Clinical Neurophysiology

Background and Objectives Interictal spikes on intraoperative electrocorticography have been used to localize the epileptogenic zone but have limited specificity. We hypothesized that identifying spatially distributed, spike-associated very high-frequency activity (vHFA; 500-2,000 Hz) would improve the ability of spikes to localize the epileptogenic zone. We aimed to characterize spike-associated vHFA and to determine whether a seizure outcome prediction model incorporating vHFA could be externally validated across independent centers. Methods This retrospective two-center cohort study included patients with drug-resistant focal epilepsy who underwent electrocorticography and subsequent resection between January 2016 and May 2025. Spikes occurring across contacts within 50 ms were classified as co-occurring; all others were classified as isolated. We determined whether vHFA was associated with co-occurring spikes. We then assessed whether contacts enriched in vHFA-positive co-occurring spikes were more likely to be located within the resected region among seizure-free patients. Finally, we determined whether the rate of such spikes remaining outside the resection predicted seizure outcomes across the two centers. Results Among 56 patients (mean age: 21.2 years; 26 females), we identified 132,359 spikes, of which 1,925 were vHFA-positive. The vHFA peak preceded the associated spike peak by 8.0 ms (95%CI: 5.0-16.0). vHFA-positive spikes were more likely to be classified as co-occurring, even after adjustment for anatomy and inter-electrode distance (OR: 3.43; 95%CI: 2.82-4.17). Among the 35 seizure-free patients, contacts with higher rates of vHFA-positive co-occurring spikes were more likely to be resected (OR: 11.92 per occurrence/min; 95%CI: 5.62-25.32). This association was stronger than that observed for all spikes (OR: 1.09 per occurrence/min; 95%CI: 1.06-1.11). Twenty patients experienced postoperative seizure recurrence; a higher proportion of vHFA-positive co-occurring spikes in preserved areas was associated with recurrence (OR: 3.22 per 0.01 increase in proportion; 95%CI: 1.14-9.12). In bidirectional cross-center validation, models incorporating vHFA-positive co-occurring spikes yielded areas under the receiver operating characteristic curves of 0.80 and 0.69, with better performance when the model was developed using the larger sample. Discussion Spike-associated vHFA may identify a biologically meaningful subset of interictal spikes involved in propagation across the epileptogenic zone. vHFA-positive co-occurring spikes may represent a generalizable intraoperative biomarker of the epileptogenic zone.

Objective: We developed a patient-specific framework to determine which information about interictal epileptiform discharges (IEDs) can be recovered from scalp EEG, separating temporal localization (where) from event-specific intracranial waveform reconstruction (what). Methods: Six subjects from a public simultaneous high-density scalp EEG and intracranial EEG (iEEG) dataset were analyzed using patient-specific dual-branch convolutional-attention models integrating waveform/topographic and time-frequency representations. For the where task, a 640-ms scalp EEG segment was divided into eight 80-ms bins, and the model predicted the bin containing the intracranial IED peak. For the what task, a 250-ms peak-centered iEEG waveform was represented as a fixed patient-specific template plus an event-specific residual predicted from scalp EEG. Direct scalp-input controls and event-clustered bootstrap confidence intervals were used to distinguish scalp-dependent information from template-driven performance. Results: Where accuracy varied markedly across subjects (0.080-0.984), with an unweighted subject-level mean of 0.333 versus a chance level of 0.125; 95% confidence intervals were above chance level in three of six subjects. Input-control analyses supported a scalp-dependent contribution in subjects with successful localization. For the what task, mean waveform correlation was 0.752 for the model and 0.754 for the fixed patient-specific template (mean difference, -0.002), while residual correlations were generally modest (-0.020 to 0.378). Conclusion: Scalp EEG contained recoverable temporal information in some subjects, but event-specific single-contact morphology was not consistently recovered beyond the patient-specific template. Separating temporal localization from template-based waveform reconstruction provides a more stringent framework for evaluating noninvasive-to-intracranial inference and reduces conflation of prior-driven similarity with input-dependent reconstruction.

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

ObjectiveThe current International League Against Epilepsy (ILAE) guidelines for Lennox-Gastaut syndrome (LGS) mandate the presence of generalized paroxysmal fast activity (GPFA) on EEG. However, GPFA is not necessarily restricted to LGS and has been described in, among others, idiopathic generalized epilepsies. A clear definition for GPFA is currently lacking, hampering research into its utility as a diagnostic or treatment biomarker. Here, we assessed expert agreement in the interpretation of GPFA to explore feasibility of a consensus-based definition. MethodsWe internationally distributed a two-part survey on GPFA to EEG experts. First, participants completed thirteen questions gauging their preferred clinical and EEG criteria for GPFA, including duration, frequency, amplitude and topography. Second, participants classified the presence versus absence of GPFA in eleven EEG clips, while blinded to clinical context, from patients with LGS, other epilepsies, and physiological variants. Results162 respondents, predominantly from North America and Europe, completed the survey. 87% considered GPFA to also occur in epilepsies other than LGS. Considerable heterogeneity was present on defining duration, spectral frequency, amplitude and topographic distribution of GPFA. Particularly poor agreement was seen for lower limits of duration and frequency, with no single response option receiving a majority consensus (36% and 30% agreement, respectively). Similarly, the degree of agreement on interpretations was highly variable across real-world EEG clips. SignificanceMarked discrepancies exist in the EEG identification and clinical interpretation of GPFA, undermining its utility as a potential biomarker in research and clinical trials of LGS and other epilepsies. The findings also highlight the need to establish a consensus-based and eventually evidence-informed definition to formally operationalize the clinical and EEG criteria for GPFA. Key pointsO_LIGeneralized paroxysmal fast activity (GPFA) is variably defined in the literature, limiting its utility as a diagnostic or treatment biomarker C_LIO_LIIn this international survey (n = 162 respondents, 25 countries) we found limited agreement on GPFAs defining features and on interpretation of fast activity on EEG clips C_LIO_LI87% of respondents considered GPFA to occur in epilepsies other than Lennox-Gastaut syndrome C_LIO_LIPending further elucidation of the pathophysiology underlying GPFA or its computational characterization (i.e., to inform a biologically-or evidence-based definition), we underscore the need for an interim consensus-based definition 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.

Based on the observation that in normal EEG rhythms, the low-voltage beta frequencies dominate in the anterior region while characteristic high(er)-amplitude slower rhythms manifest more posteriorly, we studied these gradients across normal subjects and patients with different monogenic epilepsies. From large data sets (long term records of 374 controls and 241 patients) we selected 60s eyes-open rest-EEG epochs to determine the spectral power of the EEG bands. To help manage the large data sets and facilitate analyses, we employed AI to preselect and assist in coding. While both preselection and coding was extensively verified and validated by the authors, which was a time-consuming effort, the AI contribution reduced the net effort of evaluating the large dataset. We developed a metric that reflects the anterior-posterior gradients of the beta rhythm and the so-called posterior dominant rhythm. Our results indicate that this simple gradient measure across 1-10 year-age groups may provide a robust quantitative biomarker of cerebral dysfunction in monogenic epilepsy. Because EEG is noninvasive, widely available, repeatable, and relatively inexpensive, such a biomarker could ultimately provide a practical means of assessing the cerebral effects of emerging targeted therapies across multiple genetic epilepsies.

Objective: Cortico-cortical evoked potentials (CCEPs) are used during intracranial EEG (iEEG) monitoring to probe effective connectivity and guide epilepsy surgery. However, the factors governing spatial distribution and variability of stimulation-evoked responses across patients remain incompletely understood, making it difficult to distinguish physiological variation from pathological responses. We aimed to develop interpretable models of CCEP responses that integrate normative and patient-specific features, and to assess how these relationships are influenced by distance, brain-state, and epileptogenicity. Methods: We analyzed single-pulse stimulation data from 34 patients with drug-resistant epilepsy undergoing stereo-EEG monitoring. Significant CCEP responses were defined using N1 peak amplitude. For each stimulation-response pair, we derived multimodal features capturing Euclidean distance, effective connectivity priors from F-TRACT, a published population atlas of CCEP response probabilities, normative structural connectivity and communication metrics, and patient-specific functional connectivity estimated from iEEG during baseline and pre-stimulation periods. Lasso-regularized logistic regression models were trained to predict response using leave-one-patient-out cross-validation, and feature importance was examined across spatial scales and seizure onset zone (SOZ) involvement. Results: Performance to predict stimulation-evoked responses improved with the integration of multiple feature domains, with the full model achieving the highest accuracy (median AUC = 0.84). Feature relevance varied with spatial scale: short-range responses were dominated by distance and normative priors, whereas long-range responses depended strongly on patient-specific functional connectivity, especially when measured from pre-stimulation periods. Prediction errors were significantly higher for stimulation-response pairs within the SOZ, indicating deviations from normative connectivity-response relationships in epileptogenic networks. Significance: CCEP responses reflect an interaction between spatial constraints, network architecture, and patient-specific brain state. Multimodal modeling of CCEPs provides a principled framework to interpret stimulation responses and may offer complementary markers for identifying epileptogenic networks during presurgical evaluation.

Background: Immediate transcranial evoked potentials (iTEPs) are ultra-early electroencephalographic responses elicited by single-pulse transcranial magnetic stimulation (TMS) of the primary motor cortical hand area (M1-HAND). Their characteristic latency, waveform, and sensitivity to short-interval intracortical inhibition suggest a close relationship to the cortical generators of indirect corticospinal volleys (I-waves). Here, we tested the hypothesis that iTEPs constitute a fundamental physiological response of human motor cortex and therefore generalize across motor representations, including the leg representation (M1-LEG). Methods: Single-pulse TMS was delivered over the motor cortical representation of the right tibialis anterior (TA) in 16 healthy volunteers. In a subset of participants (n=10), stimulus-response curves and paired-pulse short-interval intracortical inhibition (SICI) measures were obtained. Results: TMS of M1-LEG elicited reproducible iTEPs consisting of a slow positive deflection with superimposed high-frequency peaks with topographic maxima in electrodes adjacent to the stimulation site. M1-LEG iTEPs emerged close to active motor threshold and increased in prevalence and amplitude with increasing stimulation intensity. Paired-pulse stimulation produced consistent SICI of the second and third iTEP peaks. M1-LEG stimulation also evoked conventional later TEPs with latencies and polarities closely resembling those recorded after M1-HAND stimulation. Conclusions: TMS of the human M1-LEG elicits robust iTEPs that closely resemble those recorded from M1-HAND in their timing, intensity dependence, and sensitivity to intracortical inhibition. These findings indicate that iTEPs are not specific to M1-HAND but reflect a general physiological response of human motor cortex closely linked to corticospinal I-wave generation. This broadens their utility as a non-invasive probe of excitatory and inhibitory circuit dynamics throughout the human motor system.

Activity in the gamma frequency range (>30Hz) serves as a neural hallmark of stimulus encoding across multiple sensory modalities. Based on its distinct properties, this activity could be classified as broadband or narrowband oscillations. Visual stimuli, such as a grating, induce narrowband oscillations, which have shown promise as diagnostic markers for neurological disorders. However, such tasks are discomforting for the subjects. These can be made subject-friendly by swapping the stimulus for an auditory one; however, most auditory stimuli elicit a broadband activity. In search of an ideal candidate to induce auditory narrowband oscillations, we previously recorded responses to auditory gratings in electroencephalography (EEG) recordings (Gulati & Ray, 2024) using a passive listening paradigm. We observed a focal sustained broadband increase in high gamma activity (70-150Hz), which was not selective towards any stimulus attributes. Nevertheless, since EEG represents activity of a large population, it remained unclear whether observed activity represented a broadband increase or comprised multiple narrowband rhythms, and whether it was indeed untuned. To elucidate this, we recorded invasively from the regions on and around the superior temporal gyrus, closer to Heschl's gyrus of seven patients undergoing epilepsy resection surgery. Patients were either under anesthesia or fully awake while we presented auditory gratings through loudspeakers. We found a transient increase in broadband gamma activity (30-100Hz) followed by a sustained suppression of beta rhythm (14-28Hz). This broadband gamma was selective for slower spectro-temporal modulations. Our result compares the responses obtained across different recording scales and corroborates that auditory gratings produce broadband but not narrowband gamma activity.

Aims & MethodsTranscranial magnetic stimulation (TMS) is a well-established tool for inducing cortical excitation. However, the relevance of current direction on elicited effects is still incompletely understood. Combining TMS with electroencephalography (EEG) and electromyography (EMG) enables non-invasive analysis of evoked potentials both on cortical and peripheral level. In 23 healthy subjects, EEG and EMG responses to biphasic single pulses applied over the left motor cortex with anterior-posterior to posterior-anterior (AP-PA) or PA-AP current direction and 110% resting motor threshold (RMT) intensity were recorded and contrasted between the alternating phases. A cobot-assissted neuronavigation ensured stable coil-placement during the procedure. ResultsRMT was lower and EMG latency was shorter for AP-PA currents compared to PA-AP currents, whereas the EMG amplitude did not differ. For EEG responses, local and global evoked activity was higher for mid-components with PA-AP currents. P60 occurred earlier with PA-AP currents and N100 amplitude was higher in amplitude with AP-PA currents. The trial-wise MEP amplitude correlated significantly with P30 in the AP-PA and for both current directions with the N100 amplitude. ConclusionOur results highlight the directional sensitivity of M1 and the importance of further exploring the role of current direction in TMS protocols to better understand the cortical processes underlying cortico-cortical and cortico-spinal responses.

Epileptic seizures are accompanied by profound alterations in autonomic regulation, yet the physiological information encoded in cardiac dynamics remains incompletely understood. Bayesian heart-rate (HR) entropy has recently been proposed as a probabilistic measure of cardiac dynamics, but whether it captures clinically meaningful aspects of seizure physiology--such as behavioral awareness or seizure evolution--has not been established. We estimated Bayesian HR entropy from electrocardiographic recordings acquired during video-electroencephalographic monitoring using the BayesianAtHeart framework. Entropy-derived measures were integrated with quality-controlled clinical metadata to generate a frozen seizure-level analysis dataset, from which all subsequent analyses were performed. Associations between seizure-average Bayesian HR entropy and clinical variables were evaluated using linear mixed-effects models accounting for repeated seizures within patients, and time-resolved entropy trajectories were analyzed descriptively. Following ECG quality control, Bayesian HR entropy was successfully estimated for 51 of 67 seizures from 10 patients; the remaining 16 seizures were excluded because ECG quality was insufficient. Forty-eight seizures with complete awareness classification comprised the primary analysis cohort. Bayesian HR entropy was not associated with ictal awareness across seizure-average analyses, mixed-effects models, or time-resolved entropy trajectories. Instead, seizure duration emerged as the strongest clinical correlate of Bayesian HR entropy, with longer seizures exhibiting progressively lower Bayesian HR entropy. Time-resolved analyses indicated that this association reflected a gradual decline in entropy during seizure evolution rather than lower Bayesian HR entropy at seizure onset. Post hoc sensitivity analyses showed that this association was not attributable to selection bias or to the number of beat-to-beat intervals available to the entropy estimator, and that duration, rather than stable between-patient differences, was the dominant source of entropy variance. Entropy was not generally reduced during seizures relative to a pre-ictal baseline; instead, the variability of the entropy trajectory declined progressively with seizure duration, more strongly than its mean. These findings suggest that Bayesian HR entropy primarily reflects the evolving organization of autonomic regulation during seizures rather than behavioral awareness. Beyond identifying seizure duration as the strongest correlate of Bayesian HR entropy in this cohort, this study establishes a fully reproducible computational framework for Bayesian HR entropy analysis that provides a foundation for future prospective investigations of autonomic dynamics in epilepsy.

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.

Objective. Temporal lobe epilepsy (TLE) is associated with disrupted functional integrity in the amygdala-hippocampus complex. Cortico-cortical evoked potentials (CCEPs) can characterize this disruption and have been proposed as biomarkers of the epileptogenic zone (EZ), but their study is typically limited by the spatial sampling bias inherent to whole-brain intracranial EEG. We investigated how epileptogenicity shapes effective connectivity in the amygdala-hippocampus complex, whether structural connectivity underlies it, and ultimately derived a multimodal EZ biomarker. Methods. We retrospectively included 71 patients (50 adults, 21 children) who underwent single-pulse electrical stimulation protocols with intracranial contacts in the amygdala or hippocampus; 15 also underwent diffusion MRI. CCEPs were visually detected, and the latency and amplitude of the first response peak (D1) were extracted. Structural connectivity metrics (tract length, quantitative and fractional anisotropy, mean diffusivity) were derived between the same contacts. A Bayesian linear mixed model (BLMM) related D1 latency to clinical, neurophysiological, and structural predictors, handling missing DTI values jointly within the model. A corrected latency score was then built to discriminate epileptogenic from non-epileptogenic contacts. Results. Among 6257 possible stimulation-recording pairs, 1027 CCEPs were detected with a significantly higher rate in the hippocampus compared to the amygdala. The BLMM identified robust associations between D1 latency and epileptogenicity, epilepsy type, ipsilateral stimulation, stimulation site (hippocampus/amygdala), and quantitative and fractional anisotropy. The resulting EZ score, obtained by extracting the EZ term's contribution from the BLMM equation, demonstrated an ability to discriminate epileptogenic contacts, showing a balanced accuracy of 78% (sensitivity 86%, specificity 71%), and the resulting EZ probability, based on an elastic net logistic regression, showed a balanced accuracy of 84% (sensitivity 86%, specificity 82%). Discussion. These findings suggest that effective connectivity results from the interplay of opposing physiological (here amygdala vs. hippocampus) and pathological (epilepsy-related) influences rather than a simple facilitation within the EZ, and that white matter microstructure independently contributes to this timing. Connectivity is slower within the EZ itself, with an even greater delay observed in its vicinity compared to other brain areas. The resulting EZ score offers a practical, closed-form tool to strengthen EZ localization, and paves the way toward a structurally informed, CCEP-based framework extendable to other brain regions.

Background: Analysis of SPES responses often relies on averaging repeated stimulation trials to improve signal quality. However, this may obscure clinically relevant trial-to-trial variation. We tested whether explicitly modelling cross-trial dependencies improves localisation of the epileptogenic zone, using concordance with the clinical SOZ as a proxy endpoint, and explored whether resection of model-positive channels is associated with postsurgical seizure freedom. Methods: We developed an interleaved Hierarchical Attention Transformer (HAT) that models cross-trial and cross-channel dependencies in multi-trial SPES responses without averaging. We compared the HAT with two baselines that average either responses or trial embeddings. Models were evaluated with patient-held-out, repeated five-fold cross-validation on SPES data from 35 patients. Robustness to reduced trial availability at inference was assessed by restricting test inputs to 1 or 5 trials. Associations with surgical outcome were assessed using AUROC and patient-level tests on the proportion of model-positive channels resected. Results: The HAT had higher SOZ concordance than the trial-averaged baseline (AUROC 0.762 vs 0.721; mean paired difference 0.041; one-sided 95% lower confidence bound 0.009; Holm-adjusted p = 0.0197). Performance changed little when inference was restricted to 1 trial. Outcome analyses did not provide statistical evidence that seizure-free patients had a higher proportion of model-positive channels resected (AUROC 0.634; p = 0.102). Conclusions: Modelling cross-trial dependencies improved concordance with the SOZ compared with trial-averaged approaches, while remaining robust to reduced trial availability at inference. Associations with postsurgical outcome were inconclusive, consistent with limited sample size and training on SOZ labels rather than outcome-aligned labels.

Background: Aberrant basal ganglia activity is a key feature of the pathophysiology of dystonia, but evidence is predominantly based on adult cohorts. Neurophysiological signatures during development remain poorly characterised. Local field potentials (LFPs) recorded by bidirectional neurostimulators provide a direct measure of globus pallidus internus (GPi) activity. In a cross-sectional paediatric cohort, we investigated whether pallidal oscillatory, aperiodic and connectivity characteristics are associated with dystonia severity and whether these differ between genetic and acquired dystonias. Methods: We recruited 30 children and young adults (5-20 years) with medically refractory dystonia (genetic n = 10; acquired n = 20) who underwent bilateral GPi Deep Brain Stimulation (DBS) implantation. Ten minutes of pallidal LFPs were recorded using Medtronic Percept RC neurostimulators with stimulation off, before DBS activation and, where available, at 12 months. Spectral power, aperiodic exponent and interhemispheric pallidopallidal coherence were quantified across delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and low-gamma (30-45 Hz) frequency bands. Linear mixed-effects models were implemented to establish whether LFP markers were predictive of dystonia severity, as measured by the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS-Movement). Firths Bias-Reduced Logistic Regression assessed whether LFP markers were predictive of dystonia aetiology. Results: Spectral peaks were most consistently detected in the delta, beta, and low-gamma bands. Delta and low-gamma peak power significantly predicted dystonia severity. This association was independent of medication burden, structural GPi involvement, and dystonia aetiology. Theta and alpha peaks were detected less consistently, and their power was not associated with dystonia severity. Pallidopallidal coherence significantly predicted dystonia severity in interaction with medication load in all frequency bands. Low-gamma peak power was significantly lower in acquired than genetic dystonia, but the potentially confounding influence of dystonia severity could not be excluded. The aperiodic exponent was not associated with severity or aetiology. Conclusion: Pallidal LFP markers are associated with dystonia severity in children, with delta peak power, low-gamma peak power and pallidopallidal coherence reflecting clinically relevant features of disease expression. These findings highlight the potential of pallidal LFPs to characterise dystonia across the lifespan and motivate longitudinal studies of their developmental and clinical trajectories.

Background: Working memory, the cognitive ability to maintain and manipulate a finite amount of information, is fundamental in reasoning, learning, and decision-making. Working memory erodes in conditions from Alzheimer's disease to stroke to healthy aging. Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising neuromodulation method for improving working memory. By stimulating the auricular branch of the vagus nerve, taVNS is thought to engage ascending arousal systems such as the locus coeruleus-norepinephrine (LC-NE) system. However, the physiological link between LC-NE system modulation via taVNS and improved working memory remains largely unknown. This mechanistic gap limits the rational optimization of the stimulation protocol and the identification of the cognitive functions and patient populations that are likely to benefit. Objective: We test two competing hypotheses for how taVNS may improve working memory: sensory amplification or enhancement of cortical efficiency. The sensory-amplification hypothesis predicts increased cortical responses to stimuli, whereas the cortical-efficiency hypothesis predicts reduced cortical responses. Methods: Twenty healthy adults completed N-back working memory tasks during three counterbalanced sessions: baseline, active vibrotactile taVNS (vtaVNS), and sham stimulation, while we simultaneously recorded electroencephalography, electrocardiography, eye-tracking, and behavioral responses. To study the effect of vtaVNS on neuronal activity, we also recruited 20 patients with epilepsy undergoing invasive monitoring. Of these, 19 fully completed the spatial working-memory task. We quantified cortical responses to visual stimuli using event-related potentials (ERPs) in scalp EEG and high-gamma activity in intracranial local field potentials. To examine prediction of the cortical-efficiency hypothesis, we further explored the effect of vtaVNS on distance to criticality and heartbeat-evoked potentials. Results: Active vtaVNS reduced both scalp-recorded visual ERP and high-gamma response in temporal cortices, relative to sham stimulation. Moreover, smaller visual event-related potentials were associated with successful encoding during the most demanding working-memory condition (i.e., 4-back task). Exploratory analyses showed that vtaVNS shifted cortical dynamics closer to criticality. Conclusion: Our results converge on the conclusion that vtaVNS does not uniformly amplify cortical responses to external stimuli. Instead, vtaVNS enhances cortical efficiency, supporting more effective allocation of cognitive resources between exteroceptive and mnemonic processes. These findings provide a framework for predicting when taVNS will be most beneficial and for optimizing stimulation protocols across cognitive and clinical applications.

Objectives: Transcranial magnetic stimulation (TMS) is widely used to probe corticospinal excitability by eliciting motor evoked potential (MEP)s in targeted muscles, with MEP characteristics such as magnitude and latency reflecting the physiological state of the pathways being stimulated. Although numerous studies have examined MEP reliability in upper extremity muscles, less is known about the reliability of this measurement across the lower extremity. We hypothesized that inter-session, test-retest reliability of MEPs recorded simultaneously from multiple lower-limb muscles, from a single TMS location, would differ by muscle, stimulation intensity, and quantification method. Materials and Methods: Ten healthy participants (5 males, 5 females) completed three TMS sessions separated by atleast one week. At each session, the stimulation hotspot was identified using a five-location virtual grid anchored at the vertex, with electromyography (EMG) recorded from all eight muscles of interest at each grid location; the grid location producing the largest and most consistent MEPs in the tibialis anterior (TA), the primary target muscle, was selected as the stimulation site and held constant across all three sessions. MEPs were then recorded bilaterally from the TA, soleus, rectus femoris, and biceps femoris muscles at two stimulation intensities (110% and 120% resting motor threshold (RMT)). MEP size was quantified using mean rectified magnitude and peak-to-peak amplitude, and inter-session reliability was assessed using intraclass correlation coefficients (ICC). Bland-Altman analysis was used to characterize the range of measurement variability across all eight muscles. Results: MEP size differed across sessions, and reliability varied by muscle, intensity, and quantification method. The highest reliability was observed in the right TA, the muscle used to establish the stimulation hotspot, using mean rectified magnitude at 120% RMT. Reliability was comparatively lower in the seven non-target muscles recorded from the same fixed stimulation site, indicating that MEP consistency was not uniform across the lower-limb musculature. Conclusions: MEP reliability in the lower extremity depends heavily on the muscle, stimulation intensity, and quantification method used, and is highest in the muscle for which the stimulation site was optimized. These findings support the interpretation that coil positioning targeted to a specific muscle yields more consistent responses in that muscle than in others recorded from the same fixed site, and underscore the importance of careful muscle selection and hotspot optimization when designing TMS protocols for longitudinal or clinical lower-limb research.

The inter-trial interval (ITI) of transcranial magnetic stimulation (TMS) can modulate motor-evoked potential (MEP) amplitude and may thus confound assessments of corticospinal excitability. We tested whether this effect depends on the ITI duration per se or the associated temporal predictability and whether intracortical inhibitory processes mediate these effects on. In 26 healthy participants, we recorded single-pulse MEPs and paired-pulse measures of short-interval intracortical inhibition (SICI; 2-ms interstimulus interval) and long-interval intracortical inhibition (LICI; 100-ms interstimulus interval) at ITIs of 2, 5, and 10 s. ITIs were presented in different Patterns, i.e., separate fixed blocks (FXD), randomly intermingled within blocks (RND), or randomly intermingled but auditorily cued at 950 ms pre-TMS (RNDC). MEPs showed significant effects of ITI, Pattern, and their interaction, being smaller at 2 s than at longer ITIs in FXD but not in RND and RNDC. However, RNDC produced marked MEP suppression across all ITI. SICI showed a Pattern x ITI interaction but no main effect of Pattern or between-pattern differences per ITI, whereas LICI showed complex Pattern x ITI interaction and both main effects. Notably, ITI affected SICI and LICI oppositely. While GABA-A-receptor mediated inhibition (SICI) showed an ITI dependency compatible with the ITI effects on single-pulse MEPs, it was unlikely mediating the predictability effects but may rather relate to an independent ITI driven suppression. In contrast, GABA-B-receptor mediated inhibition (LICI), was clearly modulated by predictability, but incompatible with the observed ITI effects on MEP amplitude. In summary, the effect of ITI duration on MEP amplitude seems to be largely explained by anticipatory suppression of corticospinal excitability based on the predictability of TMS pulses as derived from their temporal context, being easier for shorter and more regular ITIs and maximal for explicit cues immediately preceding the TMS pulse. SICI and LICI are modulated by ITI as well, with the latter affected by predictability, but neither can explain the observed predictability effects on single-pulse MEP amplitudes.

ObjectivesStroke commonly impairs proprioception and motor function, yet the cortical sensory processes underlying these impairments remain poorly understood. Prior electrophysiological studies have primarily focused on the average magnitude of unilateral cortical sensory responses to vibration, potentially overlooking distributed and trial-to-trial features of sensory processing that may be functionally relevant to proprioceptive processing and motor performance. We therefore aimed to characterize bilateral cortical sensory responses to determine their relationships with proprioceptive and motor function. MethodsEEG was recorded from forty-six individuals with chronic stroke during a rapid, passive, vibrotactile stimulation paradigm applied to the left and right fingertips. Somatosensory evoked potentials (SEPs) and event-related desynchronization (ERD) were quantified. Finger proprioceptive performance was assessed using a passive, robotic, finger crossing identification task, while motor function was evaluated using the Box and Block Test, Fugl-Meyer Assessment, and Nine Hole Peg Test. Associations with function were assessed using (i) unilateral sensory response magnitude at the contralateral parietal cortex and (ii) somatosensory decoder performance, defined as the accuracy with which a decoder identified the location of the stimulated hand (i.e. paretic vs. non-paretic) from combined bihemispheric response patterns. The association between these responses and proprioceptive ability and motor function was assessed. These associations were further evaluated jointly across multiple motor function measures using an exploratory analysis leveraging nonlinear dimensionality reduction and clustering. ResultsVibrotactile stimulation of the paretic hand elicited ipsilesional SEP and ERD that were reduced in magnitude compared to stimulation of the non-paretic hand. Both decreased SEP magnitude and reduced sensory decoder performance were associated with greater finger proprioceptive error. Unlike unilateral responses, the somatosensory decoders performance was also strongly associated with motor function, explaining approximately 22.5% of the variance in motor performance. Dimensionality reduction and clustering across multiple motor assessment scores showed distinct subgroups, that showed significant differences in sensory decoding. ConclusionsThe hemispheric distribution and discriminability of cortical sensory responses are functionally relevant markers of sensorimotor integrity after stroke. Assessing relative lateralization of somatosensory responses for each hand, rather than the magnitude of dominant contralateral responses alone, may better capture the reliability of sensory processing after stroke, as well as the distributed cortical reorganization supporting sensorimotor function. These findings support the potential value of a novel decoding-based neurophysiological measure for sensory-driven rehabilitation, biomarker development, and patient stratification.