Brain Topography

Typical brain network maturation involves an increase in network flexibility and hemispheric specialization. Tourette syndrome (TS) disrupts these trajectories, with tic severity potentially modulating deviations. This study examined theta-band EEG source connectivity states in typically developing children and children with TS. We assessed age-related trajectories and the impact of tic severity using generalized linear modeling, accounting for sex and multiple comparisons. K-means clustering identified four recurrent source connectivity states (A-D), with metrics including Coverage, representing state prevalence (proportion of time spent in each state), Average Dwell Time, an index of state stability (mean duration of stable persistence of each state), and Transition Rate Per Minute, reflecting global network flexibility (frequency of state switches per minute). In healthy controls (HC), typical maturation was characterized by increased left intra-hemisphere connectivity state stability and prevalence, decreased diffuse connectivity state stability, and rising network flexibility. TS patients exhibited deviant trajectories, including age-dependent decreasing global network flexibility across subgroups stratified by tic severity and marginally divergent diffuse activity patterns, with high-severity cases showing increased diffuse connectivity state stability. The normative patterns suggest typical motor development requiring dynamic network reconfiguration and hemispheric specialization, processes that appear altered in TS. TS patients exhibit age-dependent network rigidity across severity subgroups, as reflected by decreased transition rates, alongside severity- modulated network imbalances, indicating that tic disorders disrupt mechanisms of brain network maturation underlying motor control. These findings suggest that atypical trajectories of network stability and flexibility represent a key feature of tic pathophysiology, highlighting the role of altered network dynamics in TS during maturation.

Objects moving between the left and right visual hemifields are naturally perceived as continuous entities, although early visual processing independently transmits information from the two hemifields. Therefore, interhemispheric integration of visual information is essential for maintaining an object's identity. Additionally, brain function is thought to be maintained through a dynamic balance between integration and segregation. In this study, we investigated the functional neural architecture underlying visual hemifield integration in healthy adults, using electroencephalography (EEG) and a visual integration task. To capture neural oscillatory networks without relying on prior assumptions regarding electrode pairs or frequency bands, we applied a frequency-inclusive, data-driven network analysis based on an extended network-based statistic. This analysis identified a broadband EEG phase synchronization network that emerged specifically under task conditions with high interhemispheric integration demands. Furthermore, individual differences in behavioral performance were associated with modulation of interhemispheric synchronization, with this relationship differing according to participants' relative performance across task conditions. These findings suggest that visual hemifield integration is supported by large-scale phase synchronization networks spanning multiple frequencies and are consistent with the importance of a balance between integration and segregation.

Accurate phase tracking of deep-brain activity is critical for effective closed-loop and phase-locked neuromodulation therapies. However, direct access to deep neural phase through intracranial recordings remains clinically restrictive due to the invasiveness. Here we validate and clinically benchmark the Gabor-Nelson (GN) dipole estimation method for reconstructing deep-brain oscillatory phase from non-invasive scalp EEG. GN is a geometry-based, imaging-independent approach that offers computationally efficient dipole reconstruction and has rarely been applied to source-level phase estimation in human neuroscience. We compared GN with an established MRI-informed Inverse Solution (IS) method using a three-stage reconstruction pipeline consisting of dipole modeling, dimensionality reduction, and frequency-dependent phase-delay correction. Validation is performed using (i) cadaveric recordings, where known ground-truth seizure waveforms were replayed through implanted deep electrodes, and (ii) simultaneous scalp EEG and SEEG recordings in human patients, where pseudo-ground truth was approximated via the intracranial contacts. GN achieved phase accuracy and signal fidelity comparable to IS across both datasets despite requiring no anatomical imaging. In cadaver recordings, phase-corrected reconstruction correlations exceeded r > 0.91 and {Delta}{Phi} < 9{degrees} in mean phase error. In patient SEEG data, GN reached up to r {approx} 0.80 with phase offsets suitable for neuromodulatory timing. GN offers a viable, low-barrier, imaging-independent alternative to traditional inverse modeling for non-invasive seizure phase tracking. This framework opens pathways for scalable, phase-locked and closed-loop stimulation therapies in epilepsy and potentially other network-based brain disorders.

Metastability is a fundamental dynamical property of large-scale brain networks and reflects the capacity of the brain to flexibly reorganize transient coordination patterns. In this study, we investigated whether metastable properties of resting-state electroencephalographic (EEG) phase synchronization networks are associated with individual differences in autistic traits. Resting-state EEG data from 88 neurotypical adults were analyzed using two complementary metrics: synchrony coalition entropy (SCE), which quantifies the diversity of transient phase synchronization patterns, and the metastability index (MSI), which quantifies temporal variance in global phase synchronization. SCE showed frequency-specific associations with the Autism-Spectrum Quotient (AQ) attention-switching subscore at 18-24 Hz and the communication subscore at 4-8 Hz, suggesting that frequency- and network-specific patterns of metastable synchronization are associated with distinct aspects of autistic traits. In contrast, MSI showed a modest association with the social-skill subscore in the lower-beta range, but this effect did not survive a cluster-based permutation test. This exploratory observation suggests that global synchronization variability may capture a weaker, complementary aspect of trait-related metastable dynamics. These findings suggest that, within a neurotypical population, individual differences in autistic traits may be more sensitively captured by the repertoire of transient phase synchronization patterns, as indexed by SCE, than by global phase synchronization variability, as indexed by MSI. Moreover, the associations of distinct AQ subscores with SCE in different frequency ranges suggest that different dimensions of autistic traits may be related to metastable network dynamics operating at different temporal scales. Author SummaryThe brain constantly coordinates activity across many regions, and this coordination changes over time rather than remaining constant. Understanding these dynamic patterns is important for explaining individual differences in cognition and behavior. In this study, we focused on a dynamical property called "metastability," which describes how brain activity flexibly shifts between different patterns of coordination. Instead of remaining in a stable state, the brain repeatedly forms and dissolves coordinated activity across regions. We analyzed brain signals recorded with resting-state electroencephalography (EEG) and examined whether these dynamic patterns were related to individual differences in autistic traits. We found that different aspects of time-varying coordination were linked to different dimensions of autistic traits in a neurotypical population. These findings suggest that examining how brain activity changes over time, rather than relying only on time-averaged measures, can reveal neural features associated with individual differences in autistic traits. Our study highlights metastability as a useful concept for understanding the flexible and dynamic nature of human brain function.

Abstract Optically pumped magnetometer (OPM) sensors can be placed closer to the scalp than conventional superconducting quantum interference devices (SQUID), resulting in larger magnetoencephalography (MEG) signals from neuronal activity. For event-related sensor data, such as epileptogenic activity or sensory and motor evoked responses, however, OPMs and SQUIDs often differ less in signal-to-noise ratio (SNR) than in signal magnitude. We examined two factors contributing to the relative SNR: the dependence of the signal magnitude on source depth and the effect of scalp-to-sensor distance on the noise level. Simulated MEG data for a current dipole in a spherical head model confirmed that on-scalp sensor placement delivers the largest SNR gain for superficial sources. Depending on the relative overall noise level, there may be a crossover source depth at which SNR is equal for on-scalp and off-scalp sensors and beyond which off-scalp sensors achieve higher SNR. Analysis of the equal-SNR source depth in different-sized spherical head models indicated that, for a given relative noise level, the proportion of the brain where SNR is higher in OPM than in SQUID was larger in small head models, supporting the benefits of OPMs in pediatric studies. To experimentally evaluate noise contributions of brain and non-brain origin to the SNR, we recorded somatosensory evoked fields (SEFs) at varying scalp-to-sensor distances. Generally, both the evoked response magnitude and the noise level were lower when the sensors were further away from the scalp; consequently, the SNR depended less than the signal magnitude on the scalp-to-sensor distance. Comparison of power spectral densities (PSDs) at different sensor-to-scalp distances allowed us to identify whether the dominant noise source was of brain or non-brain origin at different frequency bands. Overall, the results highlight complementary properties of OPMs vs. SQUIDs in terms of SNR, which is of interest when optimizing MEG experiments for specific subject populations and brain regions.

Non-invasive, whole-brain neuroimaging methods such as functional magnetic resonance imaging, electroencephalography (EEG), and magnetoencephalography (MEG) are essential tools for studying the basis of human cognition in health and disease. MEG offers the opportunity to study neural activity at its intrinsic timescale, by recording the magnetic fields generated by electrical currents within the brain from outside the skull. Moreover, recently developed optically pumped magnetometers (OPMs) allow these recordings to take place in new settings, for example during naturalistic behaviour and in previously inaccessible populations. These breakthroughs have led to a shift in the neuroimaging landscape, with a global increase in the adoption of MEG. Crucially, however, the extent to which MEG recordings can measure different features of neural activity remains unclear. To address this issue, we leveraged a unique and rare dataset of concurrent MEG and intracranial EEG recordings from a cohort of epileptic patients. We found that group-level inferences of spontaneous oscillatory dynamics made with source-localised MEG, i.e. estimates of power and bursts, accurately reflected the underlying neural activity. As expected, the agreement was strongest for lower-frequency activity (delta, theta, and alpha) and superficial sources, and weakest in the gamma range. Crucially, however, MEG was also sensitive to deep structures: it captured oscillatory power and burst dynamics in the hippocampus, most robustly in the theta band. These findings demonstrate that MEG is sensitive to physiologically meaningful activity in cortical and subcortical regions and establish a foundation for the interpretation of future MEG studies across a wide range of research domains.

EegFun.jl is an open-source package for electroencephalography (EEG) analysis implemented in the Julia programming language. EegFun.jl provides a flexible framework for EEG research, covering data import from standard file formats, filtering and re-referencing, Independent Component Analysis (ICA) for artifact detection/correction, epoch extraction, and ERP averaging and visualisation. The Julia language provides the readability of a high-level scripting environment together with execution speeds comparable to compiled code. EegFun.jl combines interactive data visualization with high-performance execution, making large-scale analyses both efficient and easy. Here, we provide a brief overview and introductory tutorial of the core stages of the EEG analysis workflow to illustrate the packages capabilities. The package is freely available under the MIT license.

The development of the sleep-wake cycle reflects the progressive structural and functional maturation of the brain. However, the organization of neural dynamics during prematurity remains incompletely understood. In this study, we analyzed the EEG from 54 polysomnographic recordings obtained from 39 preterm infants, grouped according to postmenstrual age (PMA) into three categories: 30-31, 32-33 and 34-35 weeks. Lempel-Ziv Complexity (LZC) and Joint Lempel-Ziv Complexity (JLZC) of the electroencephalogram (EEG) were analyzed during active sleep (AS), quiet sleep (QS), and indeterminate sleep (IS). LZC computed from the raw, unfiltered recordings were significantly higher during QS than during AS and increased with PMA during AS. To further refine the analysis, LZC was also evaluated separately in the low-frequency (1-15.5 Hz) and high-frequency (16-30 Hz) EEG bands. In the low-frequency band, LZC was consistently higher during QS than during AS, an effect that was most pronounced in more immature groups. Furthermore, LZC increased with maturation particularly during AS. Sleep-state comparisons of LZC in the high-frequency EEG band also revealed higher values during QS than during AS across all PMA groups. Moreover, in contrast to the low-frequency band, LZC progressively decreased with advancing PMA both in AS and QS, suggesting that the neural mechanisms underlying low- and high-frequency EEG activity follow distinct maturational trajectories. Interestingly, larger LZC in the temporal cortex and interhemispheric differences were detected in the 32-33 PMA group. On the other hand, JLZC analysis revealed greater joint spatiotemporal dynamics across EEG channels during QS than during AS, with consistently higher JLZC values in temporal regions and lower in occipital regions. Together, these findings show that these complexity metrics distinguishes sleep states and captures maturational changes in EEG activity in preterm infants. These results provide novel insights into early brain development and suggest potential quantitative biomarkers of neonatal brain maturation.

During wakefulness, we are used to perceive the environment through our senses, act on it and take these inputs to update our experiences and build the perceptions. When the inputs are not longer accurate or structured, people would sometimes have hallucinatory experiences. Whether such experiences are associated with distinct patterns of thought, and how they relate to large scale brain dynamics, remains unclear. To address these questions, we combined experience sampling protocol with EEG recording during multimodal Ganzfeld, where participants were exposed to unstructured, uniform visual and auditory stimulation. Participants repeatedly reported the complexity of their visual experiences together with ongoing thoughts related to perceptual belief, prediction perception mismatch, active updating, and prior mentation. EEG microstates were extracted to characterize the temporal dynamics of large-scale brain networks. We found that visual complexity was related to all four dimensions, but partly distinct in simple and complex visual experiences. These phenomenological changes were accompanied by distinct, and often nonlinear, dynamics of large-scale brain networks involved in visual processing, salience detection, and internally directed cognition. It also indicates that this paradigm might be a valuable model for investigating the mechanisms underlying hallucinatory experiences in psychosis.

Dietary iron deficiency in the absence of anemia (IDNA) affects numerous people worldwide, with a wide range of negative effects on brain functioning and cognition. Although studies employing electroencephalography (EEG) have revealed a number of negative effects of IDNA in both the time- and frequency domains, to date there have been no attempts to characterize the effects of IDNA on the temporal dynamics of whole brain interactions. To address this issue, we applied multiscale entropy (MSE) analysis to resting-state EEG data collected from IDNA (n = 21) and iron sufficient (IS, n = 21) women. The MSE analysis on this data revealed that entropy was higher overall for the IS than the IDNA group, with significant differences appearing primarily at longer time scales and under right frontal and left and right parietal electrodes. These results suggest that IDNA may negatively affect long-distance interactions among brain regions and that this could conceivably be a source of diminished cognitive function and neural resilience in IDNA.

A novel mathematical framework to define the threshold of action potentials in excitable cells is presented. Unlike previously applied methods that rely on approximations or bifurcations, the approach focuses on the geometry of membrane potential trajectories. The changes in concavity during the upstroke of an action potential can be directly obtained from a time series of voltages. The concavity criterion is then extended to models based on autonomous dynamical systems where the changes in concavity can be obtained analytically from a curve of inflection points in phase space. The inflection point manifold defines a region required for excitability: all the orbits that cross it contain action potentials, and all the trajectories that contain action potentials are in it. This analytical principle can then be used to define excitability in a dynamical system, and also a measure of excitability that enables quantification and comparisons of excitability across dynamical system. The measure provides a way to compare the excitabilities of systems that model neurons with different electrophysiological phenotypes and consider different stimulus conditions. The traditionally vague physiological concept of electrical excitability is transformed into a rigorous analytical description by considering the time-dependent curvature of the membrane potential. The criterion is robust across smooth, single compartment models of electrical excitability and can be can be extended to single compartment models in higher dimensions, and multicompartment models as well.

Despite the importance of sustaining attention focus throughout a task, sustained attention research demonstrates a rapid decline of task-focus with time-on-task. Separate research body highlights perceptual load as critical determinant of focused attention, showing that increased perceptual load draws more neural energy into task-relevant processing (Bruckmaier et al., 2020) and improves attention focus (Lavie, 2005). However, the effect of perceptual load on the neurophysiological mechanisms underlying time-on-task impact on sustained attention remains unknown. This was the aim of the present study. Participants performed a gradual continuous-performance task, detecting infrequent mountain scenes, among streams of city scenes, under either high or low perceptual load (with or without overlaid salt-and-pepper noise, respectively). EEG was recorded and parameterised into periodic and aperiodic components; the aperiodic 1/f slope linked with excitation-inhibition (E/I) balance: steeper slopes reflecting reduced E/I ratio (Gao et al., 2017). Time-on-task resulted in a wide-spread increase in alpha power, and a steeper 1/f slope in a left temporal-parietal cluster, accompanied by reduced detection sensitivity and increased response variability, as well as increased mind wandering, with reduced thoughts detail. Perceptual load improved task focus, as indexed by reduced mind wandering, but exacerbated the effect of time-on-task on detection sensitivity, and the 1/f slope, which was steeper with time-on-task in a right parieto-occipital cluster with increased load. Overall, the findings suggest that sustained attention decline with time-on-task can be attributed to depletion of neural energy needed for excitatory signalling, which is further drained with increased processing demands in tasks of high perceptual load.

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to reflect the underlying neural mechanisms during information processing and are therefore also called the "building blocks" of human thought. In this study, we examined, if EEG microstate sequences can serve as potential triggers for a Brain-Computer Interface (BCI). To this end, a semi-supervised deep learning model architecture consisting of an LSTM-based autoencoder and a dense neural network was utilized to classify between left- and right-hand motor imagery EEG data, with the resulting classification output serving as the BCI trigger. On the one hand, this was done in a 2-step approach, in which the autoencoder and classifer have been trained separately. On the other hand, an end-to-end approach was employed, where training was performed by combining reconstruction and classification losses. Results show that the proposed model architecture was able to extract relevant features from microstate sequences and exploit them for within subjects and sessions classification. Applying transfer learning to session-to-session or across-subject transfer resulted in peak classification accuracies around 89%. We also investigated to what extent transfer learning has to be applied to reach considerable classification accuracies serving as the calibration time representative. We found that on average around 400s are needed for BCI calibration when emplyoing our approach to reach 80% classification accuracy. The present study signifies that the investigation of EEG microstate trajectories can be a promising approach for extracting BCI triggers, as it reduces the dimensionality of multi-channel recorded EEG signals to a distinct number of brain states over time. Deep learning methods, especially transfer learning, applied to EEG microstate trajectories seem promising regarding user-convenient and calibration-free BCIs in real-world applications.

Deep brain stimulation (DBS) of the ventral intermediate nucleus (Vim) of the thalamus may be used to treat medication refractory essential tremor. Using recordings from in vivo human Vim neurons, our previous work has suggested that evoked potentials (that we termed quasi-evoked inhibition) ~2 ms following high frequency microstimulation pulses may be related to inhibitory synapses onto the Vim. Here, we investigate whether (i) quasi-evoked inhibition is related to clinical tremor reduction, and (ii) if quasi-evoked inhibition is dependent on the stimulation location within the Vim. By developing an objective determination of the presence or absence of quasi-evoked inhibition and utilizing accelerometer recordings, we showed that recordings with quasi-evoked inhibition at 100 Hz microstimulation exhibit greater tremor reduction than those without (P < 0.05, BF > 30). The number of stimulation pulses with quasi-evoked inhibition is also correlated with tremor reduction (rho = 0.18, P < 0.05) at all stimulation frequencies >=100 Hz. Furthermore, by analyzing microelectrode trajectories reconstructed from structural MRIs, we found that proximity to the ventral caudal border (P < 0.005) and to a previously established sweet spot (P < 0.05) are anti-correlated with the number of stimulation pulses with quasi-evoked inhibition. Our findings suggest that quasi-evoked inhibition is a potential biomarker of tremor reduction by means of network inhibition, and the more posterior regions of the Vim may allow for better recruitment of inhibition. This may be useful for closed-loop stimulation design.

Classical neurogeometric models describe the primary visual cortex as a fibered structure in which retinal position and local orientation are coupled through the geometry of the roto-translation group. We extend this approach to the visuomotor cortex by modeling it as an assemblage of visual and motor cortical geometries. The model combines orientation-selective representations, analogous to those of the primary visual cortex, with movement-direction-selective representations, analogous to those of the primary motor cortex, in order to describe the mixed visual and motor selectivity observed in the visuomotor cortex. We introduce a coupled visuomotor structure in which visual orientation and motor direction coexist over a common spatial plane and interact through a relative-orientation constraint. Neural responses are modeled by orientation- and direction-dependent profile functions, and preference maps are obtained from vectorized population responses. Numerical simulations generate visual, motor, and mixed visuomotor response maps. A competition rule between visual and motor responses produces incidence ratios close to experimental observations in macaque visuomotor cortex. This framework provides a first neurogeometric approximation of visuomotor functional architecture and a mathematical setting for studying visually guided action.

Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with development underlying reading comprehension. This study aims to address this issue by combining functional magnetic resonance imaging (fMRI) with Hidden Markov Model (HMM) to explore brain state dynamics. A total of 35 typically developing children and 31 adults were scanned while reading a story. Our results demonstrated a tripartite brain state organization, characterized respectively by high activities in the visual (State #1), language (State #2), and default mode network (DMN, State #3) regions. Children exhibited significantly longer dwell time in the DMN state (State #3) compared to adults, along with a higher probability of transitioning from the language state (State #2) to the DMN state (State #3). In addition, adults exhibited greater flexibility in state transitions during reading comprehension. Finally, the alignment between the dynamic states of children and the average states of adults was a significant positive predictor of their reading comprehension performance. This study provides a novel, intuitive perspective on how brain state dynamics evolve during the development of reading comprehension.

Effortful cognition is typically associated with controlled, task-constrained neural processing, whereas effortless awareness may require a more flexible, internally driven mode of brain organization. Critical brain dynamics provide a principled framework for characterizing this shift, as systems near criticality are thought to balance stability and flexibility, allowing efficient information processing without excessive control. Transcendental Meditation (TM), characterized by a shift from effortful mental engagement to effortless awareness, offers a natural model for testing this possibility. Here, we investigated whether critical brain dynamics track TM as a global meditative state, or instead reflect moment-to-moment fluctuations in subjective effortlessness. We combined high-density electroencephalography (EEG) with time-resolved phenomenological reports using Temporal Experience Tracing (TET). Experienced TM practitioners (N = 33) and matched controls (N = 33) completed resting-state recordings before and after a 30-minute TM or silent counting control task. Long-range temporal correlations (LRTCs) were quantified using detrended fluctuation analysis, while functional excitation/inhibition (fEI) balance was used to estimate directional deviations from criticality. State-based analyses showed that TM increased alpha and beta LRTCs relative to pre- and post-resting state within meditators, but revealed no robust between-group differences in either LRTCs or fEI balance. In contrast, neurophenomenological analyses showed that subjective effortlessness was robustly associated with increased theta, alpha, beta, and broadband LRTCs, with significantly stronger relationships in meditators than controls. Restricting analyses to low-effort periods further revealed higher beta LRTCs in meditators, a difference missed by conventional state comparisons. These findings identify scale-free neural dynamics as a candidate marker of "letting go" during meditation.

Quantifying longitudinal changes in an individual's brain is central to the development of personalised neural biomarkers in neurology and psychiatry. However, existing approaches for characterising individual neurophysiological signatures focus on discrimination between people rather than the quantification of within-subject change. To address this, we introduce the Brain Stability Index (BSI), a whole-brain metric that quantifies the similarity between two longitudinal neurophysiological scans in a low-dimensional latent space, with reference to a normative magnetoencephalography (MEG) database. Using 276 open resting-state MEG datasets and matched synthetic data, we first characterise how finite test-retest reliability sets a noise floor on the BSI. We then demonstrate that the BSI is sensitive to graded changes in whole-brain neural change that extend beyond measurement variability. Finally, we show that Factor Analysis, by separating shared structure from feature-specific noise, makes the BSI more robust to measurement artefacts. Together, these findings establish the BSI as a robust, bounded measure of neural stability that is well suited to longitudinal monitoring in neurology and psychiatry.

BackgroundLocomotor adaptation is essential for adjusting walking patterns to complex environments. This study investigated locomotor adaptation deficits in people with Parkinsons disease (PD) and examined oscillatory activity in the globus pallidus internus (GPi) during walking adaptation. We hypothesized that elevated beta-band activity in the GPi is associated with reduced locomotor adaptability in PD. MethodsTwelve PD patients with GPi deep brain stimulation (DBS) (eleven bilateral and one unilateral) were included. Local field potentials (LFPs) were recorded from DBS electrodes during split-belt treadmill walking. Patients were tested in the medication-off, DBS-off state. Locomotor adaptation was measured as the change in step length asymmetry during split-belt walking, with smaller changes indicating greater adaptation deficits. ResultsWe found that GPi high beta (20-30 Hz) and low gamma (30-60 Hz) oscillations were modulated during split-belt walking. Compared to adapters, non-adapters showed decreased movement-related beta suppression during walking. Across participants, beta activity in the GPi contralateral to the fast leg was negatively associated with adaptation magnitude (Spearmans {rho} = -0.65 to -0.75). ConclusionsGPi oscillations are dynamically modulated during locomotor adaptation in PD. Increased beta activity may underlie impaired sensorimotor adaptation during walking. These findings provide novel insight into basal ganglia mechanisms of gait adaptation in PD and suggest that elevated GPi beta activity may serve as a marker of locomotor adaptation deficits.

Aperiodic (1/f-like) EEG activity has rapidly become a popular noninvasive marker of cortical network state, proposed to index excitation-inhibition (E/I) balance and increasingly applied across neurological and psychiatric disorders. However, whether this approach remains reliable in the pathological brain, where disease progressively reorganizes neural networks, alters signal morphology, and drives continuous transitions between cortical states has yet to be systematically established.Using a medication-free genetic model of chronic epilepsy (Lafora disease; Epm2a-- mice), we tracked the aperiodic component of the cortical EEG across resting wakefulness, isoflurane anesthesia, and PTZ-induced seizures of graded severity, asking how a single spectral marker behaves as the brain moves between states. Epileptic mice exhibited systematically steeper aperiodic exponents than controls, an effect that persisted after removal of interictal epileptiform discharges and was replicated using independent time-resolved spectral parameterization. Slopes steepened predictably under GABAergic anesthesia, supporting the interpretation that aperiodic activity captures biologically meaningful state transitions beyond simple contamination by pathological waveforms. Across seizure phases, the aperiodic exponent varied systematically, however, the exponent flattened during ictal activity in step with the dominant discharge morphology, revealing that pathological waveform shape itself is a substantial contributor to seizure-state exponent changes. Together, these findings indicate that aperiodic EEG dynamics reflect a combination of chronic network-state reorganization and waveform-shape-driven spectral distortion, with their relative contributions varying across brain states. These results support spectral parameterization as a sensitive approach for tracking pathological neural activity in chronic epilepsy while delineating its interpretive boundaries in the presence of pathological waveforms.