Joseph R. Madsen

Background: In pediatric patients with drug-resistant epilepsy, successful localization of the seizure onset zone (SOZ) is critical to surgical planning and outcome prognostication. SOZ localization requires the identification of seizures captured through intracranial electroencephalography (iEEG), necessitating prolonged hospital stays for invasive monitoring and two-stage surgical procedures at minimum. Localization of SOZ in automated fashion using only interictal data would enable a substantial reduction in the time required for pre-resection iEEG recording. Using iEEG to model brain functional connectivity (FC) is an approach that has shown significant potential. Summary: We conduct a literature review on directed FC methods and their use in preoperative SOZ localization. Granger Causality, an approach originally used to analyze economic time series, has evolved into multiple similar methodologies for directed FC graph creation. Several modalities show strong correlations between electrodes with specific FC patterns and SOZs, but there is no current tool that can reliably predict SOZs from interictal iEEG data. Key Messages: Multiple studies show a pattern of increased inward FC in electrodes located in the SOZ during interictal periods, with reversed information flow during seizures, suggesting the increased inward flow toward the SOZ may represent inhibitory pathways, which, when absent, lead to a more epileptogenic state. Further analysis of the changes in directed FC across longer periods may help elucidate how to select optimal segments for localization.

Background/Objectives: Artificial intelligence (AI), particularly large language models (LLMs), has demonstrated versatility in various applications but faces challenges in specialized domains like neurology. This study evaluates a specialized LLM’s capability and trustworthiness in complex neurological diagnosis, comparing its performance to neurologists in simulated clinical settings. Methods: We deployed GPT-4 Turbo (OpenAI, San Francisco, CA, US) through Neura (Sciense, New York, NY, US), an AI infrastructure with a dual-database architecture integrating “long-term memory” and “short-term memory” components on a curated neurological corpus. Five representative clinical scenarios were presented to 13 neurologists and the AI system. Participants formulated differential diagnoses based on initial presentations, followed by definitive diagnoses after receiving conclusive clinical information. Two senior academic neurologists blindly evaluated all responses, while an independent investigator assessed the verifiability of AI-generated information. Results: AI achieved a significantly higher normalized score (86.17%) compared to neurologists (55.11%, p < 0.001). For differential diagnosis questions, AI scored 85% versus 46.15% for neurologists, and for final diagnosis, 88.24% versus 70.93%. AI obtained 15 maximum scores in its 20 evaluations and responded in under 30 s compared to neurologists’ average of 9 min. All AI-provided references were classified as relevant with no hallucinatory content detected. Conclusions: A specialized LLM demonstrated superior diagnostic performance compared to practicing neurologists across complex clinical challenges. This indicates that appropriately harnessed LLMs with curated knowledge bases can achieve domain-specific relevance in complex clinical disciplines, suggesting potential for AI as a time-efficient asset in clinical practice.

For women with shunted hydrocephalus reaching childbearing age, questions regarding the risks of cerebrospinal fluid (CSF) diversion shunts during pregnancy loom large. Current clinician resources cite decades’ old data with high shunt failure rates of 10% to 50%,^1-6 which is likely worrisome to expectant patients and prompted this investigation into the safety of CSF shunts during pregnancy.

Introduction Literature lacks studies investigating the cortical generation of sleep spindles in drug-resistant epilepsy (DRE) and how they evolve after resection of the epileptogenic zone (EZ). Here, we examined sleep EEGs of children with focal DRE who became seizure-free after focal epilepsy surgery, and aimed to investigate the changes in the spindle generation before and after the surgery using low-density scalp EEG and electrical source imaging (ESI). Methods We analyzed N2-sleep EEGs from 19 children with DRE before and after surgery. We identified slow (8–12 Hz) and fast spindles (13–16 Hz), computed their spectral features and cortical generators through ESI and computed their distance from the EZ and irritative zone (IZ). We performed two-way ANOVA testing the effect of spindle type (slow vs. fast) and surgical phase (pre-surgery vs. post-surgery) on each feature. Results Power, frequency and cortical activation of slow spindles increased after surgery (p < 0.005), while this was not seen for fast spindles. Before surgery, the cortical generators of slow spindles were closer to the EZ (57.3 vs. 66.2 mm, p = 0.007) and IZ (41.3 vs. 55.5 mm, p = 0.02) than fast spindle generators. Conclusions Our data indicate alterations in the EEG slow spindles after resective epilepsy surgery. Fast spindle generation on the contrary did not change after surgery. Although the study is limited by its retrospective nature, lack of healthy controls, and reduced cortical spatial sampling, our findings suggest a spatial relationship between the slow spindles and the epileptogenic generators.

BACKGROUND: Stereoelectroencephalography (sEEG) facilitates electrical sampling and evaluation of complex deep-seated, dispersed, and multifocal locations. Granger causality (GC), previously used to study seizure networks using interictal data from subdural grids, may help identify the seizure-onset zone from interictal sEEG recordings. OBJECTIVE: To examine whether statistical analysis of interictal sEEG helps identify surgical target sites and whether surgical resection of highly ranked nodes correspond to favorable outcomes. METHODS: Ten minutes of extraoperative recordings from sequential patients who underwent sEEG evaluation were analyzed (n = 20). GC maps were compared with clinically defined surgical targets using rank order statistics. Outcomes of patients with focal resection/ablation with median follow-up of 3.6 years were classified as favorable (Engel 1, 2) or poor (Engel 3, 4) to assess their relationship with the removal of highly ranked nodes using the Wilcoxon rank-sum test. RESULTS: In 12 of 20 cases, the rankings of contacts (based on the sum of outward connection weights) mapped to the seizure-onset zone showed higher causal node connectivity than predicted by chance (P ≤ .02). A very low aggregate probability (P < 10⁻¹⁸, n = 20) suggests that causal node connectivity predicts seizure networks. In 8 of 16 with outcome data, causal connectivity in the resection was significantly greater than in the remaining contacts (P ≤ .05). We found a significant association between favorable outcome and the presence of highly ranked nodes in the resection (P < .05). CONCLUSION: Granger analysis can identify seizure foci from interictal sEEG and correlates highly ranked nodes with favorable outcome, potentially informing surgical decision-making without reliance on ictal recordings.

Background Posterior quadrant disconnection (PQD) is intended to interrupt the propagation of intractable unilateral temporo-parieto-occipital epilepsy. Method An enhanced operative video presents the illustrative case of a total PQD indicated for a 15-year-old boy with Sturge-Weber syndrome suffering from seizure recurrence after a partial PQD. We describe the surgical procedure with emphasis on relevant anatomy and multimodal intraoperative guidance in three steps: (i) parieto-occipital disconnection, (ii) posterior callosotomy, and (iii) temporal disconnection/resection. Pearls and pitfalls of surgical management are discussed. Conclusion PQD is a less invasive surgical option to typical hemispherotomy and hemispherectomy for selected indications of posterior multilobar epilepsy.