Disconnection of the pathological connectome for multifocal epilepsy surgery

Integration of white matter architecture to stereo-EEG better describes epileptic spike propagation

OBJECTIVE

Stereo-electroencephalography (SEEG)-derived epilepsy networks are used to better understand a patient's epilepsy; however, a unimodal approach provides an incomplete picture. We combine tractography and SEEG to determine the relationship between spike propagation and the white matter architecture and to improve our understanding of spike propagation mechanisms.

METHODS

Probablistic tractography from diffusion imaging (dMRI) of matched subjects from the Human Connectome Project (HCP) was combined with patient-specific SEEG-derived spike propagation networks. Two regions-of-interest (ROIs) with a significant spike propagation relationship constituted a Propagation Pair.

RESULTS

In 56 of 59 patients, Propagation Pairs were more often tract-connected as compared to all ROI pairs (p < 0.01; d = -1.91). The degree of spike propagation between tract-connected ROIs was greater (39 ± 21%) compared to tract-unconnected ROIs (31 ± 18%; p < 0.0001). Within the same network, ROIs receiving propagation earlier were more often tract-connected to the source (59.7%) as compared to late receivers (25.4%; p < 0.0001).

CONCLUSIONS

Brain regions involved in spike propagation are more likely to be connected by white matter tracts. Between nodes, presence of tracts suggests a direct course of propagation, whereas the absence of tracts suggests an indirect course of propagation.

SIGNIFICANCE

We demonstrate a logical and consistent relationship between spike propagation and the white matter architecture.

Endovascular Electroencephalogram Records Simultaneous Subdural Electrode-Detectable, Scalp Electrode-Undetectable Interictal Epileptiform Discharges

Introduction: We hypothesized that an endovascular electroencephalogram (eEEG) can detect subdural electrode (SDE)-detectable, scalp EEG-undetectable epileptiform discharges. The purpose of this study is, therefore, to measure SDE-detectable, scalp EEG-undetectable epileptiform discharges by an eEEG on a pig. Methods: A pig under general anesthesia was utilized to measure an artificially generated epileptic field by an eEEG that was able to be detected by an SDE, but not a scalp EEG as a primary outcome. We also compared the phase lag of each epileptiform discharge that was detected by the eEEG and SDE as a secondary outcome. Results: The eEEG electrode detected 113 (97%) epileptiform discharges (97% sensitivity). Epileptiform discharges that were localized within the three contacts (contacts two, three and four), but not spread to other parts, were detected by the eEEG with a 92% sensitivity. The latency between peaks of the eEEG and right SDE earliest epileptiform discharge ranged from 0 to 48 ms (mean, 13.3 ms; median, 11 ms; standard deviation, 9.0 ms). Conclusion: In a pig, an eEEG could detect epileptiform discharges that an SDE could detect, but that a scalp EEG could not.

Neuromonitoring of the language pathways using cortico-cortical evoked potentials: a systematic review and meta-analysis

Cortico-cortical evoked potentials (CCEPs) are a surge in activity of one cortical zone caused by stimulation of another cortical zone. Recording of CCEP may be a useful method of intraoperative monitoring of the brain pathways, particularly of the language-related tracts. We aimed to conduct a systematic review and meta-analysis, dedicated to the clinical question: Does the CCEP recording effectively predict the postoperative speech deficits in neurosurgical patients? We conducted language-restricted PubMed, Google Scholar, Scopus, and Cochrane database search for eligible studies of CCEP published until March 2021. There were 4 articles (3 case series and 1 case report), which met our inclusion/exclusion criteria. A total of 32 patients (30 cases of tumors and 2 cavernomas) included in the analysis were divided into two cohorts - quantitative and qualitative, in accordance with the method of evaluating changes in the amplitude of CCEP after the lesion resection and postoperative alterations in speech function. Quantitative variables were studied using the Spearman rank correlation coefficient. Categorical variables were compared in groups by Fisher's exact test. We found a strong positive correlation between the decrease in the N1 wave amplitude and the severity of postoperative speech deficits (quantitative cohort: r = 0.57, p = 0.01; qualitative cohort: p = 0.02). Thus, the CCEP method using the N1 wave amplitude as a marker enables to effectively predict postoperative speech outcomes. Nevertheless, the low level of evidence for the included works indicated the necessity for additional research on this issue.

Intraoperative Brain Mapping by Cortico-Cortical Evoked Potential

To preserve postoperative brain function, it is important for neurosurgeons to fully understand the brain's structure, vasculature, and function. Intraoperative high-frequency electrical stimulation during awake craniotomy is the gold standard for mapping the function of the cortices and white matter; however, this method can only map the "focal" functions and cannot monitor large-scale cortical networks in real-time. Recently, an in vivo electrophysiological method using cortico-cortical evoked potentials (CCEPs) induced by single-pulse electrical cortical stimulation has been developed in an extraoperative setting. By using the CCEP connectivity pattern intraoperatively, mapping and real-time monitoring of the dorsal language pathway is available. This intraoperative CCEP method also allows for mapping of the frontal aslant tract, another language pathway, and detection of connectivity between the primary and supplementary motor areas in the frontal lobe network. Intraoperative CCEP mapping has also demonstrated connectivity between the frontal and temporal lobes, likely via the ventral language pathway. Establishing intraoperative electrophysiological monitoring is clinically useful for preserving brain function, even under general anesthesia. This CCEP technique demonstrates potential clinical applications for mapping and monitoring large-scale cortical networks.

Tailor-Made Surgery Based on Functional Networks for Intractable Epilepsy

Normal and pathological networks related to seizure propagation have got attention to elucide complex seizure semiology and contribute to diagnosis and surgical monitoring in epilepsy treatment. Since focal and generalized epileptogenic syndromes abnormalities might involve multiple foci and large-scale networks, we applied electrophysiolpgy (cortco-cortico evoked potential; CCEP), and tractography to make detailed diagnosis for complex syndrome. All 14 epilepsy patients with no or little abnormality on images investigations underwent subdural grid implantation for epilepsy diagnosis. To perform quick network analysis, we recorded and analyzed high gamma activity (HGA) of epileptogenic activity and CCEPs to identify pathological activity distribution and network connectivity. [Results] Pathological CCEPs showed two negative deflections consisting of early (>40 ms) and late (>150 ms) components in electrically stable circumstance at bed side and early CCEPs appeared in 57% of the patients. On the basis of the CCEP findings, tractography detected anatomical connections. Early components of pathological CCEPs diminished after complete disconnection of tractoography-based fibers between the foci in seven of eight cases. One case with residual pathological CCEPs showed poorer outcome. Thirteen (92.8%) patients with or without CCEPs who underwent network surgery had favorable prognosis except for a case with wide traumatic epilepsy. Intraoperative CCEP measurements and HGA mapping enabled visualization of pathological networks and clinical impotence as a biomarker to improve functional prognosis. HGA/CCEP recording should shed light on pathological and complex propagation for epilepsy surgery.

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

Localization of Epileptogenic Zone Based on Cortico-Cortical Evoked Potential (CCEP): A Feature Extraction and Graph Theory Approach

Objective

Epilepsy is a chronic brain disease, which is prone to relapse and affects individuals of all ages worldwide, particularly the very young and elderly. Up to one-third of these patients are medically intractable and require resection surgery. However, the outcomes of epilepsy surgery rely upon the clear identification of epileptogenic zone (EZ). The combination of cortico-cortical evoked potential (CCEP) and electrocorticography (ECoG) provides an opportunity to observe the connectivity of human brain network and more comprehensive information that may help the clinicians localize the epileptogenic focus more precisely. However, there is no standard analysis method in the clinical application of CCEPs, especially for the quantitative analysis of abnormal connectivity of epileptic networks. The aim of this paper was to present an approach on the batch processing of CCEPs and provide information relating to the localization of EZ for clinical study.

Methods

Eight medically intractable epilepsy patients were included in this study. Each patient was implanted with subdural grid electrodes and electrical stimulations were applied directly to their cortex to induce CCEPs. After signal preprocessing, we constructed three effective brain networks at different spatial scales for each patient, regarding the amplitudes of CCEPs as the connection weights. Graph theory was then applied to analyze the brain network topology of epileptic patients, and the topological metrics of EZ and non-EZ (NEZ) were compared.

Results

The effective connectivity network reconstructed from CCEPs was asymmetric, both the number and the amplitudes of effective CCEPs decreased with increasing distance between stimulating and recording sites. Besides, the distribution of CCEP responses was associated with the locations of EZ which tended to have higher degree centrality (DC) and nodal shortest path length (NLP) than NEZ.

Conclusion

Our results indicated that the brain networks of epileptics were asymmetric and mainly composed of short-distance connections. The DC and NLP were highly consistent to the distribution of the EZ, and these topological parameters have great potential to be readily applied to the clinical localization of the EZ.

A quantitative method for evaluating cortical responses to electrical stimulation

BACKGROUND

Electrical stimulation of the cortex using subdurally implanted electrodes can causally reveal structural connectivity by eliciting cortico-cortical evoked potentials (CCEPs). While many studies have demonstrated the potential value of CCEPs, the methods to evaluate them were often relatively subjective, did not consider potential artifacts, and did not lend themselves to systematic scientific investigations.

NEW METHOD

We developed an automated and quantitative method called SIGNI (Stimulation-Induced Gamma-based Network Identification) to evaluate cortical population-level responses to electrical stimulation that minimizes the impact of electrical artifacts. We applied SIGNI to electrocorticographic (ECoG) data from eight human subjects who were implanted with a total of 978 subdural electrodes. Across the eight subjects, we delivered 92 trains of approximately 200 discrete electrical stimuli each (amplitude 4-15 mA) to a total of 64 electrode pairs.

RESULTS

We verified SIGNI's efficacy by demonstrating a relationship between the magnitude of evoked cortical activity and stimulation amplitude, as well as between the latency of evoked cortical activity and the distance from the stimulated locations.

CONCLUSIONS

SIGNI reveals the timing and amplitude of cortical responses to electrical stimulation as well as the structural connectivity supporting these responses. With these properties, it enables exploration of new and important questions about the neurophysiology of cortical communication and may also be useful for pre-surgical planning.