The persistent sensitivity to sensory and painful stimuli of people with migraine has been associated with disruption of the salience network (SN). Although migraine is characterized by severe head pain attacks, brain changes have been observed in people with migraine in between attacks (i.e., during the interictal phase). We hypothesized that SN dynamics may be altered in people with migraine during spontaneous attacks as well as an attack imagery task engaging sensory, emotional, and cognitive aspects of pain. To test this, we analysed the SN dynamics in patients with episodic menstrually related migraine without aura across three conditions: at rest during a spontaneous attack; during the interictal phase while performing an imagery task recalling a previous attack and at interictal rest. Healthy controls were studied in matching conditions, including rest and a non-migraine head-pain imagery. We collected functional magnetic resonance imaging (fMRI) data, using a dynamic functional connectivity (dFC) analysis to examine SN temporal features from a total of 26 participants. A significant between-group difference was observed during the pain imagery task, with people with migraine exhibiting a shorter SN dwell time than controls. Such differences were not detected during spontaneous attacks or interictal rest. The between-group difference in SN dynamics observed during the pain imagery condition suggests that this paradigm may be useful for engaging the cognitive and emotional dimensions of pain. However, further work is needed to determine whether this difference is migraine-specific or rather reflects differences in the salience and affective content of the recalled experiences.
Transcranial interference stimulation (TIS) is a non-invasive neuromodulation technique designed to modulate deep brain structures. Although most TIS simulation studies assume isotropic tissue conductivity, white-matter (WM) conductivity is direction dependent. This study investigated how diffusion-tensor-imaging-derived, volume-normalized WM conductivity anisotropy affects the magnitude and spatial distribution of the maximum low-frequency envelope amplitude, here termed maximum modulation depth (MDmax), in individualized TIS models. Six individualized head models were constructed from structural and diffusion MRI data. Isotropic and anisotropic conductivity models were compared under two manually selected proof-of-concept TIS montages designed to produce contrasting modulation-depth directions relative to the dominant fibre orientation of the corpus callosum. For each voxel, MDmax and the corresponding unit modulation-depth direction vector were calculated. Directionality was evaluated in the corpus callosum by comparing this vector with the principal diffusion eigenvector, v₁. To assess whether these effects extended beyond the corpus callosum, additional target-specific analyses were performed in the left M1 and left hippocampus using montages selected in the isotropic model and held fixed between conductivity models. Anisotropic conductivity altered both the magnitude and spatial distribution of the simulated TIS envelope. Whole-brain MDmax was higher in the anisotropic than in the isotropic model under both montages, whereas threshold-defined high-MD volume showed montage-dependent differences. In the corpus callosum body, regional peak modulation depth was higher when the modulation-depth direction vector was more collinear with v₁, but lower when the two directions were approximately orthogonal. Under the target-specific montages, regional peak modulation depth increased modestly in the left primary motor cortex but decreased in the left hippocampus. These findings indicate that anisotropy-related changes are direction- and region-dependent and vary across target-specific montage configurations. Local fibre orientation should therefore be considered when interpreting individualized TIS field models.
Autism Spectrum Disorder (ASD) is a neurological and developmental condition that affects children’s social and cognitive skills, leading to repetitive behaviors, challenges in social interaction, communication difficulties, and restricted interests. Early diagnosis of autism can help mitigate its severity and long-term effects. This study proposes an automated electroencephalography (EEG)-based ASD detection method using data from Iraq and Poland. EEG signals underwent preprocessing, which included noise removal using a band-pass filter and artifact subspace reconstruction to ensure clean signals. Following preprocessing, features were extracted from the EEG channel power spectral density (PSD), and topographic brain maps (TBMs) were generated as inputs for deep feature extraction models, including AlexNet and GoogLeNet. Analysis of variance (ANOVA) was employed for feature selection (FS). Two types of linear classifiers, namely linear Support Vector Machine (SVM-L) and Linear Discriminant Analysis (LDA), were used for classification. The alpha band yielded the highest accuracy, reaching 98
Infancy poses a spatial-coordinate challenge for developmental EEG: cortical geometry, head geometry, and EEG forward propagation change rapidly, so a cortical coordinate system that is meaningful at one age may not be directly comparable at another. We addressed this question in a template-based computational analysis using age-specific infant anatomical templates distributed through MNE-Python. Specifically, we computed cortical Laplace–Beltrami (LB) eigenmodes (cortical harmonics), EEG forward models, and forward-projected eigenmode dictionaries. We asked which eigenmode orders are preferentially expressed at the scalp and whether independently computed age-specific eigenmode coordinates remain stable for cross-age comparison. Forward-projected scalp gain was concentrated in lower eigenmode orders across infancy, indicating a stable coarse-to-fine transfer profile. However, neighboring-age LB bases were only locally comparable by nominal mode index. Same-index modes showed local reordering and mode-index drift, and the same cortical pattern produced coefficient leakage into nearby modes when re-expressed in a neighboring-age basis. These template-level coordinate differences affected simulated downstream analyses: neighboring-age dictionaries were not fully substitutable in low-dimensional sensor space, and a fixed adult-derived basis was suboptimal for recovering same-index coordinates, with larger penalties in early- to mid-infancy. Sequential Procrustes tracking improved same-index consistency, supporting local alignment as a practical step toward age-aware coordinates. Because all quantitative summaries are derived from population-average templates and simulations, they should be interpreted as mechanistic evidence about coordinate-system effects rather than direct estimates of empirical EEG error. These results motivate age-parameterized or explicitly tracked cortical harmonic coordinates for longitudinal developmental EEG and related lifespan analyses.
Flow, as defined by Mihalyi Csikszentmihalyi (1975), is a holistic experience in which individuals are fully immersed in an activity, resulting in a diminished sense of self and an altered perception of time. To investigate the global neural dynamics underlying flow, we employed EEG microstate analysis, which capture the spatial and temporal properties of dominant transient global brain states (Lehmann et al. 1998). In a study involving 43 participants playing the video game Thumper for 25 min, we extracted three four-minute EEG segments from each session corresponding to reported experiences of flow, boredom, and frustration, as determined by self-reports and performance metrics. Across conditions, six distinct microstate topographies (A–F) accounted for most of the global variance. Given that reduced self-referential processing is a key feature of flow, we hypothesized that flow would modulate the properties of microstates C and E, which have been associated with brain regions resembling the default mode network (DMN). Compared to boredom and frustration, the flow condition showed significantly decreased global explained variance, mean duration, time coverage, and occurrence frequency of microstate E, as well as reduced mean duration and time coverage of microstate C. These findings suggest that microstates associated with self-referential processing are shorter and less frequent during flow than during boredom and frustration. This supports the notion that the flow experience modulates global brain dynamics, particularly within the DMN. Furthermore, our results align with previous research reporting reduced DMN activity during meditative and psychedelic states, reinforcing the idea of diminished self-awareness in such conditions.
The amygdala is a central hub for emotional processing, whose interactions with large-scale brain networks have been implicated in the pathophysiology of depression. Investigating how amygdala-centered connectivity relates to depression in a community-based sample, rather than focusing exclusively on patients with major depressive disorder, may provide broader insights into depression vulnerability. To this end, functional connectivity (via cross-correlation) and effective connectivity (via Granger causality) were computed using resting-state functional magnetic resonance imaging (fMRI) data from 161 adults obtained from the Nathan Kline Institute (NKI)/Rockland community sample. Subsequently, a behavior-guided windowed association analysis identified connectivity indicators correlated with depression severity (Beck Depression Inventory scores), whose contributions to depression prediction were subsequently ranked using machine learning based on a support vector machine. Specifically, higher Beck Depression Inventory (BDI) scores were associated with increased amygdala functional connectivity with the salience/ventral attention system and the dmPFC component of the default mode network, together with decreased connectivity involving dorsal attention, sensorimotor, and visual regions. Granger causality analyses further revealed predominantly reduced directional influences both to and from the amygdala, particularly between the amygdala and distributed default mode, salience/ventral attention, visual, and subcortical regions, suggesting weakened large-scale information exchange. Machine learning analyses identified amygdala-centered connectivity features, especially those involving the default mode and salience-related systems, as among the most informative predictors of depression severity. Overall, these findings delineate an amygdala-centered multi-system network pathology underlying depressive vulnerability, providing quantifiable neural signatures for early detection and potential targets for intervention.
Understanding how the brains structural architecture supports task-evoked functional activity is a fundamental goal of neuroscience. However, most predictive models rely on single-scale structural features, overlooking the brains intrinsically hierarchical organization. Here, we introduce the Collaborative Graph Attention Multi-Task Network (CoGA-MTN), a deep learning framework that integrates multi-scale structural features to jointly predict task-based functional connectivity (FC) and identify potential disease-related network alterations. CoGA-MTN employs a dual-branch graph attention network to extract complementary global statistical and local topological features from structural MRI, and a cross-modal task-coordinated learning mechanism that enables task-conditioned FC prediction alongside multi-disease classification. Validated on the Consortium for Neuropsychiatric Phenomics dataset (152 participants, three tasks, four diagnostic groups), CoGA-MTN outperforms single-scale baselines in task-conditioned FC prediction (PCC = 0.657 ± 0.02) and achieves macro F1 scores of 0.68–0.75 across three psychiatric disorders. Crucially, the model reconstructs a stable whole-brain connectivity architecture that is conserved across tasks, while simultaneously revealing diagnosis-related discrepancies that are consistent with established pathophysiological models. By modeling psychiatric conditions within a normative structure–function framework, this work provides a unified approach for characterizing the relationships between multi-scale brain structure, dynamic function, and psychopathology.
Visual mental imagery is the process of reconstructing perceptual experience without sensory input. How the brain performs this process is poorly understood, particularly from the perspective of conventional linear EEG analysis. This study aims to evaluate if the two non-linear EEG complexity measures—Lempel-Ziv Complexity (LZC) and Higuchi Fractal Dimension (HFD)—can differentiate between perception and imagination and if they can be used as objective indices of neural separability of mental imagery. LZC and HFD were extracted from 62 scalp EEG channels in 46 healthy adults performing the PerceiveImagine paradigm (Li and Fan 2024), after wideband Picard ICA decomposition (1–200 Hz), which was used to make residual artefacts explicit rather than to remove components, with edge-channel EMG monitoring for artefact control. Statistical analyses included cluster-based permutation testing (Maris and Oostenveld 2007), Hotelling T², and leave-one-subject-out cross-validation (LOSO-CV). Broadband LZC topography differed between perception and imagination (cluster p = 0.005; Hotelling F = 3.08, p = 0.002, V = 0.28). LOSO-CV classification reached AUC = 0.811 (95
The relationship between brain structure and functional measures, such as resting motor threshold (rMT), is of interest and has been studied in adults, but there is a lack of research in adolescents. We studied the correlation between rMT and the grey matter thickness as well as white matter parameters of the motor cortex, using magnetic resonance imaging and navigated transcranial magnetic stimulation. Forty-five participants (twenty boys and twenty-five girls) aged sixteen to nineteen years were recruited for the study. We determined rMT for the abductor pollicis brevis muscle and investigated the correlation between rMT and grey matter thickness and white matter parameters, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) in the hand knob and in the extended hand knob. Grey matter thickness in the primary motor cortex was associated with rMT. We also discovered that white matter parameters RD and MD in a broader area of the primary motor cortex were positively associated with rMT. Both grey and white matter appear to influence cortical excitability in adolescence. Understanding the normal development of the brain is necessary to understand pathological neurodevelopmental processes.
Music can evoke profound emotions and autobiographical memories. The Bonny Method of Guided Imagery and Music (BMGIM) is a music therapy approach that uses music-evoked imagery to elicit emotions and promote self-exploration. To investigate the neural mechanisms of BMGIM, 22 healthy university students were recruited, of whom 21 were included in the final analysis. Brain activity was measured using functional near-infrared spectroscopy (fNIRS) during a Music Only session and a BMGIM session based on a standardized BMGIM music program. Mood changes were assessed using the Profile of Mood States 2. NIRS data were analyzed using a region-of-interest (ROI)-based approach. Reduced negative mood scores were observed in both sessions, with depression–dejection showing a significant reduction only following BMGIM but not after listening to music alone. In the third piece of music, which was structurally associated with the deepest phase of the BMGIM experience, increased oxygenated hemoglobin responses were observed during BMGIM compared to listening to music alone. After statistical correction, significant condition-dependent effects were observed only in the orbitofrontal cortex, with trends in other regions directionally consistent with the channel-based analyses. The present findings suggest that BMGIM engages the orbitofrontal cortex during emotional processing and specifically reduces depression–dejection. These findings highlight its potential as a clinically meaningful intervention for individuals with depression. Future studies should employ more rigorous experimental designs with larger and more diverse samples to elucidate the mechanisms underlying BMGIM.
Changes in the temporal parameters of EEG microstates are observed in various psychiatric illnesses, including psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk for psychosis. It is unclear whether dopamine abnormalities, distinctive of psychotic disorders, contribute to microstate alterations. Fifty-eight healthy participants (M: F = 32:26) took part in a single-dose drug challenge study. Using a randomized, double-blind, cross-over, and placebo-controlled design, participants received the dopamine precursor L-DOPA (100 mg), the dopamine antagonist haloperidol (2 mg), and a placebo. Resting-state (10 min, eyes-closed) 64-channel EEG was recorded at peak drug effects. We calculated temporal parameters (coverage, duration, and occurrence) of 5 microstate classes (A-E) and compared them between drug conditions using linear mixed-effect models. Significant group x class interactions were observed for all parameters. Compared to placebo, parameters of microstate B were decreased under L-DOPA (duration and occurrence) and haloperidol (coverage, duration, and occurrence). Coverage and occurrence of microstate C were reduced under L-DOPA compared to haloperidol and placebo. Parameters of microstate E were reduced under L-DOPA compared to haloperidol (coverage and occurrence) and placebo (coverage, duration, and occurrence). Both haloperidol and L-DOPA increased all parameters of microstate D, compared to placebo. Our results show that resting-state EEG microstates are modulated by dopaminergic drugs. However, extrapolations from healthy controls to patients with psychotic disorders are complicated by various factors. Further studies should investigate the (long-term) effects of haloperidol and other antipsychotics on EEG microstates in patients with psychotic disorders.
The thalamus is a critical subcortical hub that relays sensorimotor information and regulates higher-order cognitive processes. Accurate delineation of thalamic nuclei is essential for elucidating disease mechanisms and tracking clinical progression. In this study, we compared two segmentation approaches implemented in FreeSurfer: the conventional structural method and a joint framework that integrates diffusion tensor imaging. Magnetic resonance imaging (MRI) data from 24 healthy controls (HC), 27 patients with cognitively normal Parkinson’s disease (PD-CN), and 33 Parkinson's disease patients with mild cognitive impairment (PD-MCI) were analyzed. Segmentation methods were compared in HC to assess their effect on volume estimates. Group comparisons were then conducted separately for each method to evaluate sensitivity in detecting disease-related volumetric differences. Finally, nuclei with significant group effects in joint segmentation were tested for associations with Addenbrooke’s Cognitive Examination-Revised (ACE-R) scores. Joint segmentation yielded systematically lower thalamic volume estimates than the structural method, with significant differences across hemispheres and nuclei in HC. Group-wise analyses revealed that joint segmentation, but not structural segmentation, detected significant atrophy in the right thalamus of PD-MCI patients. At the nuclei group level, joint segmentation showed greater sensitivity, identifying bilateral anterolateral and posterior nuclei as significantly reduced in PD-MCI relative to HC. Moreover, volumes of these nuclei correlated positively with ACE-R scores. These results highlight that methodological choices critically shape the detection of thalamic pathology in PD, suggesting that incorporating diffusion MRI into segmentation may improve sensitivity to cognitively relevant subnuclear changes and support early diagnosis and disease monitoring.
Deep brain stimulation (DBS) systems with electrophysiological recording capabilities offer a unique opportunity to chronically record local field potentials (LFP) from implanted electrodes, enabling real-time monitoring of subcortical neural dynamics and the development of adaptive neuromodulation strategies. However, translating raw LFP into clinically relevant biomarkers remains challenging due to numerous technical considerations. To address this, we share both a practical guide for conducting LFP experiments and BrainWave-DBS, an open-source pipeline that implements the workflow and supports analysis of patient-derived LFP data. The pipeline covers data import, preprocessing, spectral analysis, and visualization, offering a reproducible framework for LFP analysis. Furthermore, we demonstrate the functionality of the pipeline using a representative Parkinson's disease dataset and highlight key technical insights gained through this work. By improving access to neural signal acquisition and analysis, our goal is to foster broader adoption of this technology, which could advance closed-loop neuromodulation of deep brain structures.
Research suggests that ADHD is characterized by dominant Default-Mode Network (DMN) and diminished Task Positive Network (TPN) activity, which has been linked to detrimentally affecting attention and executive task-dependent functioning. Interventions targeting this DMN-TPN interplay may be clinically important in ameliorating ADHD symptoms. This hypothesis was explored using frequency and spatial domains of 33-channel EEG data as an index of the EEG-DMN. Specifically, tonic EEG power spectra measures and Low Resolution Brain Electromagnetic Tomography (LORETA) analysis during resting-state eyes-open and eyes-closed were examined in 54 ADHD patients versus 16 healthy-controls. Furthermore, the effects of a 12-week Mindfulness-Based Cognitive Therapy (MBCT) programme were observed in the same ADHD patient sample, with 30 patients randomly allocated to MBCT and 24 to wait-list control. Significantly reduced power across EEG spectra, and increased activation of the medial ventral prefrontal cortex (mvPFC) indexed via LORETA, was observed in ADHD patients compared to healthy controls. MBCT-related increase in β-power, and increased LORETA current density of the precuneus, was observed. These findings suggest the clinical utility of considering the basal brain state in ADHD within the wider perspective of cortical hypoactivity. MBCT-related elevated β-power viably connects to enhanced cortical alertness/activation and arousal of the DMN, conceivably aiding the more efficient regulation of the DMN-TPN interplay. Increased precuneus activation, a central region of the DMN, may reflect altered intrinsic baseline activity relevant to more adaptive regulation of DMN–TPN dynamics, associated with attentional control and executive processes. Further understanding of the EEG DMN-TPN dynamic within ADHD may present neural markers of inattention and executive functioning, further to elucidating a working mechanism of MBCT in its treatment.
Brain aging is characterized by complex alterations in both anatomical structure and neural function. While the interdependence between structural connectivity (SC) and functional connectivity (FC) is well-established, the patterns of structural-functional coupling (SFC) during aging remain largely unexplored, despite being crucial for elucidating the neural mechanisms of age-related changes. Moreover, traditional resting-state fMRI studies have predominantly focused on linear correlations, often overlooking nonlinear causal interactions that may play a pivotal role in the aging brain. To address this, we employed a Nonlinear Granger Causality (NGC) model to investigate SFC at the whole-brain level. The study included 227 healthy participants, stratified into a young group (20–35 years, N = 153 ) and an older group (59–77 years, N = 74 ), with further subgrouping by sex. We analyzed SFC from both static and dynamic perspectives at regional and subnetwork levels. Our results demonstrated that the young group exhibited significantly stronger NGC-based SFC compared to the sex-matched older group. Additionally, males displayed a higher proportion of strong SFC connections than age-matched females. Notably, a widespread age-related decline in nonlinear causal coupling was observed across both regional and subnetwork scales, particularly within networks governing cognitive control and attention. Furthermore, dynamic analyses across sliding windows confirmed the persistence of these aging patterns throughout the scanning duration, despite increased temporal variability observed in the elderly. This study underscores the importance of incorporating nonlinear causal relationships into brain network research, as this approach offers deeper insights into the potential mechanisms underlying age-related cognitive decline and neurodegenerative processes.
Depression is a prevalent mental health condition with a significant global burden, yet its diagnosis remains challenging due to the inherent limitations of conventional assessment tools. The electroencephalography (EEG) could be a potential diagnostic modality of depression. This study aims to evaluate the diagnostic test accuracy of EEG in depression diagnosis. A systematic search was performed in the major academic databases from inception to January 25, 2025. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist was used to assess the bias of all the included studies. Bivariate random-effect models and hierarchical summary receiver operating characteristic (HSROC) curve models were used to illustrate the diagnostic performance. The pooled sensitivity (SEN), specificity (SPE), and the Diagnostic Odds Ratio (DOR) were calculated to evaluate the diagnostic accuracy. Subgroup analyses were conducted to investigate the possible sources of heterogeneity. A total of 18 studies were included, contributing 58 reported results in total. The diagnostic test accuracy of EEG for depression was high across the studies, as the pooled SEN, SPE, and DOR were 0.939 (95
The Default Mode Network was a key finding for cognitive neuroscience, but being the result of a data-driven analysis of resting-state fMRI data, its psychological and clinical implications have been difficult to elucidate. This is because in the resting-state paradigm we cannot directly correlate an observable specific task with specific brain connectivity patterns, and therefore inferences about the relationship between particular cognitive domains and the resting-state networks are limited. A similar problem arises when trying to link the network with personality traits: the DMN, as other intrinsic networks, is not a simple metric to compare with the results of a psychological test, but a complex composite of spatio-temporal features. Although over the last two decades several research works have provided insights about these relationships, we still lack a consensus on the methodology that best captures these interactions. In this context, we propose an alternative method to model the psycho-physiological relationships of the resting state components with behavioral data, based on the dimensionality reduction of an extensive psychological evaluation and the spatial dimension of the intrinsic connectivity components. Our results show that the connectivity networks are low to moderately related with behavioral and personality traits, or at least this relation is not in a direct way. This integration of neuroimaging and psychological assessment data creates valuable pathways for cognitive neuroscience, potentially revealing with precision how intrinsic brain network organization relates to individual variations in cognitive functioning and personality dimensions.
This study aimed to examine the spatiotemporal properties of resting‑state global brain network activity and their associations with emotional and behavioral functioning in children. Resting‑state electroencephalography (EEG) was acquired in 83 children aged 6–7 years. Microstate features were extracted via K‑means clustering, and partial least squares (PLS) regression was used to characterize multivariate associations between microstate temporal metrics (global explained variance, duration, occurrence, and coverage) and parent‑rated scores on the Child Behavior Checklist (CBCL). Results demonstrated that four canonical microstate classes were already present in 6–7-year-old children, with microstate C showing dominance across temporal parameters. PLS revealed that the temporal parameters of microstate C were positively associated with total CBCL scores, whereas no significant relationships were observed for the other microstate classes. These findings suggest that neural dynamics indexed by microstate C may be associated with individual differences in emotional and behavioral regulation in 6–7-year-old children, highlighting its potential as an electrophysiological marker of neurobehavioral development.
This study evaluates the temporal occurrence of A-phases (TOAP) across superficial and deep electroencephalogram (EEG) recordings to investigate their self-similarity and regulation dynamics across different brain depths during sleep. Sleep recordings from 10 epileptic patients were analyzed to characterize the TOAP in scalp, neocortex (NC), and hippocampus (HPC) signal recordings. TOAP was represented as a binary series, with ‘1’ indicating the presence and ‘0’ the absence of an A-phase. Detrended fluctuation analysis (DFA) was applied to evaluate scale-free properties, and entropy metrics were used to quantify the occurrence probability of symbolic patterns in the binary signal. Mono and multiscale approaches captured TOAP dynamics across different time scales. DFA results revealed consistent correlation properties in TOAP across scalp and deep brain recordings. Monoscale analysis showed persistent long-range correlations, and multiscale analysis indicated a decrease in the scaling exponent from approximately 1.5 toward 0.5 at short scales, and the opposite trend at longer scales. Entropy analysis showed that the TOAP pattern distribution varies with brain region and scale, with higher diversity at the scalp and lower in the NC and HPC. Shannon entropy was positively correlated with cyclic alternating pattern rate. TOAP exhibits consistent scaling across brain regions, suggesting that A-phases follow a unified temporal structure throughout the brain, potentially reflecting a global mechanism of EEG modulation during sleep. In addition, entropy variations suggest that anatomical location and the analysis scale modulate A-phase occurrence. There is a potential relationship between symbolic entropy of TOAP patterns and sleep instability.
Although moral decision-making engages complex cognitive and emotional processes, little is known about how some individual differences relate to its neural underpinnings. This study investigated how individual differences in decision-making relate to neurophysiological activity during moral reasoning. Thirty adults completed a moral dilemma task while electrophysiological (EEG) activity was recorded and completed questionnaires assessing moral identity (MIQ), decision-making styles (GDMS), and maximization tendencies (MS). Correlational analysis between psychometric data and EEG bands was performed. Results revealed that spontaneous decision-making style was positively correlated with increased theta power in parieto-occipital regions, suggesting a possible greater reliance on intuitive, bottom-up processes during moral evaluation. Higher decision difficulty was associated with reduced alpha activity in temporo-central areas, which may indicate enhanced attentional engagement and increased cognitive effort, whereas alternative search correlated positively with frontal beta power, a neural marker possibly associated with deliberative top-down control processes. Finally, the two dimensions of moral identity exhibited opposite associations with posterior delta activity: moral integrity correlated positively with delta power, whereas moral self showed a negative correlation, suggesting potentially distinct neurocognitive pathways underlying value-based processing. Overall, these findings demonstrate that individual differences significantly shape the neural dynamics of moral reasoning, highlighting the interplay between intuitive, deliberative, and identity-driven processes in the evaluation of morally relevant situations.
