Clinical Neurophysiology

Objective: Computational magnetoencephalography (MEG) interictal epileptiform discharge (IED) detectors have mainly used generalized MEG-only models, whereas clinical MEG interpretation routinely integrates simultaneous electroencephalography (EEG) and includes MEG-unique or MEG-dominant discharges. We developed a patient-specific EEG-MEG IED detector and evaluated event-wise prediction stability across models and the effect of adding EEG to MEG-based prediction. Methods: Seventeen patients undergoing clinical EEG-MEG evaluation for epilepsy were retrospectively analyzed. Clinically accepted dipole-review IEDs were treated as positive events, and nonannotated events were sampled as negatives. Logistic regression (LR), random forest (RF), and a lightweight three-dimensional ResNet were trained separately within each patient using EEG-only, MEG-only, and combined EEG-MEG (EMEG) inputs. Primary performance metrics were the area under the receiver operating characteristic curve (ROC-AUC) and average precision. Event-wise stability was assessed using rank disagreement, rank volatility, and class-aware distribution quotient analysis. Results: Aggregate discrimination was high across models and modalities. Median ROC-AUCs for EEG, MEG, and EMEG were 0.850, 0.890, and 0.880 for LR; 0.880, 0.860, and 0.910 for RF; and 0.920, 0.960, and 0.960 for ResNet. Despite comparable aggregate performance, event-wise analysis revealed model-dependent prediction behavior. ResNet showed significantly lower non-IED rank volatility than classical machine learning models and lower non-IED rank disagreement, particularly compared with RF. Adding EEG to MEG was associated with more favorable class-aware event-wise positioning in most events, while MEG-unique/dominant cases showed greater relative MEG contribution. Conclusions: Patient-specific EEG-MEG IED detection revealed clinically meaningful event-wise differences not captured by aggregate metrics. Simultaneous EEG complemented MEG-based detection, while MEG contribution remained prominent in MEG-dominant cases, supporting multimodal patient-specific IED event prioritization.

ObjectiveStroke-related corticospinal tract (CST) disruption causes lasting hand impairments, but many stroke survivors retain some residual CST connections. In neurotypical adults, motor cortex (M1) TMS interventions can strengthen CST transmission when coupled to EEG brain states reflecting heightened M1 excitability. Because stroke alters the relationship between these brain states and cortical excitability, we aimed to identify poststroke brain states that accurately capture ipsilesional M1 excitability. We hypothesized that heightened ipsilesional M1 excitability would be represented by a common, group-level EEG pattern and a participant- specific, personalized pattern. MethodsWe acquired single-pulse TMS-EEG-EMG datasets in 15 chronic stroke survivors with residual CST connections. We then identified group-level and individual-specific EEG power patterns that distinguished between high and low ipsilesional M1 excitability states. ResultsAt the group level, bilateral sensorimotor mu power was significantly suppressed during high versus low excitability states, but this suppression did not correlate with hand impairment severity or trait-level ipsilesional M1 excitability. At the individual level, spatiotemporally varied EEG activity patterns distinguished between excitability states, but these patterns were only present in 60% of individuals. Conclusion and SignificanceThis study is the first to systematically characterize poststroke EEG brain states reflecting ipsilesional M1 excitability. Findings suggest that individual-specific EEG patterns may inconsistently index ipsilesional M1 excitability and instead identify bilateral sensorimotor mu power suppression as a group-level excitability marker that is present across the full spectrum of poststroke hand impairment. HighlightsO_LIWe analyzed TMS-EEG-EMG to identify group and individual level ipsilesional motor cortical excitability states in chronic stroke C_LIO_LIBilateral sensorimotor mu suppression marked heightened ipsilesional motor cortical excitability across hand impairment severity C_LIO_LI60% participants had individual level scalp patterns linked to motor cortical excitability states, challenging their reliability C_LI

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.

Deep brain stimulation (DBS) modeling relies heavily on biophysical neuron models to estimate neural activation thresholds and predict stimulation spread. In this study, we systematically compared a widely adopted axon model, the McIntyre-Richardson-Grill (MRG) model (Model I), with a more detailed biophysical model, the Cohen model (Model II), to assess how structural and electrophysiological differences affect predicted DBS outcomes. Electric field distributions generated by 2202 DBS lead were applied to the neuron models as extracellular input stimuli. Both models were simulated under biphasic pulse stimulation across varying axon-electrode distances, pulse widths, and stimulation frequencies. Activation distances ranged from approximately 2 to 10 mm depending on stimulation parameters and contact location. At 2 mA, Model I achieved an activation distance of 6 mm, whereas Model II reached 10 mm, indicating greater excitability. Across matched fiber tracts, threshold differences ranged from -1.40 mA to 0.27 mA, with Model II requiring lower thresholds in 97.7% of cases. Both models showed a strong inverse relationship between pulse width and activation threshold. However, frequency responses differed: Model II exhibited increasing thresholds at higher frequencies, while Model I showed a slight decrease. Machine learning regressors trained on distance, pulse width, and frequency achieved high predictive accuracy, with Gradient Boosting performing best. Model II demonstrated superior prediction metrics (R^2 = 0.986; RMSE = 0.045 mA; MAE = 0.034 mA) compared to Model I (R^2 = 0.977; RMSE = 0.089 mA; MAE = 0.068 mA). Overall, both models reliably estimate DBS-induced activation, but structural differences significantly affect excitability and frequency-dependent behavior. With appropriate awareness of their respective strengths and limitations, either model can be used to derive activation distances for estimating electric field isolevels and the volume of tissue activated in patient-specific DBS simulations.

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's Disease (PD), but identifying optimal stimulation contacts is time-intensive. Beta-band activity (13-35 Hz) from local field potentials (LFP) correlates with motor symptoms and attenuation by dopaminergic therapy and DBS supports its role as a programming biomarker. The recently introduced Electrode Identifier (EI) feature of Medtronic PerceptTM neurostimulators may facilitate beta-guided contact selection. Objective: To evaluate whether pseudo-monopolar STN beta power obtained using EI predicts optimal stimulation contacts and compare its performance with reconstructed bipolar recordings and MPR. Methods: LFPs were recorded in 69 patients using EI and Electrode Survey (ES). Prediction accuracy was assessed using predefined ranking rules and compared with clinically selected contacts. Agreement between EI, ES, and MPR was evaluated. Motor outcome was assessed using MDS-UPDRS-III. Results: EI predicted clinically selected contacts above chance (TOP1: 45%, p = 0.010; TOP2-80: 57%, p = <0.001), whereas ES exceeded chance only under more inclusive selection criteria (TOP1: 38%, p = 0.073; TOP2-80: 55%, p = 0.0021). Accuracy did not differ between methods (TOP1: p = 0.720; TOP2-80: p = 1.000). EI showed highest agreement with MPR and tended to select ventral contacts. Neither method predicted motor outcome, although EI-matched contacts showed a trend toward greater improvement. Due to technical constraints, one-third of EI recordings were excluded. Conclusions: Pseudo-monopolar STN beta power provides clinically relevant information for DBS contact selection with performance comparable to bipolar approaches. Further improvements are needed before clinical implementation.

Objective: To determine whether paradoxical relief, feeling unusually better after a seizure compared to before it, is more common after functional/dissociative seizures (FDS) than epileptic seizures (ES), quantify its diagnostic accuracy, and explore its relationship with preictal symptoms. Methods: Consecutive patients admitted to a tertiary epilepsy unit for prolonged inpatient EEG monitoring underwent a structured clinical interview on admission, before final multidisciplinary diagnostic classification. Preictal dissociative and autonomic/somatic symptom burden was assessed using items adapted from established questionnaires. Diagnostic classification incorporated clinical history, seizure semiology, video electroencephalography findings, and collateral information. Patients with dual or indeterminate diagnoses were excluded. Associations with paradoxical relief were examined using logistic regression, followed by an exploratory mediation analysis. Results: Of 176 patients assessed, 66 with FDS and 65 with ES were included. Paradoxical relief was reported by 46/66 patients with FDS (69.7%) and 10/65 with ES (15.4%; unadjusted odds ratio [OR] 12.65, 95% confidence interval [CI] 5.57 to 31.09). As a diagnostic signal for FDS, paradoxical relief had 69.7% sensitivity (95% CI 57.1 to 80.4), 84.6% specificity (95% CI 73.5 to 92.4), a positive likelihood ratio of 4.53 (2.51 to 8.19), and a negative likelihood ratio of 0.36 (0.24 to 0.52). FDS diagnosis remained independently associated with paradoxical relief after adjustment (OR 10.59, 95% CI 3.42 to 38.06). In a parallel mediation analysis, dissociative symptom burden showed a significant indirect effect, accounting for 19.5% of the association between diagnostic group and relief, whereas the indirect effect through somatic/autonomic symptom burden was not significant. Significance: Paradoxical relief is substantially more common after FDS than ES and may provide a simple, clinically useful diagnostic signal. Its absence does not exclude FDS, and the finding requires external validation. The association with dissociative symptoms is exploratory and supports prospective investigation of whether relief reflects transient resolution of a disturbed, disembodied preictal state.

BACKGROUND Children receiving extracorporeal membrane oxygenation (ECMO) are at high risk for focal cerebral injury (FCI). There is emerging evidence that electroencephalography (EEG) may aid FCI detection. The EEG Correlate of Injury to the Nervous System (COIN) index quantifies and displays focal background asymmetries. We evaluated whether COIN is associated with FCI in pediatric ECMO. METHODS Retrospective, cross-sectional study of patients age 28 days to 21 years, on venoarterial ECMO at a tertiary children's hospital, who received EEG monitoring and neuroimaging during ECMO. COIN was calculated from all available EEG data. COIN of 0 implies a symmetric EEG and negative COIN values are observed with FCI. Median COIN values near FCI recognition time were compared to median COIN values from randomly selected control EEG batches using logistic regression. A receiver operator characteristic curve was used to identify multilevel FCI test ranges. Likelihood ratios were calculated to estimate the posttest FCI probability for each COIN range. RESULTS During the 8-year study period (2015-2023), 33 of 142 ECMO runs met study criteria for COIN analysis. Twelve patients (36%) had FCI. The COIN cutoff of -13.3 had 92% sensitivity and 67% specificity for FCI. The COIN cutoff of -27.7 had 67% sensitivity and 90% specificity. Likelihood ratios were 0.13 for COIN (0 to -13.3), 1.1 for COIN (-13.3 to -27.7), and 7.0 for COIN (< -27.7). Posttest probability was 0.02, 0.13, 0.49 in each respective range. CONCLUSION FCI on ECMO is associated with COIN-measured EEG asymmetry. COIN may support FCI risk-stratification during ECMO.

Background and Objectives: In persons with epilepsy, seizure risk is tightly linked to the health of sleep and circadian rhythms. Rest-activity rhythms (RARs), derived from continuously worn activity monitors, can provide objective assessments of diurnal patterns of activity. Compared with healthy controls, adults with epilepsy have been shown to display weak and unstable RARs. In this study, we aimed to directly measure RARs in patients with infantile epileptic spasms syndrome (IESS), a potentially devastating developmental and epileptic encephalopathy. As a comparator, we similarly examined identically measured RARs from a cohort of healthy infants. Methods: For this cross-sectional case-control comparison, we obtained multiday actograms in a sample of infants with IESS using ankle-worn Actiwatch-2 devices deployed during overnight follow-up EEG evaluations designed to assess initial treatment efficacy. Control actograms (similarly obtained via Actiwatch-2 devices) from the Rise & SHINE study (Sleep Health in Infancy and Early Childhood) were downloaded from the National Sleep Research Resource. We computed a series of parametric and non-parametric measures to depict the maturation of RARs over this developmental window and compared RARs from each IESS subject against up to 4 age-matched controls. Results: In 891 actigraphy recordings obtained from 333 SHINE subjects, age-dependent increases in body length and weight were associated with progressive increases in RAR height (amplitude/mesor/M10), regularity (interdaily stability), entropy and fractal complexity, together with progressive declines in RAR fragmentation (intradaily variability). Compared with age-matched controls, multiday actograms from IESS subjects (n = 11, 9 males) displayed marked reductions in RAR height (amplitude/mesor/M10) and interdaily stability, together with reductions in entropy and fractal complexity. Conclusions: During infancy, rest-activity rhythms display a stereotyped maturation in height, complexity and day to day consistency, revealing a developmental "growth curve" of RAR maturation. Severe RAR disruptions in infants with IESS may relate to the encephalopathy imposed by the underlying genetic/metabolic condition, structural lesion, and/or the psychomotor retardation imparted by antiseizure medications. Actigraphy recordings may offer a scalable, noninvasive approach to objectively and longitudinally assess circadian health in patients with IESS.

Interictal epileptiform discharges (IEDs) are diagnostically important EEG abnormalities observed between seizures. This study addresses a conditional spatial-classification task where every analyzed four-second epoch had already been reviewed and confirmed by experts as containing an IED, and the model assigned that epoch to one of five predefined scalp-distribution categories (generalized, frontal, temporal, occipital, or centro-parietal). The analysis therefore does not evaluate IED-versus-non-IED detection. After preprocessing, 2,514 IED-labelled epochs were analyzed using identical stratified epoch-level partitions, SMOTE based training, 26 handcrafted features per included channel, and multiple machine-learning classifiers. A staged channel ablation compared 19-channel scalp EEG, 21-channel EEG with ECG, and the complete 29-channel input containing scalp EEG, referential, ECG, and EMG channels. The best EEG-only result was obtained with linear discriminant analysis (88.89% test accuracy). CatBoost achieved 93.25% on EEG with ECG channel and 94.44% with the whole channel set. All eight directly comparable classifiers showed numerically higher test accuracy after ECG channel was added; for CatBoost, the increase was 6.35 percentage points. In the EEG with ECG channel, CatBoost model on ECG channel on right and left arm received respectively 15.79% and 15.12% of normalized global SHAP attribution, and beta-band power was the leading of all features (18.76%). These SHAP values indicate model-specific predictive contributions and do not establish physiological biomarkers, causal autonomic mechanisms, or clinical localization. The findings support a limited methodological conclusion which is ECG-derived features were associated with improved internal epoch-level categorization of expert-confirmed IED epochs. They do not establish IED detection, artifact rejection, independent EMG effects, or generalization to unseen patients.

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.

Temporal lobe epilepsy (TLE) is associated with dysregulation of transforming growth factor {beta} (TGF{beta}) signaling, a key contributor to epileptogenesis. SARA (Smad Anchor for Receptor Activation), a central regulator of this pathway, is controlled by the E3 ubiquitin ligase Smurf2 through ubiquitination. However, the role of the SARA-Smurf2 axis in regulating TGF{beta} signaling during TLE has not previously been described, and whether this pathway can be therapeutically targeted remains unknown. Using a pilocarpine-induced status epilepticus (SE) model and astrocytes derived from patients with refractory TLE, we identified dysregulation of the SARA-Smurf2 pathway in both experimental systems. In SE rats, SARA and Glial Fibrillary Acidic Protein (GFAP) levels were significantly increased, whereas Smurf2 induction was insufficient to prevent SARA accumulation. In TLE-derived astrocytes, increased SARA and GFAP immunoreactivity was accompanied by reduced Smurf2 immunoreactivity and altered Smurf2 subcellular distribution. Losartan treatment restored SARA and Smurf2 immunoreactivity toward a control-like pattern in both models and reduced seizure frequency and duration in SE animals. These findings point towards a dysregulation of the SARA-Smurf2 axis as a molecular signature of TLE, support SARA as a potential therapeutic target, providing experimental evidence for the repositioning of Losartan as a potential treatment alternative for drug-resistant epilepsy, warranting further translational and clinical investigation. KEY POINTSO_LIDysregulation of the SARA-Smurf2 axis is a molecular signature of experimental and human temporal lobe epilepsy. C_LIO_LIImpaired Smurf2-dependent regulation of SARA may sustain TGF{beta} signaling, astrocyte reactivity, and epileptogenesis. C_LIO_LILosartan restores the SARA-Smurf2 axis and reduces seizures, supporting a novel therapeutic strategy for TLE. C_LI

Down syndrome (DS) is the most common genetic cause of intellectual disability, yet age-related cortical maturation patterns that contribute to developmental delays remain poorly understood. We analyzed longitudinal resting-state EEG and developmental data from 86 children with DS and 154 typically developing (TD) children between 12 and 81 months of age. Linear mixed-effect models tested age-related trajectories of aperiodic and periodic spectral features, and manifold learning was used to characterize multivariate EEG profiles associated with age and developmental ability. Children with DS showed altered maturation across multiple EEG features. Aperiodic exponent decreased with age in DS, but not TD children, indicating possible altered maturation of cortical excitability. While TD children showed expected age-related increases in theta-alpha peak frequency and amplitude, children with DS exhibited limited alpha maturation and greater persistence of theta-only and theta+alpha peak profiles. We next asked whether multivariate EEG organization reflected chronological maturation or individual differences in developmental ability. A spectral dimension associated with chronological age in TD children was not similarly age-associated in DS. Instead, a second spectral dimension was associated with verbal developmental quotient in children with DS, independent of chronological age and nonverbal developmental ability. This language-associated profile included features considered atypical relative to TD maturation, including increased aperiodic activity and continued presence of theta activity. These findings suggest that in DS there is an altered relationship between cortical spectral organization, chronological age, and language development, extending beyond a uniform delay in typical maturation.

Physiological mirror activity (pMA) is the increase in involuntary muscle activity observed on the contralateral side during unilateral voluntary movement in neurologically healthy participants. This cross-sectional study aimed to explore the relationship between pMA and corticomuscular coherence (CMC) during finger dexterity tasks in young and older adults. Thirty-one right-handed young adults and 24 older adults performed a left-hand finger dexterity task. Electroencephalogram (EEG) signals were recorded from C3 and C4, and electromyogram (EMG) signals were collected from bilateral finger flexors and extensors. pMA was quantified as the change in right-hand EMG from rest to task. Gamma-band CMC was calculated from task-related EEG-EMG pairs, and its association with pMA was analyzed. In young adults, greater pMA was associated with lower CMC (C3- and C4-right flexors), whereas in older adults, greater pMA was associated with higher CMC (C3-left flexor). Young adults may suppress pMA emergence by appropriately monitoring and inhibiting activity, in the hand not performing the task. Conversely, in older adults, the mobilization of the ipsilateral motor cortex may have contributed to pMA emergence. This study suggests that the neuromuscular mechanisms involved in pMA during finger dexterity tasks differ between young and older adults.

Background: Hippocampal sclerosis (HS) is a common cause of drug-resistant focal epilepsy (DRFE) and amenable to neurosurgical treatment. Detection relies on MRI but can be challenging. 7 Tesla (T) ultra-high field MRI and automated MRI post-processing tools have independently been shown to improve radiological diagnosis of HS. However, combining these approaches remains underexplored. This study evaluated whether AID-HS, a tool for HS detection developed using 3T MRI, generalises to 7T MRI data. Methods: We collated a dataset of paired 3T and 7T T1-weighted MRI from four epilepsy centres, including 23 patients with HS, 39 healthy controls, and 23 individuals with focal cortical dysplasia as disease controls. Histopathology served as the gold standard for defining HS where available (n=7), otherwise radiological findings (n=16). AID-HS was applied to images acquired at both field strengths, and sensitivity and specificity for detection and lateralisation of HS were compared. Additionally, agreement of hippocampal features across 3T and 7T was evaluated. Results: We found no evidence of a difference in performance of AID-HS between 3T and 7T. Sensitivity for detection of unilateral HS was 63% (12/19) at 3T and 68% (13/19) at 7T (McNemar's exact test p=1.0). Specificity in controls was 97% (60/62) at 3T and 100% (62/62) at 7T (p=0.5). Bilateral HS was correctly flagged in 3 of 4 cases using feature-based criteria, with high specificity in controls. Quantitative hippocampal features showed moderate to good agreement across field strengths (ICC 0.70 to 0.98), with small differences observed for volume and thickness estimates. Conclusion: AID-HS provides robust detection and lateralisation of HS across multiple 7T MRI centres, highlighting its potential to enhance lesion detection. Future work is needed to investigate whether models trained on 7T data can leverage the improved image quality for further gains in HS detection performance.

Background: Low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) over the contralesional primary motor cortex is widely used for post-stroke upper-limb rehabilitation, but treatment response varies substantially. This systematic review and meta-analysis aimed to quantify the efficacy of contralesional LF-rTMS and to examine whether baseline motor impairment severity and corticospinal tract (CST) integrity modify treatment effects. Methods: We searched seven databases from inception to July 2026 for randomized controlled trials of contralesional LF-rTMS ([&le;]1 Hz) versus sham after stroke, with comparable rehabilitation in both arms. The primary outcome was the change in Fugl-Meyer Assessment for the upper extremity (FMA-UE) scores. Random-effects meta-analysis used restricted maximum likelihood estimation with Knapp-Hartung adjustment. Effect modification was examined through meta-regression and biomarker-stratified analyses, and neurophysiological outcomes were also synthesized. Results: Thirty trials (33 comparisons, 1,668 participants) were included. LF-rTMS produced greater FMA-UE improvement than sham (mean difference 4.11 points, 95% CI 2.83-5.39; Hedges g 0.64, 95% CI 0.45-0.84), with substantial heterogeneity. Baseline severity did not significantly modify the effect in continuous meta-regression. However, exploratory within-trial biomarker-stratified analyses suggested larger effects in participants with preserved CST integrity or positive motor-evoked potential (MEP) status. LF-rTMS also shortened MEP latency and central motor conduction time, but these measures could not be validated as surrogate endpoints. Conclusions: Contralesional LF-rTMS provides a statistically significant but modest improvement in post-stroke upper-limb motor recovery. Baseline clinical severity alone may not identify responders, whereas CST integrity is an exploratory, hypothesis-generating candidate biomarker. It requires confirmation in adequately powered biomarker-stratified trials before it can inform clinical decisions. Trial Registration The study was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD420261441561).

Cognitive impairment affects up to 80% of patients with drug resistant epilepsy (DRE), yet the basis for this impairment in patients with otherwise comparable disease characteristics remains poorly understood. Prior work has largely focused on identifying focal nodes responsible for cognitive decline, leaving the broader network reorganization associated with cognitive preservation poorly characterized. In this study, we hypothesized that the brain's capacity to reorganize its functional network hubs, rather than the degree of underlying pathology, distinguishes cognitively resilient from cognitively impaired patients. We studied a retrospective cohort of 105 DRE patients and 60 healthy controls who underwent resting-state functional neuroimaging. DRE patients were stratified into epilepsy cognitively neutral (ECN) and epilepsy cognitively impaired (ECI) subgroups based on comprehensive neuropsychological profiling spanning both domain-general and domain-specific levels. The subgroups did not differ in key disease characteristics including epilepsy duration, age of onset, seizure lateralization, and lesion status (p>0.05). We characterized hub organization across the whole brain, canonical functional networks and subcortical levels and summarized each subject's functional reorganization using the hub disruption index. We found that whole brain topology is preserved in both groups whereas disruption concentrates in the salience network and dissociates within subcortical structures with reduced hippocampal node strength in both groups and increased thalamic node strength, with the latter more pronounced with cognitive burden. Inter-network connectivity shifted from focal, selective up-regulation in ECN to diffuse hyperconnectivity in ECI. Critically, the hub disruption index (HDI) for centrality separated the groups where the ECN group showed the greatest redistribution of centrality from canonical hubs towards alternative relay regions whereas ECI demonstrated comparatively little reorganization (ECN vs ECI: d=0.52, p=0.029; Bonferroni corrected). The same pattern held within individual domains, with greater hub reorganization in patients whose language and memory function was preserved. These cross-sectional findings link cognitive impairment in epilepsy to a reduced capacity for adaptive hub reorganization rather than to pathology alone. Because the HDI for centrality is computable at the individual level, it may offer an objective imaging biomarker to complement neuropsychological testing, aid identification of patients at risk for cognitive decline, and inform prognostic counseling and surgical planning in DRE.

Objective: This computational modeling study quantified the influence of sex and race-related cranial anatomy on predicted brain-wide current flow during electroconvulsive therapy (ECT) across conventional (bifrontal (BF), bitemporal/bilateral (BL), right unilateral (RUL)) and experimental (focal electrically administered seizure therapy (FEAST) and frontomedial (FM)) electrode montages. The objective was to determine whether race-associated variability meaningfully contributes to differences in ECT stimulation metrics across montages. Methods: Finite element head models of Chinese, Black, and Caucasian subjects were developed using high-resolution magnetic resonance imaging and analyzed using the Realistic vOlumetric- Approach-based Stimulator for Transcranial electric stimulation (ROAST) pipeline (N = 150 total; n = 50 per cohort, comprising 25 M and 25 F, age range: 20-30 years). Five ECT montages were simulated under a constant-current condition (900mA). Stimulation strength (Ebrain/Eth) was quantified as 90th percentile of brain-wide E-field magnitude (Ebrain) relative to neuronal activation threshold (Eth = 0.25 V/cm) quantified stimulation strength. Overall focality was evaluated as a percentage of brain volume stimulated above the neural activation threshold (Ebrain [&ge;] Eth), while laterality was quantified as the median right-to-left hemispheric E-field magnitude ratio. The effects of race, sex, and montage on stimulation strength, focality, and hemispheric laterality were statistically analyzed. Results: Substantial race- and sex-related differences observed in cranial anatomy resulted in systematic variation in predicted ECT-induced E-field intensity. Brain-wide E-field magnitude varied by both race and montage, with the largest fields generally observed in Caucasian head models and during BL stimulation. Montage exerted the strongest effect on stimulation strength (Ebrain/Eth) with BL and FEAST producing the highest stimulation strengths, followed by RUL and FM, while BF produced the lowest. Caucasian subjects generally predicted higher stimulation strengths than Black and Chinese subjects, whereas females predicted modestly higher stimulation strengths than males. Laterality was primarily determined by montage, with FEAST producing the greatest hemispheric asymmetry, followed by RUL. Chinese subjects demonstrated higher laterality ratios than both Black and Caucasian subjects. BL, RUL, and FEAST stimulated substantially larger brain volumes above neural activation threshold (less focal stimulation) than BF. Lower focality was observed in Caucasian subjects relative to Black and Chinese subjects, and in females relative to males. Conclusions: Electrode montage was the primary determinant of predicted ECT stimulation strength, focality, and laterality. Race-related anatomical differences and, to a lesser extent, sex-related differences systematically altered stimulation patterns, supporting consideration of individualized anatomy in ECT dosing and treatment optimization.

Background: On-call clinicians frequently report the anecdotal impression of 'theme shifts' during which specific acute neurological diagnoses appear to cluster. Whether such clustering reflects a statistically true and reproducible phenomenon has not been systematically investigated; the present paper examines seasonality and temporal clustering within six different acute neurological conditions. Methods: In this retrospective, single-center cohort study, we identified all patients admitted to a tertiary neurological department between July 2016 and June 2026 with acute unilateral vestibulopathy, cerebral artery dissection, generalized epileptic seizures, primary intracerebral hemorrhage, peripheral facial nerve palsy, or transient global amnesia (TGA) (n = 2,140). Monthly and seasonal distributions were assessed using chi-squared goodness-of-fit and cosinor analysis. Short-term temporal clustering was tested by Monte Carlo permutation across time windows from 24 hours to 90 days, and endogenous cluster dynamics were characterized using Hawkes self-exciting point process modeling. Results: Admissions for generalized epileptic seizures showed a statistically significant deviation from a uniform monthly distribution with a winter distribution (p<0.001 and q = 0.002), and a significant temporal clustering across time windows from 72 hours to 90 days (all q < 0.05). Peripheral facial nerve palsy presented significant clustering at the 90-day window (q = 0.029) and TGA at 60-day time window (q = 0.041) without seasonality; the diagnostic groups of acute unilateral vestibulopathy, cerebral artery dissection and primary intracerebral hemorrhage showed neither seasonality nor clustering after correction for multiple comparison. No diagnostic group showed clustering within a 24-hour window, statistically significant self-excitation in Hawkes process modelling, or a significant linear trend in monthly case counts over the study period. Conclusion: The anecdotal impression of diagnostic 'theme shifts' among on-call neurologists appears to have a measurable basis, although clustering is confined to specific conditions and rather on a time scale of weeks to months. Generalized epileptic seizures were the only diagnostic group that uniquely combined seasonality with temporal clustering, suggesting a shared trigger, while facial palsy and TGA showed episodic, yet non-seasonal clustering.

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.

BackgroundFreezing of gait (FOG) in Parkinsons disease is associated with abnormal beta activity in the subthalamic nucleus (STN), but the temporal structure of burst dynamics remains poorly understood. ObjectivesTo determine whether temporal features of STN beta bursts distinguish pre-freeze from stable gait and predict freezing onset. MethodsSTN recordings during gait from four individuals were analyzed. Temporal features of burst timing, including entropy and variability, were computed across behavioral states. Predictive performance was assessed using leave-one-patient-out classifiers. ResultsEntropy of inter-burst intervals was reduced prior to freezing (p < 0.01), with strong predictive performance (AUC = 0.825; threshold AUC = 0.858). During freezing, variability measures decreased and temporal structure increased, while entropy did not differ from pre-freeze. Phase-amplitude coupling showed frequency-specific but heterogeneous effects across comparisons. ConclusionsReduced temporal variability of STN beta burst timing precedes and predicts freezing, suggesting a transition to constrained neural dynamics.