Clinical Neurophysiology

Objective: Needle electromyography (nEMG) is essential for diagnosing neuromuscular disorders but is invasive and often painful. We employed single-channel bipolar surface EMG (sEMG) analyzed with a novel wavelet-based analytical approach, Detecting and Extracting Elemental Wave Components based on a Wavelet Coefficient Set (DEWCS) and investigated whether fasciculation-related activity could be identified. Methods: In this prospective study, 28 patients undergoing nEMG for suspected neuromuscular disorders and 13 healthy controls were included. Resting-state sEMG was recorded from selected muscles using single-channel bipolar active electrodes at a high sampling rate. DEWCS was used to extract indices reflecting fast- and slow-type motor unit (MU)-related activity. These standardized indices were evaluated against nEMG-detected fasciculation potentials using generalized estimating equation logistic regression to account for within-subject clustering. Diagnostic performance was assessed by receiver operating characteristic analysis. Results: A total of 67 muscles from 38 participants were analyzed. Indices of fast- and slow-type MU-related activity were significantly associated with fasciculation potentials (slow: OR 5.10, p = 0.0041; fast: OR 2.38, p = 0.0162). The combined model showed excellent discrimination (area under the curve = 0.97), outperforming either index alone. Muscle region had no significant effect. Conclusions: A single-channel bipolar sEMG setup combined with DEWCS detected fasciculation-related activity with promising accuracy. This method may serve as a non-invasive surrogate marker of lower motor neuron involvement. Further validation in larger cohorts is warranted. Significance: This non-invasive sEMG approach may help detect fasciculation-related activity and complement nEMG in neuromuscular diagnostics.

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

Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compressed spectral array (CSA). Methods Using a public dataset of annotated neonatal EEGs, features of the aEEG and CSA were extracted from the left and right centroparietal electrodes. These features were then used to train and test three machine learning classifiers, Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Results The trained RF, SVM, and ANN classifiers had areas under the curve (AUC) of 0.80, 0.69, and 0.79 for capturing seizure time periods and an average accuracy of 0.91, 0.90, and 0.92 respectively for capturing seizure and non-seizure time periods. Median accuracy scores were higher among patients without hypoxic-ischemic encephalopathy (HIE; median = 1 for all three classifiers) than HIE patients (median = 0.92, 0.93, 0.93). Conclusion A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.

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.

Abstract Objective To validate a neonatal seizure detection algorithm that is based on extracted clinical features of the aEEG and CSA on a cohort of cooled neonatal patients with HIE. Methods A seizure detection algorithm was designed using aEEG margin features, CSA features, trained on a public dataset of 79 neonatal EEGs with three supervised machine learning classifiers. It was subsequently tested on an inhouse cohort of 23 neonates with asphyxia whose EEGs were collected during hypothermia therapy. Results The trained Random Forest Classifier, Support Vector Machines and Artificial Neural Network classifiers had an AUC of 0.76, 0.77, and 0.77 and an average accuracy of 0.85, 0.86, and 0.85 respectively. Finally, the average AUC across the 10 seizure patients included was 0.85. Conclusion A neonatal seizure detection algorithm that uses a combination of aEEG and CSA clinical features can capture seizures in HIE patients. Performance across seizure patients is not correlated with seizure duration.

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.

In patients with epilepsy, seizures are associated with pathological neural synchronization. However, the preictal period preceding a seizure often exhibits reduced spatial synchronization compared to normal cognition. This observation aligns with the concept of the brain as a complex dynamical system, where a reduction in dimensionality and resilience can precede a phase transition. The Critical Brain Hypothesis suggests a connection between the loss of healthy scale-free behavior and various disorders, including epilepsy. Our study investigates preictal changes by utilizing network features, such as mean node degree and mean clustering coefficient, derived from thresholded correlation matrices of patient intracranial electrocorticographic electrode data. We observed a suppression of intermittent high-synchronization periods within the feature space during the minutes leading up to seizure onset. This constriction of the explored hypervolume in the preictal state indicates a breakdown in the brains ability to maintain normal coherence. We use these preictal changes to predict the probability of seizure onset using a Support Vector Machine algorithm. These discrete predictions can then be combined into real-time continuous seizure risk forecasts via Bayesian updating. This innovative and computationally lightweight approach has the potential to significantly improve upon static predictions, providing opportunities for more adaptable, quantitative, and interpretable tools for managing seizures.

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.

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.

The aim of this work is to validate patient-specific EEG baseline establishment using the e-norms method as a screening and retrospective-review tool for seizure detection in pediatric epilepsy. The method was applied to 247 seizure-free EEG recordings (263.92 hours) from 10 patients in the CHB-MIT Scalp EEG Database (ages 3-18). A composite stability metric combining first-derivative dynamics, spectral entropy, variance, and line length was computed per 2-second epoch across 23 channels. Patient-specific detection thresholds were derived from each patient's seizure-free baseline using a weighted statistical procedure. Performance was validated against 72 expert-annotated seizures (2,705 epochs) across 62 seizure files, with durations spanning 6 to 264 seconds (44-fold range). The results show that detection achieved 94.4% event-level sensitivity (68 of 72 seizures; 95% CI 86.6-97.8%) and 81.5% epoch-level sensitivity (2,204 of 2,705 epochs; 95% CI 80.0-82.9%). Eight of ten patients achieved 100% event-level sensitivity with epoch-level sensitivity ranging from 58.7% to 100.0%. Two patients showed partial event-level failures (CHB-15: 17 of 20; CHB-18: 5 of 6), with the four missed events attributable to two characterizable failure modes. Patient-specific thresholds ranged from 4.06 to 4.81 (mean 4.51 +/- 0.25); threshold variation did not correlate reliably with age or sex, confirming that no universal threshold could achieve comparable performance. Detection margins ranged from 0.88 to 1.24 times. Patient-specific e-norms achieves 94.4% event-level sensitivity for pediatric EEG seizure detection without requiring labeled seizure training data, exceeding published human expert inter-rater agreement (50-76%) and recent automated approaches in adult cohorts using behind-the-ear EEG and wearable ECG. Two characterizable failure modes account for the four missed events and inform appropriate clinical use. As a high-sensitivity screening tool complementary to real-time alarm systems, the method is ready for adult validation, prospective deployment, and head-to-head benchmarking.

Muscle fatigue potentially interferes with proprioceptive afference from peripheral "movement sensors"-- the proprioceptors, which may hinder the crucial sensorimotor integration and thus locomotor performance. However, little is known about how muscle fatigue affects cortical processing of proprioceptive afference. Twenty-four healthy volunteers (30.7 {+/-} 6.5 yrs, 13 females) participated in the experiment, which included magnetoencephalography (MEG) recordings during ankle proprioceptive stimulation (2-Hz passive movements), and fatigue tasks comprised of isometric ankle plantar flexion. Corticokinematic coherence (CKC) between foot acceleration and MEG signals was examined before (PRE) and [~]3 min after (POST) the fatigue tasks to quantify the cortical proprioceptive processing. CKC peaked in the gradiometer pairs above the foot region of the primary sensorimotor (SM1) cortex in each participant. CKC strength did not show significant difference between PRE and POST at 2 Hz (0.30 {+/-} 0.12 vs. 0.30 {+/-} 0.14, p = 0.981) or its first harmonic at 4 Hz (0.38 {+/-} 0.14 vs. 0.37 {+/-} 0.13, p = 0.724). However, 4-Hz MEG power was [~]30% lower in POST than in PRE. Surprisingly, fatigue-induced bilateral increase of alpha and beta power was observed in SM1 hand regions during the movement stimulation. Our results indicated that the early processing of proprioceptive afference from the ankle joint was negligibly affected by muscle fatigue, or it recovered rapidly. The effects of muscle fatigue on the proprioceptive processing appear to extend beyond the primary somatotopic regions to bilateral SM1 neuronal networks. This cortical adaptation to muscle fatigue potentially preserves proprioceptive processing by modulating SM1 inhibitory neurons, offering a novel perspective for future research on proprioception.

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.

ObjectiveAging is associated with changes in cortical excitability and altered responsiveness to benzodiazepines, but the effects of benzodiazepine challenge on motor cortical paired-pulse physiology in older adults remain incompletely understood. We examined whether intravenous midazolam differentially modulates corticospinal excitability and short-interval paired-pulse transcranial magnetic stimulation (TMS) responses in younger and older adults. MethodsFifteen younger adults (18-35 years) and fifteen older adults (50-69 years) underwent single-pulse and paired-pulse TMS of the left primary motor cortex at baseline and during intravenous midazolam administration. Single-pulse motor evoked potential (MEP) amplitude was used to assess corticospinal excitability. Short-interval paired-pulse responses were quantified as the ratio of conditioned to unconditioned MEP amplitude. ResultsAt baseline, younger adults showed greater corticospinal excitability than older adults, reflected by larger single-pulse MEP amplitudes (adjusted p = 0.04). Younger adults demonstrated paired-pulse inhibition at baseline, reflected by a conditioned/unconditioned MEP ratio below 1.0 (ratio = 0.73; adjusted p < 0.01), whereas older adults did not show inhibition and instead had a mean ratio above 1.0 (ratio = 1.25). Midazolam reduced single-pulse MEP amplitudes in both groups. During midazolam administration, paired-pulse inhibition was no longer observed in younger adults, and older adults continued to show no evidence of inhibition. ConclusionsYounger and older adults differed in baseline corticospinal excitability and in short-interval paired-pulse TMS responses. Intravenous midazolam reduced corticospinal excitability and altered paired-pulse response patterns, eliminating baseline paired-pulse inhibition in younger adults while producing little measurable change in older adults. These findings suggest that aging may modify the net motor cortical response to benzodiazepine challenge. The results should be interpreted in relation to the paired-pulse stimulation parameters used and support further studies using complementary approaches to characterize age-related differences in inhibitory and facilitatory motor cortical circuits.

Central nervous system injury causes motor deficits through derangement of excitatory, inhibitory, and/or neuromodulatory inputs to motoneurons, the three fundamental components of motor commands. Typically, study of pathologic neural control in humans is restricted to only one of the three. Chardon et al. (2024) presented a fundamentally new approach to comprehensively study all components by reverse engineering motor unit firing patterns. We apply their framework to motor unit firing patterns from 89 people with multiple sclerosis (MS) and 34 controls to study excitatory, inhibitory, and neuromodulatory contributions to pathologic motor output. Disruptions to all components are plausible in MS, a disease hallmarked by heterogeneity in nearly all aspects. Accordingly, we found abnormalities in MS for all three components. Notably, neuromodulation included both high and low extremes. Our results suggest that pathophysiology of motor commands in MS varies among patients, a finding fundamentally different from other studied populations showing relative consistency.

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.