Journal of Clinical Neurophysiology最新文献

Summary: Epilepsy is not solely a disorder of abnormal brain structure; it is fundamentally a disorder of disrupted brain networks and impaired communication across brain regions. Thalamic neuromodulation, once conceptualized as a fixed, anatomically guided intervention, is now undergoing a paradigm shift toward dynamic, network-informed modulation. Using tools such as stereo-EEG, diffusion MRI, and advanced connectomic analyses, we are entering a new era where neurostimulation strategies can be individualized, responsive, and aligned with the real-time neurophysiology and structural networks of each patient. By integrating anatomic and functional connectivity data, we are moving toward precision neuromodulation tailored to patient-specific seizure networks. In this review, we highlight the emerging role of functional and structural connectivity in refining our understanding of seizure dynamics and guiding neuromodulation interventions.

Summary: Open label use of therapies with adult indications raises unique challenges in pediatric DRE. The following review details the landscape of pediatric intracranial neuromodulation. Initially, I discuss available evidence in pediatric neuromodulation while detailing the only randomized clinical trial in a pediatric developmental and epileptic encephalopathy. The reader is then directed to the use of intracranial neuromodulation in special circumstances and the rising trend in StereoEEG implantation of thalamic nuclei during presurgical monitoring in an attempt to further personalize individual therapy while circling back to challenges in getting insurance approval for off-label use in pediatric DRE.

Summary: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus is an FDA-approved therapy for drug-resistant focal epilepsy. Recent advances in device technology, thalamic stereotactic-EEG, and chronic sensing have deepened our understanding of corticothalamic networks in epilepsy and identified promising biomarkers to guide and personalize DBS. In this review, we examine electrophysiological, imaging, and clinical biomarkers relevant to epilepsy DBS, with a focus on their potential to support seizure detection, target engagement, network excitability tracking, and seizure risk forecasting. We highlight emerging insights from thalamic sEEG, including both passive recordings and active stimulation protocols, which enable mapping and modulation of large-scale brain networks. The capabilities of clinical sensing-enabled DBS systems are reviewed. As device functionality and biomarker discovery evolve, concerted translational efforts are needed to realize a new paradigm of personalized DBS in epilepsy.

Purpose: Visual EEG Confusion Assessment Method-Severity (VE-CAM-S) quantifies encephalopathy severity based on electroencephalography features. This study evaluated inter-rater reliability among experts using the VE-CAM-S scale.

Methods: Nine experts from six institutions independently reviewed 32 15-second electroencephalography samples in an online test, assessing 29 features (16 in the VE-CAM-S and 13 additional, or "VE-CAM-S+"). A consensus of three experts served as the gold standard. Performance was measured by the median Matthews correlation coefficient between expert and gold-standard VE-CAM-S+ scores, along with average sensitivity and specificity. Qualitative analysis identified common feature-recognition errors affecting scores.

Results: Experts achieved a median Matthews correlation coefficient of 0.82 [95% CI: 0.74-0.99]. Specificity exceeded 90% for most features except background β (87%) and generalized delta (71%). Sensitivity was ≥65% except for burst suppression with epileptiform activity (61%), extreme delta brush (EDB; 61%), posterior dominant rhythm (50%), background α (59%) and β (42%). Common errors included missing subtle findings, confusing features, and misidentifying extreme delta brush.

Conclusions: This pilot study offers some initial support for the reliability of VE-CAM-S+ scoring. The largest errors occurred when experts missed or falsely identified features with higher weight in the VE-CAM-S. Encephalopathy grading through VE-CAM-S may be improved by breaking high-stakes features into smaller parts, creating a "cheat sheet" with scored examples, and designing teaching materials.

Summary: Recent advances in artificial intelligence (AI) and machine learning (ML) can revolutionize neuromodulation therapies for drug-resistant epilepsy. Successful incorporation of AI/ML methods into the management of epilepsy can guide treatment decisions, enable interventions to adapt to dynamic epileptic networks, and hopefully improve patient outcomes. We introduce some common concepts in ML, focusing on neural networks, particularly convolutional and recurrent neural networks, and support vector machines, because these methods have been commonly applied to epilepsy neuromodulation. We discuss current AI/ML applications in neuromodulation, encompassing vagus nerve stimulation, responsive neurostimulation, and deep brain stimulation, for the treatment of epilepsy. We consider how AI/ML methods leverage large data sets to enhance patient-specific epileptic network analysis, optimize stimulation targets, and refine closed-loop systems for real-time seizure detection and termination. AI/ML applications extend to recognizing autonomic and behavioral seizure surrogates, detecting interictal epileptiform activity, and forecasting seizures for preemptive interventions. Furthermore, AI-powered neuroimaging analysis can enhance segmentation accuracy for precise electrode placement, which can improve neuromodulation outcomes. We review which AI/ML tools have been applied to each problem, as well as their relative performance. Challenges remain, however, in translating AI/ML models into clinical settings due to interpatient variability and limited real-world validation. Future directions include integrating behavioral signals, developing AI-assisted clinical decision tools, and refining energy-efficient neurostimulation designs. Large language models and generative AI hold promise for optimizing patient-specific neuromodulation strategies. However, further research is required to validate AI/ML applications in clinical practice, enhance model generalizability, and address ethical concerns surrounding data privacy and AI-driven decision making.

Purpose: Intraoperative neuromonitoring (IONM) is a valuable tool to monitor the neural axis during various procedures and guide intervention aimed at managing operative complications. The literature lacks large scale data on trends and demographic disparities in IONM use in the United States.

Methods: Data were abstracted from the 2008-2021 National Inpatient Sample. Hospitalizations for neurosurgical (spinal, craniotomy, carotid artery, cranial/peripheral nerve), cardiac/vascular, and head/neck/thyroid procedures were identified and stratified by IONM use. Logistic regression models were estimated to assess disparities and trends in IONM use. Multivariable models adjusted for patient- and facility-level characteristics.

Results: From 2008 to 2021, the rate of IONM use increased significantly in neurosurgical (3.69% to 18.62%, p < 0.001) and cardiac/vascular procedures (0.018% to 0.6%, p < 0.001). IONM use for head/neck/thyroid procedures increased steadily until 2014 and then declined (p < 0.001). Compared with hospitalizations of White patients, Black (aOR = 0.87, 95% CI: 0.81-0.94) and Hispanic (aOR = 0.88, 95% CI: 0.81-0.96) patients were associated with lower odds of IONM use during craniotomy. Lower incomes (0-25th quartile) were associated with lower odds of IONM use in both spinal (aOR = 0.83, 95% CI: 0.78-0.88) and craniotomy procedures (aOR = 0.78, 95% CI: 0.72-0.85).

Conclusions: There is a growing demand for IONM to enhance the safety of various procedures, indicating a need for neurologists and technologists with this expertise. In addition, we found significant racial/ethnic and socioeconomic disparities in IONM use. These findings can be valuable for health care administrators and policymakers to address disparities in the access to IONM.

Summary: Over the past 20 years, responsive neurostimulation (RNS), a closed-loop device for treating certain forms of drug-resistant focal epilepsy, has become ensconced in the epileptologist's therapeutic armamentarium. Through neuromodulatory effects, RNS therapy gradually reduces seizures over years, providing diagnostically valuable intracranial recordings along the way. However, the neuromodulatory potential of RNS therapy has not been fully harnessed. Seizure reduction is often slow, outcomes vary across individuals and defy prognostication, seizure freedom is uncommon, and many patients do not derive significant benefit. These limitations may stem from the "black box" nature of RNS therapy. The antiseizure mechanism(s) of RNS remain poorly understood, and, in the absence of first principles to inform selection of the candidates most likely to benefit, the ideal brain regions to target, and the most effective stimulation parameters, contemporary use of RNS therapy is largely empiric. Fortunately, recent advances in neuroimaging, neurophysiology, artificial intelligence, and engineering have made the goal of rational, personalized neurostimulation a near-term reality. Here, we review recent progress toward this goal, focusing on novel approaches to patient selection, brain network topology, state-dependent effects, and stimulation parameter optimization. By considering the who, where, when, and how of RNS, we highlight emerging paradigm shifts that will help usher in a new age of RNS therapy that is more personalized and more effective.