Citations

Understanding motor representation in the human brain requires mapping beyond the primary motor cortex, into the distributed networks that coordinate complex movements. The insular cortex, a multifunctional hub buried within the Sylvian fissure, has been implicated in motor control through clinical observations and neuroimaging. Yet its precise relation to primary sensorimotor processing remains one of the least understood aspects of motor neurophysiology. To address this gap, we quantified electrophysiological changes from implanted depth electrodes in patients performing simple movement tasks combined with single-pulse electrical stimulation (SPES) to map effective connectivity. The movement data reveal somatotopically specific representation bilaterally, as well as intereffector regions that are active for different movement types. Hand representation is centered along the contralateral ventral aspect of the middle and posterior short gyri bilaterally, while tongue/mouth tuned sites cluster in the dorsal posterior short gyrus and the dorsal long gyri. Insular activity temporally follows the primary motor cortex (M1) and precedes movement onset. SPES revealed somatotopically specific connectivity between corresponding sites in M1 and insula (hand-to-hand, tongue-to-tongue) and between bilateral insulae. These observations establish that somatotopy is a conserved property of distributed motor control incorporating the insular representations and connectivity.

Miller et al. maintain that thalamic SEEG can now be considered a cornerstone of standard clinical practice in individualized epilepsy care, helping to define seizure onset and spread, assess therapeutic stimulation, map connectivity, and identify target-specific biomarkers for responsive neurostimulation.

Objective The effects of deep brain stimulation (DBS) manifest across multiple timescales, spanning seconds to months, and involve direct electrical effects, neuroplasticity, and network reorganization. In epilepsy, the delayed impact of DBS on seizures presents challenges for optimization. Single‐pulse stimulation and resulting brain stimulation evoked potentials (BSEPs) provide a means to assess effective connectivity and network excitability. This study integrates BSEPs and short trials of DBS during stereoelectroencephalography (sEEG) to map seizure network engagement, modulate network dynamics, and monitor excitability and interictal abnormalities for biomarker informed neuromodulation. Methods Ten individuals with drug resistant epilepsy undergoing clinical sEEG were enrolled in this retrospective cohort study of epilepsy neuromodulation biomarkers. Each patient underwent a trial of high frequency (145Hz) thalamic DBS. BSEPs were acquired before and after DBS trials. Baseline BSEP amplitude assessed seizure network engagement, and modulation of amplitude (pre vs post DBS) assessed change in network excitability. Interictal epileptiform discharges were tracked by an automated classifier. Results Baseline BSEPs delineated distinct patterns of network engagement between thalamic subfields with maximal frontotemporal engagement achieved with stimulation of the anterior nucleus of the thalamus‐ventral anterior nucleus junction. DBS delivered for >1.5 hours reduced BSEP amplitudes compared to baseline, and the degree of modulation correlated with baseline connectivity strength. Shorter DBS trials did not induce reliable BSEP amplitude suppression, but did immediately suppress interictal epileptiform discharge rates in well‐connected seizure networks. Interpretation BSEPs and trials of DBS during sEEG provide novel network biomarkers to evaluate the modulation of large‐scale networks across multiple timescales, advancing biomarker informed neuromodulation. ANN NEUROL 2026;99:748–760

Abstract Objective This study reports development and validation of a deep learning seizure detection algorithm for two‐channel subscalp electroencephalographic (EEG) recordings. Ultra‐long‐term monitoring of people with epilepsy may produce new insights into the timing and pattern of their seizures and may pave the way for novel therapeutic options. Although EEG is the accepted standard for epilepsy monitoring, ultra‐long‐term EEG recordings generate a massive quantity of data and are not reasonably reviewable in full by human readers. Methods The convolutional neural network‐bidirectional long short‐term memory (CNN‐BiLSTM) hybrid algorithm uses nine layers, operating on channel spectrograms 5 min in length with 50% overlap. Retrospective subscalp EEG data from 16 patients recorded at three centers were available for algorithm development and testing. EEG was recorded for a median of 63 days (range = 9–508), and a median of 17 seizures (range = 0–96) were recorded. Training data were augmented with scalp EEG seizures, and performance was benchmarked against a conventional spectral power classifier algorithm. We also evaluated an intrapatient training–testing approach where the algorithm was trained on the first 45% of data. Results The CNN‐BiLSTM trained on scalp EEG and subscalp EEG achieved an area under the receiver operating characteristic curve (AUROC) of .98 and an area under the precision–recall curve (AUPRC) of .50, which correspond to 94% sensitivity with 1.11 false detections per day. The same model trained on iEEG achieved only AUROC = .94 and AUPRC = .36. The conventional spectral band power detector achieved AUROC = .93 and AUPRC = .38. The CNN‐BiLSTM detector trained on the earliest half of the subscalp EEG data achieved AUROC = .93 and AUPRC = .37, corresponding to 87% sensitivity and 5.9 false detections per day. Significance High sensitivity and specificity are possible in automated seizure detection in two‐channel subscalp EEG data using a CNN‐BiLSTM framework. Performance of the detector is superior using subcutaneous EEG data for training rather than intracranial EEG, but addition of scalp EEG seizures for training was helpful.

Abstract Despite the growing interest in network-guided neuromodulation for epilepsy, uncertainty about the safety and long-term efficacy of thalamocortical stimulation persists. Our evaluation focused on the use of a four-lead open-loop implantable pulse generator for thalamocortical network neuromodulation. We retrospectively reviewed seven patients with diverse seizure networks—poorly localized, regional, or multifocal—undergoing thalamocortical neuromodulation. Employing a four-lead system, electrodes targeted both thalamic and cortical seizure network nodes. We assessed seizure severity, life satisfaction and sleep quality on a 10-point scale, and seizure frequency was assessed via telephone interviews and chart review. Outcomes were assessed by the Wilcoxon sign-rank test at the 0.05 significance level. Seven patients with a median age at implant of 22 years (range 14–42 years) had a median disabling seizure reduction of 93% (range 50–100%, P = 0.0156), with 100% responder rate (≥50% reduction in seizure frequency) after a median of 17 months post-implantation (range 13–60). The median improvement in seizure severity was 3.5 out of 10 points (P = 0.0312), life satisfaction 4.5 points (P = 0.0312) and quality of sleep 3 points (P = 0.062). No perioperative complications occurred. Transient seizure exacerbations (n = 2) and stimulation-related sensory/motor side-effects (n = 2) quickly resolved with parameter adjustments. One patient required surgical revision due to delayed infection. Six patients had permanent electrode placement refined by intracranial EEG trial stimulation; five patients had >90% reduction in seizure frequency during trial stimulation. Thalamocortical network neuromodulation using a four-lead open-loop system is safe and associated with significant improvements in seizure control and patient quality of life. Trial stimulation during intracranial EEG shows promise for enhancing seizure network engagement and parameter optimization but requires further study. Prospective controlled trials are needed to further characterize and validate the efficacy and side-effect profile of thalamocortical network neuromodulation for epilepsy.

Summary: Epilepsy neuromodulation treatment failure is a significant challenge, with multiple possible causes. The responsive neurostimulation (RNS) system delivers stimulation from a single current source, and the relative flow of the electrical current through each stimulating contact is inversely proportional to the relative impedance of each contact. Current shunting through low-impedance contacts (i.e., intraventricular contacts) can divert therapy away from the intended targets and may be a cause of treatment failure. We present a case of a patient with bitemporal epilepsy and bitemporal encephaloceles, with poor response to bilateral mesial temporal RNS, who completed stereotactic EEG (sEEG) monitoring to investigate the possible causes of treatment failure. The sEEG was safely completed without damaging the RNS device. The sEEG recorded independent bitemporal interictal epileptiform discharges and seizures, which did not arise from sampled encephalocele regions. The sEEG-recorded RNS stimulation artifact was reduced in the left mesial temporal region relative to the right, which suggested potential current shunting through the right-sided contacts. Impedance measurements confirmed several low-impedance contacts from the right lead, with associated intraventricular position on imaging. At last follow up, 161 days after replacement of the right lead, the patient experienced an additional 58% reduction in seizure burden. Effective therapy delivery by single-current-source neurostimulation systems, such as RNS, critically depends on relative electrode impedances. Current shunting through low-impedance contacts is an underappreciated potential cause of treatment failure. Routine impedance assessments and individualized stimulation programs are recommended to avoid unintended current diversion. Concurrent sEEG monitoring and active RNS are feasible and can characterize stimulation effects.

AbstractObjectiveThe effects of brain stimulation for diseases like epilepsy are delayed, making stimulation optimization difficult. The parameters for anterior thalamic nuclei (ANT) deep brain stimulation (DBS) for focal drug‐resistant epilepsy management are often restricted to those used in the SANTE landmark trial. There is little evidence regarding effective alternatives, and low‐frequency stimulation is typically neglected. We prospectively compare a widely differing stimulation parameter set to typical settings.MethodsThis randomized, modified crossover, open trial compares the efficacy and safety of an alternative set of parameters using continuous low‐frequency stimulation (cLFS) with a longer pulse width (7 Hz, 200 μs, continuous) compared to SANTE's intermittent high‐frequency stimulation (iHFS) with a short pulse width (145 Hz, 90 μs, cycling 1 min on/5 min off). After 3 months on a randomly assigned first set, patients are switched to the other settings, unless seizure‐free. Patients are re‐evaluated after 3 more months, which marks the completion of the trial. After that, they can either remain on the same settings or switch back.ResultsSixteen patients with a median baseline seizure frequency of 13.8 seizures/month (interquartile range [IQR] = 2.7–22.8) were included in the analysis. At trial completion (median follow‐up = 30 weeks, IQR = 26–35), ANT‐DBS significantly reduced median seizure frequency (62%, IQR = 18–81, Wilcoxon test statistic [W] = 99, p = .008). Both iHFS (33%, IQR = 0–65, W = 81, p = .02) and cLFS (73%, IQR = 30–79, W = 105, p = .001) significantly reduced median seizure frequency. cLFS showed improved median seizure frequency reduction compared to iHFS (W = 63, p = .03) and was not associated with any moderate or severe adverse effects.SignificanceResults support cLFS for ANT‐DBS as a safe and effective alternative to typical iHFS parameters. Stimulation with widely differing parameter sets may be as effective as or, in some situations, more effective than typical stimulation parameters.