Reassessing the impact of intraoperative electrocorticography on postoperative outcome of patients undergoing standard temporal lobectomy for MRI-negative temporal lobe epilepsy

Can we improve electrocorticography using a circular grid array in brain tumor surgery?

Intraoperative electrocorticography (iECoG) is used as an adjunct to localize the epileptogenic zone during surgical resection of brain tumors in patients with focal epilepsies. It also enables monitoring of after-discharges and seizures with EEG during functional brain mapping with electrical stimulation. When seizures or after-discharges are present, they complicate accurate interpretation of the mapping strategy to outline the brain's eloquent function and can affect the surgical procedure. Recurrent seizures during surgery requires urgent treatment and, when occurring during awake craniotomy, often leads to premature termination of brain mapping due to post-ictal confusion or sedation from acute rescue therapy. There are mixed results in studies on efficacy with iECoG in patients with epilepsy and brain tumors influencing survival and functional outcomes following surgery. Commercially available electrode arrays have inherent limitations. These could be improved with customization potentially leading to greater precision in safe and maximal resection of brain tumors. Few studies have assessed customized electrode grid designs as an alternative to commercially available products. Higher density electrode grids with intercontact distances less than 1 cm improve spatial delineation of electrophysiologic sources, including epileptiform activity, electrographic seizures, and afterdischarges on iECoG during functional brain mapping. In response to the shortcomings of current iECoG grid technologies, we designed and developed a novel higher-density hollow circular electrode grid array. The 360-degree iECoG monitoring capability allows continuous EEG recording during surgical intervention through the aperture with and without electrical stimulation mapping. Compared with linear strip electrodes that are commonly used for iECoG during surgery, the circular grid demonstrates significant benefits in brain tumor surgery. This includes quicker recovery of post-operative motor deficits (2.4 days versus 9 days, p = 0.05), more extensive tumor resection (92.0% versus 77.6%, p = 0.003), lesser reduction in Karnofsky Performance scale postoperatively (-2 versus -11.6, p = 0.007), and more sensitivity to recording afterdischarges. In this narrative review, we discuss the advantages and disadvantages of commercially available recording devices in the operating room and focus on the usefulness of the higher-density circular grid.

Can intraoperative electrocorticography be used to minimize the extent of resection in patients with temporal lobe epilepsy associated with hippocampal sclerosis?

OBJECTIVE

Tailored surgery to extensively resect epileptogenic lesions using intraoperative electrocorticography (ioECoG) may improve seizure outcomes. However, resection of large areas is associated with decreased memory function postoperatively. The authors assessed whether ioECoG could provide useful information on how to minimize the focus resection and obtain better seizure outcomes without memory deterioration. They examined the postoperative seizure-free period and memory alteration in a retrospective cohort of patients with mesial temporal lobe epilepsy (TLE) due to hippocampal sclerosis (HS) in whom the extent of removal was determined using ioECoG findings.

METHODS

The authors enrolled 82 patients with TLE associated with HS who were treated surgically. Transsylvian amygdalohippocampectomy was indicated as the first step. When visual inspection identified interictal epileptic discharges from the lateral temporal lobe on ioECoG, anterior temporal lobectomy (ATL) was eventually performed. The patients were divided into the selective amygdalohippocampectomy (SA, n = 40) and ATL (n = 42) groups. Postoperative seizure outcomes were assessed at 1, 2, 3, 5, and 7 years postoperatively using the International League Against Epilepsy classification. The Kaplan-Meier survival analysis was applied to evaluate the period of seizure recurrence between the SA and ATL groups. Factors attributed to seizure recurrence were analyzed using the Cox proportional hazards model, and they were as follows: epileptic focal laterality; age at seizure onset (< 10 or ≥ 10 years old); seizure frequency (more than weekly or less than weekly seizures); history of focal to bilateral tonic-clonic seizure; infectious etiology; and surgical procedure. The Wechsler Memory Scale-Revised was used to evaluate memory function pre- and postoperatively.

RESULTS

Seizure outcomes were significantly worse in the SA group than in the ATL group at 2 years postoperatively (p = 0.045). The International League Against Epilepsy class 1 outcomes at 7 years postoperatively in the SA and ATL groups were 63% and 81%, respectively. Kaplan-Meier analysis showed that seizure recurred significantly earlier in the SA group than in the ATL group (p = 0.031). The 2-way ANOVA analysis was used to compare the SA and ATL groups in each memory category, and revealed that there was no significant difference regardless of the side of surgery.

CONCLUSIONS

Visual assessment of ioECoG cannot be used as an indicator to minimize epileptic focus resection in patients with TLE associated with HS. ATL is more effective in obtaining seizure-free outcomes; however, both ATL and SA can preserve memory function.

Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks

Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.

Quantitative assessment of structural and functional changes in temporal lobe epilepsy with hippocampal sclerosis

Background

Magnetic resonance imaging (MRI) changes in hippocampal sclerosis (HS) could be subtle in a significant proportion of mesial temporal lobe epilepsy (mTLE) patients. In this study, we aimed to document the structural and functional changes in the hippocampus and amygdala seen in HS patients.

Methods

Quantitative features of the hippocampus and amygdala were extracted from structural MRI data in 66 mTLE patients and 28 controls. Structural covariance analysis was undertaken using volumetric data from the amygdala and hippocampus. Functional connectivity (FC) measured using resting intracranial electroencephalography (EEG) was analyzed in 22 HS patients and 16 non-HS disease controls.

Results

Hippocampal atrophy was present in both MRI-positive and MRI-negative HS groups (Mann-Whitney U: 7.61, P<0.01; Mann-Whitney U: 6.51, P<0.01). Amygdala volumes were decreased in the patient group (Mann-Whitney U: 2.92, P<0.05), especially in MRI-negative HS patients (Mann-Whitney U: 2.75, P<0.05). The structural covariance analysis showed the normalized volumes of the amygdala and hippocampus were tightly coupled in both controls and HS patients (ρ_Spearman =0.72, P<0.01). FC analysis indicated that HS patients had significantly increased connectivity (Student's t: 2.58, P=0.03) within the hippocampus but decreased connectivity between the hippocampus and amygdala (Student's t: 3.33, P=0.01), particularly for MRI-negative HS patients.

Conclusions

Quantitative structural changes, including hippocampal atrophy and temporal pole blurring, are present in both MRI-positive and MRI-negative HS patients, suggesting the potential usefulness of incorporating quantitative analyses into clinical practice. HS is characterized by increased intra-hippocampal EEG synchronization and decreased coupling between the hippocampus and amygdala.

A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG

To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative electrocorticography (ECoG) recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset (58 min, 16 recordings). We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (Spearman's [Formula: see text] = 0.81). The postsurgical seizure outcome was "predicted" with 100% (CI [63 100%]) accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.