Search Results (682)

Background: The emergence of generative adversarial networks has laid the groundwork for data augmentation, addressing challenges of missing training data in various research scenarios. However, simulating functional magnetic resonance imaging (fMRI) data remains particularly challenging, especially for populations with varying degrees of mild cognitive impairment (MCI). Effectively characterizing and capturing the mechanisms of brain function variations poses a critical issue in cognitive neuroscience. This study aims to simulate and analyze synthetic fMRI blood-oxygen-level-dependent (BOLD) signals across four cognitive stages: healthy control (HC), early MCI (EMCI), late MCI (LMCI), and Alzheimer’s disease (AD). Methods: We propose WGTMM, an innovative method that integrates the Vision Transformer for fMRI (VTFF) into a generative adversarial network architecture. Crucially, WGTMM directly generates fMRI time-series data from pink noise rather than modeling in a latent space, thereby preserving rich temporal dynamics. The framework incorporates a Wasserstein GAN (WGAN) with feature matching to enhance generation quality and mitigate mode collapse. Results: demonstrate that WGTMM-generated fMRI data exhibit lower Kullback-Leibler (KL) divergence compared to traditional GAN and WGAN models, indicating a closer resemblance to real datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Furthermore, when applied to data augmentation, the synthetic data substantially improve multi-class classification performance. Conclusions: WGTMM not only enriches training datasets but also provides new insights into spatial biomarkers of cognitive decline. By leveraging VTFF to investigate class token attention patterns across 360 brain regions, this study reveals monotonic weight variations along disease stages in key cortical areas, including the rostral Area 6, the primary sensory cortex, and PFm near Wernicke’s area, offering a fine-grained exploration of disease progression. Full article

Spiking neural networks (SNNs) are well suited for modeling temporally evolving information due to their event-driven and dynamic neuronal mechanisms. Nevertheless, the majority of existing SNN topologies are constructed through algorithmic procedures rather than guided by constraints from biological brain connectivity, which weakens their biological plausibility. In our earlier work, we developed a spiking neural network (SNN) by incorporating topological information from functional brain networks extracted from functional magnetic resonance imaging (fMRI) data of healthy individuals, and named the resulting model fMRISNN. Nevertheless, the fMRI data used in previous work were resting-state fMRI. Compared with resting-state fMRI, task-state fMRI can capture brain-region coordination patterns induced by specific task stimuli, and the resulting functional brain network is therefore more closely related to the corresponding task. Motivated by this advantage, this study replaces the resting-state topology used in previous fMRISNN studies with a task-state, number/digit-related fMRI topology and validates the resulting Task-fMRISNN on handwritten digit recognition. The experimental results demonstrate that the proposed Task-fMRISNN outperforms the Rest-fMRISNN in terms of recognition accuracy, lesion robustness, and noise robustness. In addition, the Task-fMRISNN achieves significantly better performance than several baseline models constructed using algorithmically generated topologies. While deep convolutional neural networks (CNNs) may deliver superior absolute recognition performance, the proposed fMRISNN provides a more compact model structure and shows potential resource-efficiency advantages due to its sparse and event-driven computational characteristics. Full article

The classical localizationist framework in biology and neuroscience has provided a powerful approach for linking structure to function. However, increasing evidence indicates that biological functions emerge from distributed interactions across complex systems. While network and systems-based approaches have advanced this transition, they often remain focused on connectivity patterns or statistical dependencies. In this review, I argue that a further conceptual step is required: a coordination-based framework in which biological function emerges from the context-dependent selective stabilization of interactions among distributed components that become causally relevant for specific outcomes. I develop this perspective comparing brain network organization and plant signaling, two systems that exhibit adaptive behavior without relying on centralized control. Across both domains, function depends on the dynamic coordination of heterogeneous processes operating across multiple spatial and temporal scales. This framework acknowledges structural specialization but argues that specialized components become effective through coordinated interaction regimes. I further discuss how this perspective extends current systems biology approaches by prioritizing temporally structured interaction patterns as the primary explanatory target. Finally, I outline empirically testable predictions suggesting that biological function is better captured by time-resolved coordination dynamics, hub-mediated integration, and metastable interaction regimes than by localized activity or static connectivity. Full article

Sepsis-associated encephalopathy (SAE) is a frequent neurological complication of sepsis, driven by six interconnected pathophysiological components: (1) systemic inflammation-triggered neuroinflammatory cascades, initiated by systemic recognition of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) and propagated by pro-inflammatory mediators; (2) central nervous system (CNS) immune cell-mediated neuroinflammation, wherein microglia, regulatory T cells, and neutrophils dynamically regulate inflammatory progression; (3) blood–brain barrier (BBB) disruption, progressing from functional disturbance to structural damage via tight junction degradation and immune infiltration; (4) multimodal programmed cell death, encompassing autophagy, apoptosis, pyroptosis, and ferroptosis driven by mitochondrial dysfunction; (5) neurotransmitter network imbalance, manifesting as cholinergic deficiency and glutamate excitotoxicity; and (6) gut–brain axis dysregulation, characterized by reduced microbiota-derived metabolites such as butyrate and indolepropionic acid. These components are organized along a core pathological axis comprising four sequential stages: neuroinflammatory storm (encompassing components 1 and 2) → BBB disruption and microcirculatory disturbances (component 3) → multimodal programmed cell death (component 4) → neurotransmitter imbalance (component 5), with the gut–brain axis (component 6) functioning as a bidirectional regulatory node that intersects and modulates all four stages. Mitochondrial dysfunction serves as the central converging node linking these pathological axes. Targeted interventions against neuroinflammation, immune cell modulation, BBB restoration, inhibition of aberrant cell death, neurotransmitter homeostasis, and gut microbiota remodeling hold therapeutic promise. Elucidating the crosstalk among these pathways will accelerate the clinical translation of precision therapies for SAE. Full article

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to classify 19-channel EEG entropy features into three classes (dementia, stroke, healthy control; N = 45; 15 per class). The architecture suggests four new designs. First, the Channel Importance Attention (CIA) block, which adaptively learns to re-weight the importance of electrodes via squeeze-excitation. Second, the dual-branch encoder, which combines the global multi-head self-attention with the local depthwise-separable convolution. Third, the gated sigmoid fusion mechanism. Fourth, the bottleneck residual classification head, to solve overfitting. Eight entropy feature sets: Amplitude-Aware Permutation Entropy (AAPE), Attention Entropy (AttEn), Dispersion Entropy (DisEn), Distribution Entropy (DistrEn), Fluctuation-based Dispersion Entropy (FDispEn), Fuzzy Entropy (FuzEn), Linear Gaussian Estimation of the Conditional Entropy (LinEn), and Symbolic Dynamics (SyDy) were evaluated individually with stratified 5-fold cross-validation on within-fold SMOTE augmentation. Results: EEG-ChTABNet consistently outperformed the baseline Transformer on all 8 feature sets. DisEn and SyDy features yielded peak classification accuracy of 73.3% (AUC: 0.823 and 0.857, respectively) compared to the corresponding baseline of 57.8% and 55.6%. SyDy achieved the best overall AUC of 0.857 and the dementia detection sensitivity was up to 86.7% over multiple feature sets. Conclusions: EEG-ChTABNet shows the effectiveness of channel-adaptive, dual-branch Transformer Designs for EEG-based neurological classification from Small-Sample Entropy Feature Data, and Identifying SyDy and DisEn as the Most Discriminative Feature Representations for Three-Class Neurological EEG Classification. Full article

Neonatal seizures represent the most common neurological emergency in the neonatal period and arise within a uniquely immature and highly dynamic brain. Their recognition is challenging due to frequent electroclinical dissociation, with many seizures remaining purely electrographic and therefore detectable only through continuous electroencephalogram (cEEG) monitoring. This narrative review provides an integrated and updated overview of neonatal seizures, bridging developmental neurobiology, diagnostic challenges, etiological classification, and therapeutic strategies. The immature brain is characterized by an imbalance between excitation and inhibition, transient network architectures, and activity-dependent developmental processes, all of which contribute to the distinct electroclinical features of neonatal seizures. cEEG remains essential for accurate diagnosis and quantification of seizure burden, which may influence outcome. Etiology represents the primary determinant of prognosis, with hypoxic–ischemic encephalopathy (HIE), stroke, and genetic disorders among the most frequent causes. Advances in genetic testing have improved diagnostic precision and enabled targeted therapies in selected cases, supporting a precision medicine approach. Several key findings emerge from the current evidence base: (i) the neonatal brain is a developmentally constrained system in which excitation–inhibition imbalance, transient circuits and immature long-range connectivity shape an electroclinically distinct seizure phenotype; (ii) cEEG is the gold standard for detection and quantification of seizure burden, since the majority of neonatal seizures are electrographic-only and bedside clinical recognition systematically underestimates true seizure burden; (iii) etiology—chiefly HIE, stroke, and genetic causes—remains the strongest determinant of outcome, while seizure burden acts as an independent and potentially modifiable prognostic modifier; (iv) phenobarbital retains an evidence-based advantage in acute electrographic seizure control, whereas levetiracetam offers a favorable safety profile in the absence of robust long-term human neurotoxicity data; (v) rapid genomic diagnostics, artificial intelligence-assisted EEG analysis and multimodal neuromonitoring are converging toward a precision-neonatology framework, but their translation into routine practice requires validation, standardization, and equitable access. Future neonatal seizure care should extend beyond seizure control to the preservation and optimization of neurodevelopmental outcomes. Full article

Extensive explorations in neuroscience, psychology, and psychotherapy increasingly recognized the embodied and relational foundations of selfhood, underscoring the need for an integrated framework spanning development, psychopathology, and therapeutic change. This narrative review synthesizes empirical and theoretical literature across neuroscience, embodiment research, predictive processing, developmental science, phenomenology, and psychodynamic theory, proposing a multidimensional neuropsychodynamic framework of embodied selfhood and its clinical implications. A central contribution is the positioning of Peripersonal Space (PPS) as an embodied action-oriented interface that functions as a primary developmental scaffold for bodily self-consciousness, self-other relations, affect regulation and temporal continuity. PPS is proposed as a dynamic matrix linking embodied predictive self-processes with relational experience, thereby shaping subjective temporality and autobiographical processes. Within this framework, subjective time emerges through bodily rhythms, interpersonal synchronization, and predictive engagement with environmental affordances. These embodied temporal processes gradually extend toward autobiographical continuity and mentalizing capacities, supported by coordinated interactions among large-scale brain networks. Psychodynamic concepts including holding, containment, dimensionality, and symbolic transformation are revisited in dialogue with contemporary embodied and relational neuroscience. Clinically, disturbances of selfhood across psychopathological conditions are discussed in relation to altered PPS organization, disturbances in self-evidencing, and embodied temporal continuity. Psychotherapeutic change is conceptualized as involving gradual reorganization across embodied, affective, and reflective dimensions through co-regulation, interpersonal attunement, and temporally extended relational engagement. Overall, this perspective advances a process-oriented and interdisciplinary framework linking embodiment, temporality, autobiographical integration, and psychotherapy, while highlighting directions for future interdisciplinary research at the interface of neuroscience, embodiment and psychodynamics. Full article

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by complex interactions among genetic, environmental, and biological factors. Increasing evidence suggests that immune system processes intersect with neurodevelopment in ways that may influence brain maturation, synaptic organization, and large-scale network function. However, existing literature is often fragmented across molecular, cellular, and systems levels, limiting the development of a coherent interpretive framework. In this review, we propose a developmental neuroimmune cascade model of ASD, in which early-life immune perturbations, arising from prenatal or perinatal factors, may interact with genetic susceptibility to influence cytokine signaling, microglial function, blood-brain barrier dynamics, and gut-immune communication. These processes may, in turn, affect synaptic pruning, excitatory-inhibitory balance, and the maturation of neural circuits, contributing to alterations in large-scale brain networks implicated in sensory processing, interoception, and social cognition. We synthesize evidence from observational human studies, postmortem analyses, and experimental animal models to examine how immune-related mechanisms may contribute to neurodevelopmental trajectories associated with ASD, while explicitly distinguishing associative findings from mechanistic inference. Particular attention is given to the role of distributed network vulnerability, including, but not limited to, insula-centered systems that integrate internal bodily states with affective and cognitive processing. Finally, we consider implications for biomarker development and stratified intervention approaches, emphasizing the importance of developmental timing, biological heterogeneity, and cautious interpretation of translational potential. Rather than positioning immune dysfunction as a singular cause of ASD, this model conceptualizes neuroimmune processes as modulators of developmental trajectories, offering a structured basis for future research linking immune signaling to circuit-level and behavioral outcomes. Full article

Functional brain networks in schizophrenia (SZ) are often characterized by covariance-based measures, yet covariance matrices live on a curved geometric structure rather than in ordinary Euclidean space, complicating noise-robust inference from scalp EEG. We develop a Riemannian Geometry-based Adaptive Nonlinear Coupling Analysis (RGA-NCA) framework that integrates the affine-invariant Riemannian metric (AIRM), tangent space mapping (TSM), and an anatomically adaptive artifact rejection (AAAR) strategy accounting for regional signal-to-noise heterogeneity. The framework is grounded in the observation that Euclidean summaries of symmetric positive definite matrices are sensitive to noise-driven volume inflation, whereas geodesic distances on the manifold emphasize shape deformation. RGA-NCA was evaluated on four benchmark dynamical systems, a supplementary multichannel EEG-like sample covariance simulation, and a public button-tone SZ/HC EEG dataset associated with the auditory feedback paradigm described by Ford et al. (81 subjects; 49 SZ, 32 healthy controls). Compared with Euclidean and linear baselines, RGA-NCA showed lower sensitivity to noise-driven distance distortion and yielded clearer group-level contrasts in the tested ROI analyses; all four pre-specified frontotemporal and parietal channel pairs remained significant after Benjamini–Hochberg FDR correction. The resulting patterns are consistent with reduced long-range connectivity together with localized hyper-synchronization-like effects in SZ. Quantitatively, the Riemannian structural sensitivity index $(\mathrm{sim}=\exp (-d^2/4))$ remained high across all tested SNR levels (−20 to +10 dB; 50 Monte Carlo trials per level; range 0.936–0.964), with only a 0.026 endpoint change between +10 and −20 dB, whereas the Euclidean metric fell from 0.922 at +10 dB to 0.000 at −20 dB. These findings support Riemannian modeling as a candidate strategy for noisy covariance-based neural data, pending validation in larger independent cohorts. Full article

Emotion recognition through electroencephalogram (EEG) signals is crucial for brain–computer interfaces (BCIs), yet existing methods often struggle with heterogeneous feature fusion and capturing long-range temporal dependencies. To address these challenges, we propose MAF-TransNet, a novel unified spatiotemporal framework. Specifically, parallel Fully Connected Neural Network (FCNN) modules first non-linearly align heterogeneous differential entropy (DE) and power spectral density (PSD) features. Subsequently, an Adaptive Channel-wise Feature Encoder (ACFE) recalibrates spatial–spectral responses to highlight emotion-relevant cortical activations. Finally, a Transformer encoder dynamically models the global temporal evolution of emotional states. Evaluated on the SEED-IV and DEAP datasets, MAF-TransNet achieves superior subject-dependent (SD) accuracies of 88.80% and 96.58%, respectively, alongside robust subject-independent (SI) performance. Furthermore, Granger causality analysis reveals distinct emotion-dependent prefrontal asymmetry, while t-SNE visualizations confirm the formation of a highly discriminative, linearly separable feature manifold. Ultimately, MAF-TransNet effectively unifies local spatial–spectral extraction with global temporal modeling, providing an accurate and robust approach, while offering preliminary insights into the spatiotemporal dynamics of emotion for future affective BCI applications. Full article

Introduction: Schizophrenia is characterised by distributed abnormalities in electrophysiological dynamics and large-scale brain networks, yet unimodal EEG or MRI alone cannot fully explain how fast neural computations relate to spatially organised circuit dysfunction. Multimodal EEG–MRI approaches offer a bridge across temporal and anatomical scales by explicitly modelling cross-modal coupling. Methods: Following PRISMA 2020 guidance, we conducted a systematic, mechanistic review of human studies (adults ≥ 18 years) comparing schizophrenia-spectrum groups with healthy controls using EEG combined with at least one MRI modality (fMRI, structural MRI, and/or diffusion MRI) and explicit EEG–MRI integration (e.g., EEG-informed fMRI, joint ICA, mCCA/MCCA, coupled matrix–tensor factorisation, DCM-based fusion). Searches were performed in PubMed/MEDLINE, Embase, Web of Science, Scopus, PsycINFO, IEEE Xplore, ResearchGate, and Google Scholar for January 2000–December 2025, supplemented by citation tracking. Risk of bias was assessed with ROBINS-I, and due to heterogeneity, results were synthesised narratively by integration of families. Results: From 148 records, 23 studies met the inclusion criteria. Studies used mainly simultaneous EEG–fMRI at 3T and spanned resting-state designs and task paradigms dominated by auditory processing (oddball, MMN/N100–P200, ASSR/aeGBR), with additional work in affective context, working memory, semantic processing (N400), sensory gating, and pharmacologic challenge. Across tasks, the most reproducible multimodal signature was disrupted coupling between electrophysiological markers and the recruitment of large-scale networks, rather than isolated changes in EEG or fMRI metrics. Target detection/oddball paradigms converged on reduced late ERP responses (especially P300, sometimes N2) alongside reduced expression or loss of coupling to salience/ventral attention and control circuitry (including ACC/anterior insula/TPJ). Resting-state studies most consistently indicated altered “coupling rules” (frequency specificity, timing/lag structure, and directionality), including abnormalities detectable even when unimodal summaries were weak. Extended multimodal studies (adding sMRI/DTI and/or classification) suggested that combining modalities can improve discrimination, though performance was sensitive to sample size, demographic imbalance, and feature-selection/validation choices. Conclusions: Multimodal EEG–MRI studies support schizophrenia as a disorder involving persistent structural and circuit-level abnormalities whose functional expression varies dynamically across cognitive states and task demands. Future progress will depend on harmonised acquisition/artefact-control practices for simultaneous EEG–fMRI, larger and more diverse samples (including early/CHR and longitudinal designs), and cross-site replication of mechanistically interpretable coupling biomarkers. Full article

Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, and electroencephalogram (EEG) signals provide a direct measure of brain activity for detection. Although deep learning achieves high accuracy, it often lacks physiological interpretability. We propose the Excitation/Inhibition Dynamic Polynomial Network (E/I-DynPolyNet), a biologically grounded framework for interpretable seizure detection. Specifically, E/I-DynPolyNet introduces a dual excitatory/inhibitory (E/I) pathway with sign-constrained synaptic weights, encouraging the learned activations to reflect latent E/I representations. Furthermore, a differentiable Wilson-Cowan (WC) module is embedded to govern the temporal evolution of E/I interactions, ensuring consistency with neurophysiological principles. A physics-informed optimization strategy integrates supervised learning with dynamical residual constraints and E/I balance regularization, guiding the model to learn physiologically consistent representations. Experimental results on the CHB-MIT and Bonn datasets demonstrate competitive accuracies of 95.81% and 98.5%, respectively. Crucially, E/I-DynPolyNet enables quantitative estimation of E/I imbalance, revealing that E/I ratios increase from 1.01 in the pre-ictal phase to 1.38 during seizures—a finding consistent with clinical observations of ictogenesis. These results indicate that E/I-DynPolyNet not only improves detection performance but also provides a mechanistic description of seizure dynamics, bridging the gap between data-driven learning and neurophysiological interpretation. Full article

Background: Chronic exposure to addictive substances induces persistent alterations in neural dynamics, reflecting maladaptive brain plasticity. While such changes are well documented using neuroimaging techniques, their electrophysiological signatures—particularly those derived from nonlinear EEG complexity—remain insufficiently explored across diverse substance use profiles. This preliminary study aims to investigate whether nonlinear EEG complexity measures can serve as sensitive biomarkers of maladaptive plasticity in substance use disorder (SUD) across multiple substance categories. Methods: A total of 350 participants were included and categorized into seven groups (n = 50 each): six substance use groups (cannabis, heroin, heroin–cannabis, methamphetamine–cannabis, methamphetamine–heroin, and multi-drug) and one control group without a diagnosis of substance use disorder. Resting state EEG signals were recorded using an eight-channel system. Four nonlinear features, Largest Lyapunov Exponent (LLE), Fractal Dimension (FD), Hurst Exponent (HE), and Kolmogorov Complexity (KC) were extracted. Statistical analysis was performed using two-way ANOVA, and classification was conducted using the K Nearest Neighbour (KNN) algorithm. Results: Significant group differences (p < 0.05) were observed across all nonlinear features. Control participants without a diagnosis of substance use disorder consistently exhibited higher complexity values compared to substance use groups, indicating reduced neural dynamical variability associated with the history of sustained substance uses over multiple years. Region wise analysis revealed that frontal and central cortical areas linked to motor planning and sensorimotor integration were particularly affected. The KNN classifier achieved an accuracy of 98.4%, sensitivity of 100%, and specificity of 96.8%. Conclusions: Nonlinear EEG complexity measures provide a robust electrophysiological marker of substance induced maladaptive brain plasticity. The observed reduction in complexity reflects impaired neural adaptability, particularly within motor control networks. These findings highlight the potential of EEG based complexity metrics for objective assessment, classification, and neurorehabilitation monitoring in substance use disorders. Full article

Clinical outcomes after acute ischemic stroke remain highly heterogeneous, even among patients with comparable lesion characteristics and successful reperfusion, challenging traditional lesion-based models. Increasing evidence suggests that stroke should be conceptualized as a disorder of distributed brain networks, yet the mechanisms linking focal ischemia to large-scale dysfunction remain incompletely understood. In this review, we propose that diaschisis constitutes a central physiological mechanism underlying this transition from focal injury to network-level impairment. Building on advances in functional imaging, connectomics, and cerebellar physiology, we propose that diaschisis may be conceptualized, at least in part, as a disruption of cerebello-cortical loop dynamics rather than solely a nonspecific remote effect. These closed, polysynaptic circuits linking cortex, cerebellum, and thalamus support the integration of motor and cognitive processes and are particularly vulnerable to perturbation. Focal ischemia may therefore induce a cascade of dysfunction that propagates across these loops, leading to widespread impairment despite limited structural damage. Within this framework, outcome variability emerges from the interaction of three key factors: lesion characteristics, brain reserve and network vulnerability, and the extent of diaschisis. We further highlight that functional suppression of cerebellar output, even in the absence of structural degeneration, may play a critical role in mediating network dysfunction. This circuit-based perspective provides a mechanistic explanation for inter-individual variability in stroke outcomes and shifts the focus from lesion localization to network dynamics. Understanding diaschisis as a potential manifestation of cerebello-cortical loop dysfunction opens new avenues for prognosis and therapeutic intervention, emphasizing the potential of targeting network-level restoration to improve recovery after stroke. Full article

Epilepsy is increasingly conceptualized as a disorder of dynamic network instability rather than a static imbalance between excitation and inhibition. However, substantial variability in seizure occurrence, clinical expression, and treatment response remains insufficiently explained by existing models. This narrative review examines how neurotransmitter systems contribute to seizure dynamics within a state-dependent framework, in which factors such as sleep–wake cycles, stress, inflammation, and metabolic conditions modulate network excitability. The review identified four key findings: neurotransmitter function in epilepsy is state-dependent rather than fixed; multiple physiological state modifiers shape seizure susceptibility; seizure termination is an active state-sensitive process; and biomarker performance depends on the prevailing brain state. Evidence from experimental and clinical studies indicates that neurotransmitter function is context-sensitive and interacts with molecular pathways, including ion channel function, synaptic plasticity, and neuromodulatory signaling. These interactions influence key stages of seizure dynamics, including initiation, propagation, and termination, and may differ across etiological categories of epilepsy. This perspective also helps explain the limited performance of static biomarkers, as they do not capture temporal variability in network states. Instead, state-sensitive markers and context-aware interpretations of electrophysiological and clinical data may provide more informative insights. Overall, integrating neurotransmitter mechanisms with dynamic brain states offers a more precise perspective on seizure variability and may support the development of individualized, state-aware approaches to epilepsy management. Full article