knowledge gap. We recorded electroencephalograms (EEG) of well-trained sports athletes while they performed a Simon task with expertise-related stimuli. By manipulating the congruency between the location of expertise-related stimuli and the response hand, we dissociated automatic habitual response and goal-directed inhibition control. We observed a stronger behavioral congruency effect on expertise-related stimuli than neutral stimuli in sports athletes but not healthy controls. Furthermore, sports athletes exhibited larger response-locked lateralized readiness potentials and stronger frontocentral beta band (15-25Hz) activity in the congruent condition than the incongruent condition, which indicate an enhanced response tendency toward expertise-related stimuli. In contrast, prominent mid-frontal theta (3-7Hz) activity observed in the incongruent condition signaled the involvement of response inhibition. Additionally, lateralized readiness potentials amplitude and theta power showed significant correlation with performance efficiency. Taken together, these results suggest that sports athletes exhibit an enhanced coordination for expertise-related stimuli, involving automatic response preparation and proficient response inhibition through extensive training.

Neural response to errors among mothers with a history of recurrent depression and their adolescent daughters.

Depression is transmitted within families, but the mechanisms involved in such transmission are not clearly defined. A potential marker of familial risk is the neural response to errors, which may play a role in depression symptoms and is known to be partially heritable. Here, 97 mother-daughter dyads completed a Flanker task while electroencephalography markers of error monitoring were recorded: the error-related negativity (ERN) and response-locked delta and theta power. We assessed whether these measures of neural response to errors 1) were associated with history of recurrent major depressive disorder (MDD) and current depression symptoms among mothers, 2) were correlated among mother-daughter dyads, and 3) were associated with maternal history of recurrent MDD and maternal symptoms of depression among daughters. A history of recurrent MDD was associated with blunted delta and increased theta among mothers. Across mothers, delta and theta were negatively and positively associated, respectively, with current depression symptoms. Mothers' and daughters' ERN were positively correlated. Finally, current maternal depression symptoms were negatively associated with delta power in daughters. These results suggest that neural responses to errors may be implicated in the intergenerational transmission of depression. These results also support the relevance of delta oscillations to understanding pathways to depression.

Theta power reduction and theta–gamma coupling desynchronization are associated with working memory interference and anxiety symptoms in panic disorder: a retrospective study

BackgroundTheta-gamma coupling (TGC) describes the modulation of gamma oscillations by the theta phasic activity, which is crucial for processes such as the ordering of information during working memory (WM) performance. The mental arithmetic (MA), which involves performing calculations with numbers, is a crucial tool for evaluating and understanding the sensory processing and management abilities of WM. Evaluating TGC may provide greater insight into the neural mechanisms mediating WM deficits in panic disorder (PD).MethodsMedical and electroencephalography (EEG) records of psychiatric outpatient clinic between 1 March 2020 and 30 September 2023 were retrospectively reviewed. A total of 34 PD patients and 34 age- and sex-matched healthy controls (HCs) underwent EEG to assess the overall functional interaction of the brain using multi-channel EEG analysis, focusing on specific brain regions including the frontal, temporal, parietal, and occipital lobes. EEG recordings were conducted during two sessions: a 5-min eyes-closed resting-state (RS) and a subsequent 5-min eyes-closed MA. The TGC and the spectral power of the theta and gamma frequency bands, which are well known to be associated with WM, were analysed.ResultsCompared to those in HCs, TGC and theta power were significantly attenuated in PD patients. When analysing both HCs and PD patients together, RS TGC and relative theta power were negatively correlated with state anxiety and perceived stress scores, respectively. In contrast, TGC and relative theta power during the MA condition were positively correlated with the MA performance. Specifically, in PD patients, RS theta power across all electrodes was significantly negatively correlated with the Hamilton Anxiety Scale (HAMA) score. Linear regression analysis revealed that theta power in the T5 channel remained negatively correlated with pathological anxiety as measured by the HAMA score, even after controlling for other confounding factors.ConclusionsThis study highlights significant alterations in TGC and theta power in PD patients. PD patients exhibit reduced TGC and theta power compared to HCs, indicating deficits in the neural mechanisms underlying anxiety and/or WM in PD. These insights contribute to a better understanding of the neural basis of WM deficits in PD and suggest potential avenues for targeted therapeutic interventions.

Theta and gamma modulation in the nucleus accumbens as drivers of neurophysiological responses to acute methamphetamine sensitization in mice.

Methamphetamine (METH) has well-documented long-term effects on the brain, including increased psychomotor activity and behavioral sensitization. However, its immediate effects on the brain's reward system following acute exposure, which may contribute to the development of addiction, are less understood. This study aimed to investigate the effects of acute METH on brain oscillations in the nucleus accumbens of C57BL/6 mice. Mice in the METH group received 5mg/kg of METH for 5 days during the conditioning phase, followed by an 8-day abstinence period. Afterward, they underwent a 6-minute tail suspension test and were given a 1mg/kg METH challenge. Local field potential (LFP) data were analyzed for percent total power, mean frequency indices, and phase-amplitude coupling (PAC) to assess the neural effects of METH exposure across these phases. A reduction in theta power was observed across the conditioning, abstinence, and challenge phases of METH exposure. The subsequent METH challenge enhanced gamma oscillations, and PAC analysis revealed a consistent theta-gamma coupling index during both the METH administration and challenge phases. It highlights the sensitivity of the reward system to intense, short-term drug exposure, providing new insights into how acute neural stimulation may contribute to the development of addictive behaviors, reinforcing the brain's vulnerability to drug-induced changes in neural circuitry.

Interest paradigm for early identification of autism spectrum disorder: an analysis from electroencephalography combined with eye tracking.

Early identification of Autism Spectrum Disorder (ASD) is critical for effective intervention. Restricted interests (RIs), a subset of repetitive behaviors, are a prominent but underutilized domain for early ASD diagnosis. This study aimed to identify objective biomarkers for ASD by integrating electroencephalography (EEG) and eye-tracking (ET) to analyze toddlers' visual attention and cortical responses to RI versus neutral interest (NI) objects. The study involved 59 toddlers aged 2-4 years, including 32 with ASD and 27 non-ASD controls. Participants underwent a 24-object passive viewing paradigm, featuring RI (e.g., transportation items) and NI objects (e.g., balloons). ET metrics (fixation time and pupil size) and EEG time-frequency (TF) power in theta (4-8 Hz) and alpha (8-13 Hz) bands were analyzed. Statistical methods included logistic regression models to assess the predictive potential of combined EEG and ET biomarkers. Toddlers with ASD exhibited significantly increased fixation times and pupil sizes for RI objects compared to NI objects, alongside distinct EEG patterns with elevated theta and reduced alpha power in occipital regions during RI stimuli. The multimodal logistic regression model, incorporating EEG and ET metrics, achieved an area under the curve (AUC) of 0.75, demonstrating robust predictive capability for ASD. This novel integration of ET and EEG metrics highlights the potential of RIs as diagnostic markers for ASD. The observed neural and attentional distinctions underscore the utility of multimodal biomarkers for early diagnosis and personalized intervention strategies. Future work should validate findings across broader age ranges and diverse populations.

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study

Increase in EEG Alpha-to-theta Ratio After transcranial Direct Current Stimulation (tDCS) in Patients with Disorders of Consciousness: A Pilot Study

Background Transcranial direct current stimulation (tDCS) has emerged as a potentially effective intervention for improving consciousness levels in patients with disorders of consciousness (DoC). Most studies demonstrating benefits have targeted the left dorsolateral prefrontal cortex (DLPFC). However, inconsistent results have been reported across studies, and the brain effects of the stimulation remain unclear. Objective This work aimed to investigate the effects of a tDCS treatment on brain reactivity at rest in patients with DoC. Methods A 10-session tDCS treatment was administered over the left DLPFC in a group of patients with DoC. The effect of this stimulation was tested by combining the conventional behavioral assessment, conducted with the Coma Recovery Scale-Revised (CRS-R), with a quantitative analysis of the resting-state electrical brain activity, measured by electroencephalography (EEG). Results Following treatment, there was a slight improvement in CRS-R scores. More importantly, an increase in the ratio between alpha and theta power over posterior scalp regions was observed, even in patients who did not show changes in CRS-R scores. Conclusions These findings suggest that adding a simple quantitative EEG analysis alongside conventional clinical assessment post-tDCS could enhance the detection of changes in brain reactivity.

Slow oscillation-spindle coupling predicts sequence-based language learning.

Sentence comprehension involves the rapid decoding of both semantic and grammatical information, a process fundamental to communication. As with other complex cognitive processes, language comprehension relies, in part, on long-term memory. However, the electrophysiological mechanisms underpinning the initial encoding and generalisation of higher-order linguistic knowledge remain elusive, particularly from a sleep-based consolidation perspective. One candidate mechanism that may support the consolidation of higher-order language is the temporal coordination of slow oscillations (SO) and sleep spindles during non-rapid eye movement sleep (NREM). To examine this hypothesis, we analysed electroencephalographic (EEG) data recorded from 35 participants (Mage = 25.4, SD = 7.10; 16 males) during an artificial language learning task, contrasting performance between individuals who were given an 8hr nocturnal sleep period or an equivalent period of wake. We found that sleep relative to wake was associated with superior performance for rules that followed a sequence-based word order. Post-sleep sequence-based word order processing was further associated with less task-related theta desynchronisation, an electrophysiological signature of successful memory consolidation, as well as cognitive control and working memory. Frontal NREM SO-spindle coupling was also positively associated with behavioural sensitivity to sequence-based word order rules, as well as with task-related theta power. As such, theta activity during retrieval of previously learned information correlates with SO-spindle coupling, thus linking neural activity in the sleeping and waking brain. Taken together, this study presents converging behavioral and neurophysiological evidence for a role of NREM SO-spindle coupling and task-related theta activity as signatures of successful memory consolidation and retrieval in the context of higher-order language learning.Significance statement The endogenous temporal coordination of neural oscillations supports information processing during both wake and sleep states. Here we demonstrate that slow oscillation-spindle coupling during non-rapid eye movement sleep predicts the consolidation of complex grammatical rules and modulates task-related oscillatory dynamics previously implicated in sentence processing. We show that increases in theta power predict enhanced sensitivity to grammatical violations after a period of sleep and strong slow oscillation-spindle coupling modulates subsequent task-related theta activity to influence behaviour. Our findings reveal a complex interaction between both wake- and sleep-related oscillatory dynamics during the early stages of language learning beyond the single word level.

Neural processing of rhythmic speech by children with developmental language disorder (DLD): An EEG study

Abstract Sensitivity to rhythmic and prosodic cues in speech has been described as a precursor of language acquisition. Consequently, atypical rhythmic processing during infancy and early childhood has been considered a risk factor for developmental language disorders. Despite many behavioural studies, the neural processing of rhythmic speech has not yet been explored in children with developmental language disorder (DLD). Here we utilise EEG to investigate the neural processing of rhythmic speech by 9-year-old children with and without DLD. In the current study, we investigate phase entrainment, angular velocity, power, event related potentials (ERPs), phase-amplitude coupling (PAC) and phase-phase coupling (PPC), at three frequency bands selected on the basis of the prior literature, delta, theta and low gamma. We predicted a different phase of entrainment in the delta band in children with DLD, and also greater theta power, atypical cross-frequency coupling and possibly atypical gamma-band responses. Contrary to prediction, children with DLD demonstrated significant and equivalent phase entrainment in the delta and theta bands to control children. However, only the control children showed significant phase entrainment in the low gamma band. The children with DLD also exhibited significantly more theta and low gamma power compared to the control children, and there was a significant gamma-band difference in angular velocity between the two groups. Finally, group resultant phase analyses showed that low-frequency phase (delta and theta) affected gamma oscillations differently by group. These EEG data show important differences between children with and without DLD in the neural mechanisms underpinning the processing of rhythmic speech. The findings are discussed in terms of auditory theories of DLD, particularly Temporal Sampling theory.

The brain under pressure: Exploring neurophysiological responses to cognitive stress

Stress is an increasingly dominating part of our daily lives and higher performance requirements at work or to ourselves influence the physiological reaction of our body. Elevated stress levels can be reliably identified through electroencephalogram (EEG) and heart rate (HR) measurements. In this study, we examined how an arithmetic stress-inducing task impacted EEG and HR, establishing meaningful correlations between behavioral data and physiological recordings. Thirty-one healthy participants (15 females, 16 males, aged 20 to 37) willingly participated. Under time pressure, participants completed arithmetic calculations and filled out stress questionnaires before and after the task. Linear mixed effects (LME) allowed us to generate topographical association maps showing significant relations between EEG features (delta, theta, alpha, beta, and gamma power) and factors such as task difficulty, error rate, response time, stress scores, and HR. With task difficulty, we observed left centroparietal and parieto-occipital theta power decreases, and alpha power increases. Furthermore, frontal alpha, delta and theta activity increased with error rate and relative response time, while parieto-temporo-occipital alpha power decreased. Practice effects on EEG power included increases in temporal, parietal, and parieto-occipital theta and alpha activity. HR was positively associated with frontal delta, theta and alpha power whereas frontal gamma power decreases. Significant alpha laterality scores were observed for all factors except task difficulty and relative response time, showing overall increases in left parietal regions. Significant frontal alpha asymmetries emerged with increases in error rate, sex, run number, and HR and occipital alpha asymmetries were also found with run number and HR. Additionally we explored practice effects and noted sex-related differences in EEG features, HR, and questionnaire scores. Overall, our study enhances the understanding of EEG/ECG-based mental stress detection, crucial for early interventions, personalized treatment and objective stress assessment towards the development of a neuroadaptive system.

EEG and brain network analysis in the early diagnosis of Alzheimer's disease

Abstract. The early diagnosis of Alzheimers Disease (AD) is critical for effective intervention and improved patient outcomes. This paper explores the role of electroencephalography (EEG) and brain network analysis in the early detection of AD, with a particular focus on neurofeedback training as a therapeutic intervention. We review the characteristic changes in EEG signals, such as decreased alpha power and increased theta power, and their implications for identifying early cognitive decline. Functional connectivity analysis using coherence and phase synchronization methods reveals disruptions in key brain networks, providing valuable biomarkers for early AD. We also discuss quantitative EEG (qEEG) techniques, including power spectral density and fractal dimension, which enhance diagnostic accuracy. Additionally, mathematical models like the Kuramoto model offer insights into network dynamics and predict disease progression. Integrating EEG with other diagnostic tools, such as MRI and PET, creates a comprehensive multimodal approach. This integrative framework enhances early diagnosis and informs personalized treatment strategies, representing a significant advancement in the field of AD research and clinical practice.

Electrocortical Profiles in Relation to Childhood Adversity and Depression Severity: A Preliminary Report.

Objective: Assessment of electroencephalographic (EEG) activity in depression has provided insights into neural profiles of the illness. However, there is limited understanding on how symptom severity and risk factors, such as childhood adversity, influence EEG features. Methods: Eyes-closed EEG was acquired in N = 28 depressed individuals being treated in a tertiary psychiatric setting. Absolute alpha, beta, theta, and delta power and inter-/intra-hemispheric coherence were examined. Relations between the Montgomery-Åsberg Depression Scale (MADRS) and Adverse Childhood Experience (ACE) Questionnaire and EEG features were assessed. Results: Individuals in the high (MADRS≥30) versus lower (MADRS ≤ 29) symptom severity group exhibited greater overall beta power, and lower Fp1-Fp2 delta and theta coherence. Those with high (≥3) versus lower (≤2) ACE scores exhibited greater T7-T8 beta coherence. Lowest F3-F4 beta coherence was observed in those with high ACE/high depression severity. A negative correlation existed between F8-P8 alpha coherence and symptom severity. Conclusions: Those with higher depression severity exhibit increased beta power, possibly reflecting a hyper-vigilant state. Depression severity and ACE history may produce subtle alterations in frontal delta/theta and temporal/frontal beta coherence regions. Significance: This is the first study to examine the neural impact of depression severity and ACE-assessed childhood trauma in depressed individuals receiving treatment in a tertiary setting, accounting for the clinical reality of the prevalence of their co-occurrence.

Comparison of dry and wet electroencephalography for the assessment of cognitive evoked potentials and sensor-level connectivity.

Multipin dry electrodes (dry EEG) provide faster and more convenient application than wet EEG, enabling extensive data collection. This study aims to compare task-related time-frequency representations and resting-state connectivity between wet and dry EEG methods to establish a foundation for using dry EEG in investigations of brain activity in neuropsychiatric disorders. In this counterbalanced cross-over study, we acquired wet and dry EEG in 33 healthy participants [n = 22 females, mean age (SD) = 24.3 (± 3.4) years] during resting-state and an auditory oddball paradigm. We computed mismatch negativity (MMN), theta power in task EEG, and connectivity measures from resting-state EEG using phase lag index (PLI) and minimum spanning tree (MST). Agreement between wet and dry EEG was assessed using Bland-Altman bias. MMN was detectable with both systems in time and frequency domains, but dry EEG underestimated MMN mean amplitude, peak latency, and theta power compared to wet EEG. Resting-state connectivity was reliably estimated with dry EEG using MST diameter in all except the very low frequencies (0.5-4 Hz). PLI showed larger differences between wet and dry EEG in all frequencies except theta. Dry EEG reliably detected MMN and resting-state connectivity despite a lower signal-to-noise ratio. This study provides the methodological basis for using dry EEG in studies investigating the neural processes underlying psychiatric and neurological conditions.

HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network

Diabetes mellitus, also known as diabetes, is a medical condition marked by high blood sugar levels and impacts a large population worldwide. Treating diabetes is not feasible. It can be managed. Hence, it is crucial to promptly identify a diagnosis of diabetes. This study explores the effects of diabetes on the heart, focusing on heart rate variability (HRV) signals, which can offer valuable information about the existence and seriousness of diabetes through the evaluation of diabetes-related heart problems. Extracting crucial data from the irregular and nonlinear HRV signal can be quite challenging. Studying cardiac diagnostics involves a thorough analysis of electrocardiogram (ECG) signals. Traditional electrocardiogram recordings utilize twelve channels, each capturing a complex combination of activities originating from different regions of the heart. Examining ECG signals recorded on the body’s surface may not be an effective method for studying and diagnosing diabetic issues. The study introduces a research proposal utilizing a high-density resolution electrocardiogram (ECG) system with a minimum of 64 channels and multi-view convolutional neural network classification (HD-MVCNN) to address the mentioned challenges. This framework may help identify the hypoglycaemia effects on brain regions, leading to decreased complexity and increased theta and delta power during scalp electrocardiogram procedures. The convolutional architectural model primarily contributes to enhancement and optimization through its Stochastic Gradient Descent (SGD) along with convolutional layers and according to results, the HD-MVCNN demonstrated better stability and accuracy in comparison to traditional classification models. Thus, HD-MVCNN shows promise as a powerful method for classifying features in diabetes clinical data.