BACKGROUND: Decompression of the injured spinal cord confers neuroprotection. Compared with timing of surgery, verification of surgical decompression is understudied. OBJECTIVE: To compare the judgment of cervical spinal cord decompression using real-time intraoperative ultrasound (IOUS) following laminectomy with postoperative MRI and CT myelography. METHODS: Fifty-one patients were retrospectively reviewed. Completeness of decompression was evaluated by real-time IOUS and compared with postoperative MRI (47 cases) and CT myelography (4 cases). RESULTS: Five cases (9.8%) underwent additional laminectomy after initial IOUS evaluation to yield a final judgment of adequate decompression using IOUS in all 51 cases (100%). Postoperative MRI/CT myelography showed adequate decompression in 43 cases (84.31%). Six cases had insufficient bony decompression, of which 3 (50%) had cerebrospinal fluid effacement at >1 level. Two cases had severe circumferential intradural swelling despite adequate bony decompression. Between groups with and without adequate decompression on postoperative MRI/CT myelography, there were significant differences for American Spinal Injury Association motor score, American Spinal Injury Association Impairment Scale grade, AO Spine injury morphology, and intramedullary lesion length (IMLL). Multivariate analysis using stepwise variable selection and logistic regression showed that preoperative IMLL was the most significant predictor of inadequate decompression on postoperative imaging (P = .024). CONCLUSION: Patients with severe clinical injury and large IMLL were more likely to have inadequate decompression on postoperative MRI/CT myelography. IOUS can serve as a supplement to postoperative MRI/CT myelography for the assessment of spinal cord decompression. However, further investigation, additional surgeon experience, and anticipation of prolonged swelling after surgery are required.

Electrocorticography (ECoG) data are commonly obtained during drug-resistant epilepsy (DRE) workup, in which subdural grids and stereotaxic depth electrodes are placed on the cortex for weeks at a time, with the goal of elucidating seizure origination. ECoG data can also be recorded from neuromodulatory devices, such as responsive neurostimulation (RNS), which involves the placement of electrodes deep in the brain. Of the neuromodulatory devices, RNS is the first to use recorded ECoG data to direct the delivery of electrical stimulation in order to control seizures. In this review, we first introduced the clinical management for epilepsy, and discussed the steps from seizure onset to surgical intervention. We then reviewed studies discussing the emergence and therapeutic mechanism behind RNS, and discussed why RNS may be underperforming despite an improved seizure detection mechanism. We discussed the potential utility of incorporating machine learning techniques to improve seizure detection in RNS, and the necessity to change RNS targets for stimulation, in order to account for the network theory of epilepsy. We concluded by commenting on the current and future status of neuromodulation in managing epilepsy, and the role of predictive algorithms to improve outcomes.

Glioma-related epilepsy (GRE) is a hallmark clinical presentation of gliomas with significant impacts on patient quality of life. The current standard of care for seizure management is comprised of anti-seizure medications (ASMs) and surgical resection. Seizures in glioma patients are often drug-resistant and can often recur after surgery despite total tumor resection. Therefore, current research is focused on the pro-epileptic pathological changes occurring in tumor cells and the peritumoral environment. One important contribution to seizures in GRE patients is metabolic reprogramming in tumor and surrounding cells. This is most evident by the significantly heightened seizure rate in patients with isocitrate dehydrogenase mutated (IDH^mut) tumors compared to patients with IDH wildtype (IDH^wt) gliomas. To gain further insight into glioma metabolism in epileptogenesis, this review compares the metabolic changes inherent to IDH^mut vs. IDH^wt tumors and describes the pro-epileptic effects these changes have on both the tumor cells and the peritumoral environment. Understanding alterations in glioma metabolism can help to uncover novel therapeutic interventions for seizure management in GRE patients.

Over the last two decades, it has become increasingly clear that epilepsy is a network disorder. However, it is unclear whether these networks are established only during seizures or persist interictally. The goal of this study was to identify whether functional seizure networks exist interictally and evaluate if there is a structural basis to these networks. We identified four patients with mesial temporal lobe epilepsy who underwent resective epilepsy surgery. We estimated functional and structural connectivity across intracranial electrode contacts involved in seizure onset, early spread, and uninvolved controls. Across all interictal epochs considered, we found higher functional and white matter connectivity across cortical regions involved in seizure spread. Additionally, we observed that the patient in our cohort with the best seizure outcome had the highest functional connectivity across seizure contacts. Functional connectivity findings suggest the presence of an interictal seizure network that parallels underlying structural connectivity. Furthermore, our findings suggest that disruption or ablation of highly connected seizure regions may be necessary to achieve improved post-operative seizure freedom.

Epilepsy is one of the most common neurological disorders, affecting over 65 million people worldwide. Despite medical management with anti-seizure medications (ASMs), many patients fail to achieve seizure freedom, with over one-third of patients having drug-resistant epilepsy (DRE). Even with surgical management through resective surgery and/or neuromodulatory interventions, over 50% of patients continue to experience refractory seizures within a year of surgery. Over the past 2 decades, studies have increasingly suggested that treatment failure is likely driven by untreated components of a pathological seizure network, a shift in the classical understanding of epilepsy as a focal disorder. However, this shift in thinking has yet to translate to improved treatments and seizure outcomes in patients. Here, we present a narrative review discussing the process of surgical epilepsy management. We explore current surgical interventions and hypothesized mechanisms behind treatment failure, highlighting evidence of pathologic seizure networks. Finally, we conclude by discussing how the network theory may inform surgical management, guiding the identification and targeting of more appropriate surgical regions. Ultimately, we believe that adapting current surgical practices and neuromodulatory interventions towards targeting seizure networks offers new therapeutic strategies that may improve seizure outcomes in patients suffering from DRE.

Recent technological advances in intracranial brain stimulation have enhanced the potential of neuromodulation for addressing neuropsychiatric disorders. We present a review of the methodology and the preliminary outcomes of the pioneering studies exploring intracranial biomarker detection and closed-loop neuromodulation to modulate high-symptom severity states in neuropsychiatric disorders. We searched PubMed, Scopus, Web of Science, Embase, and PsycINFO/PsycNet, followed by the reference and citation lists of retrieved articles. This search strategy yielded a total of 583 articles, of which 5 articles met the inclusion criteria, focusing on depression, obsessive-compulsive disorder, post-traumatic stress disorder, and binge eating disorder. We discuss the methodology of biomarker identification, the biomarkers identified, and the preliminary treatment outcomes for closed-loop neuromodulation. Successful biomarker identification hinges on investigating across various setting. Targeted neuromodulation, either directed at the biomarker or within its associated neural network, offers a promising treatment approach. Future research should seek to understand the mechanisms underlying the effects of neuromodulation as well as the long-term viability of these treatment effects across different neuropsychiatric conditions.