SEEG assistant: a 3DSlicer extension to support epilepsy surgery

Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity

Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.

Disrupting the epileptogenic network with stereoelectroencephalography-guided radiofrequency thermocoagulation

Stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) is a treatment option for focal drug-resistant epilepsy. In previous studies, this technique has shown seizure reduction by ≥50% in 50% of patients at 1 year. However, the relationship between the location of the ablation within the epileptogenic network and clinical outcomes remains poorly understood. Seizure outcomes were analyzed for patients who underwent SEEG-guided RF-TC and across subgroups depending on the location of the ablation within the epileptogenic network, defined as SEEG sites involved in seizure generation and spread. Eighteen patients who had SEEG-guided RF-TC were included. SEEG-guided seizure-onset zone ablation (SEEG-guided SOZA) was performed in 12 patients, and SEEG-guided partial seizure-onset zone ablation (SEEG-guided P-SOZA) in 6 patients. The early spread was ablated in three SEEG-guided SOZA patients. Five patients had ablation of a lesion. The seizure freedom rate in the cohort ranged between 22% and 50%, and the responder rate between 67% and 85%. SEEG-guided SOZA demonstrated superior results for both outcomes compared to SEEG-guided P-SOZA at 6 months (seizure freedom p = .294, responder rate p = .014). Adding the early spread ablation to SEEG-guided SOZA did not increase seizure freedom rates but exhibited comparable effectiveness regarding responder rates, indicating a potential network disruption.

Voxeloc: A time-saving graphical user interface for localizing and visualizing stereo-EEG electrodes

BACKGROUND

Thanks to its unrivalled spatial and temporal resolutions and signal-to-noise ratio, intracranial EEG (iEEG) is becoming a valuable tool in neuroscience research. To attribute functional properties to cortical tissue, it is paramount to be able to determine precisely the localization of each electrode with respect to a patient's brain anatomy. Several software packages or pipelines offer the possibility to localize manually or semi-automatically iEEG electrodes. However, their reliability and ease of use may leave to be desired.

NEW METHOD

Voxeloc (voxel electrode locator) is a Matlab-based graphical user interface to localize and visualize stereo-EEG electrodes. Voxeloc adopts a semi-automated approach to determine the coordinates of each electrode contact, the user only needing to indicate the deep-most contact of each electrode shaft and another point more proximally.

RESULTS

With a deliberately streamlined functionality and intuitive graphical user interface, the main advantages of Voxeloc are ease of use and inter-user reliability. Additionally, oblique slices along the shaft of each electrode can be generated to facilitate the precise localization of each contact. Voxeloc is open-source software and is compatible with the open iEEG-BIDS (Brain Imaging Data Structure) format.

COMPARISON WITH EXISTING METHODS

Localizing full patients' iEEG implants was faster using Voxeloc than two comparable software packages, and the inter-user agreement was better.

CONCLUSIONS

Voxeloc offers an easy-to-use and reliable tool to localize and visualize stereo-EEG electrodes. This will contribute to democratizing neuroscience research using iEEG.

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings

BACKGROUND

Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined.

NEW METHOD

We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared.

RESULTS

Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters.

COMPARISON WITH EXISTING METHODS

Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation.

CONCLUSIONS

Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

Introducing HiBoP: a Unity-based visualization software for large iEEG datasets

BACKGROUND

Intracranial EEG data offer a unique spatio-temporal precision to investigate human brain functions. Large datasets have become recently accessible thanks to new iEEG data-sharing practices and tighter collaboration with clinicians. Yet, the complexity of such datasets poses new challenges, especially regarding the visualization and anatomical display of iEEG.

NEW METHOD

We introduce HiBoP, a multi-modal visualization software specifically designed for large groups of patients and multiple experiments. Its main features include the dynamic display of iEEG responses induced by tasks/stimulations, the definition of Regions and electrodes Of Interest, and the shift between group-level and individual-level 3D anatomo-functional data.

RESULTS

We provide a use-case with data from 36 patients to reveal the global cortical dynamics following tactile stimulation. We used HiBoP to visualize high-gamma responses [50-150 Hz], and define three major response components in primary somatosensory and premotor cortices and parietal operculum.

COMPARISON WITH EXISTING METHODS(S)

Several iEEG softwares are now publicly available with outstanding analysis features. Yet, most were developed in languages (Python/Matlab) chosen to facilitate the inclusion of new analysis by users, rather than the quality of the visualization. HiBoP represents a visualization tool developed with videogame standards (Unity/C#), and performs detailed anatomical analysis rapidly, across multiple conditions, patients, and modalities with an easy export toward third-party softwares.

CONCLUSION

HiBoP provides a user-friendly environment that greatly facilitates the exploration of large iEEG datasets, and helps users decipher subtle structure/function relationships.

Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans

Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits which contribute to the associated evoked potentials (EPs). Here we find that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the subject's behavioral state. We optogenetically dissect the underlying circuit in mice, demonstrating that the late component of these EPs is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with the bursting frequency and synchronicity of thalamic neurons, modulated by the subject's behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that the cortical stimulation engages cortico-thalamo-cortical circuits highly preserved across different species and stimulation modalities.

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

BACKGROUND

Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.

NEW METHODS

We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes' CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.

RESULTS

We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.

COMPARISON WITH EXISTING METHODS

GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.

CONCLUSION

GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.

Electrical stimulation induced self-related auditory hallucinations correlate with oscillatory power change in the default mode network

Self-related information is crucial in our daily lives, which has led to the proposal that there is a specific brain mechanism for processing it. Neuroimaging studies have consistently demonstrated that the default mode network (DMN) is strongly associated with the representation and processing of self-related information. However, the precise relationship between DMN activity and self-related information, particularly in terms of neural oscillations, remains largely unknown. We electrically stimulated the superior temporal and fusiform areas, using stereo-electroencephalography to investigate neural oscillations associated with elicited self-related auditory hallucinations. Twenty-two instances of auditory hallucinations were recorded and categorized into self-related and other-related conditions. Comparing oscillatory power changes within the DMN between self-related and other-related auditory hallucinations, we discovered that self-related hallucinations are associated with significantly stronger positive power changes in both alpha and gamma bands compared to other-related hallucinations. To ensure the validity of our findings, we conducted controlled analyses for factors of familiarity and clarity, which revealed that the observed effects within the DMN remain independent of these factors. These results underscore the significance of the functional role of the DMN during the processing of self-related auditory hallucinations and shed light on the relationship between self-related perception and neural oscillatory activity.

Stereo-electroencephalography (SEEG)-Guided Surgery in Epilepsy With Cingulate Gyrus Involvement: Electrode Implantation Strategies and Postoperative Seizure Outcome

SUMMARY

Surgical treatment of cingulate gyrus epilepsy is associated with good results on seizures despite its rarity and challenging aspects. Invasive EEG monitoring is often mandatory to assess the epileptogenic zone in these patients. To date, only small surgical series have been published, and a consensus about management of these complex cases did not emerge. The authors retrospectively analyzed a large surgical series of patients in whom at least part of the cingulate gyrus was confirmed as included in the epileptogenic zone by means of stereo-electroencephalography and was thus resected. One hundred twenty-seven patients were selected. Stereo-electroencephalography-guided implantation of intracerebral electrodes was performed in the right hemisphere in 62 patients (48.8%) and in the left hemisphere in 44 patients (34.7%), whereas 21 patients (16.5%) underwent bilateral implantations. The median number of implanted electrodes per patient was 13 (interquartile range 12-15). The median number of electrodes targeting the cingulate gyrus was 4 (interquartile range 3-5). The cingulate gyrus was explored bilaterally in 19 patients (15%). Complication rate was 0.8%. A favorable outcome (Engel class I) was obtained in 54.3% of patients, with a median follow-up of 60 months. The chance to obtain seizure freedom increased in cases in whom histologic diagnosis was type-IIb focal cortical dysplasia or tumor (mostly ganglioglioma or dysembryoplastic neuroepithelial tumor) and with male gender. Higher seizure frequency predicted better outcome with a trend toward significance. Our findings suggest that stereo-electroencephalography is a safe and effective methodology in achieving seizure freedom in complex cases of epilepsy with cingulate gyrus involvement.

Application of 3D Slicer Combined With Simple Coordinate Method in Operation of Cerebral Arteriovenous Malformations in Functional Areas

The authors report a case of intracranial arteriovenous malformation in functional areas, initially presenting with symptomatic epilepsy was surgically excised by the Neurosurgery Department of our hospital. The patient's head computed tomography, magnetic resonance imaging, and digital subtraction angiography examination suggested intracranial arteriovenous malformations in the left frontal functional area. A preoperative 3D-reconstruction technique was used to reconstruct the malformed vascular mass, supplying arteries, draining veins, and precise surgical resection was performed. Postoperative pathology indicated vascular malformation. No seizures occurred after surgery. There was no further neurological impairment. Preoperative use of image postprocessing techniques can facilitate precise surgical resection of brain arteriovenous malformations. Three-dimensional Slicer in cerebral arteriovenous malformations in functional areas not only shortened the preoperative planning time but also improved the efficiency of the surgery. Reduce the incidence of postoperative complications. It is helpful for further popularization and application.

Stereo-EEG tailored resection in a child with presumed perinatal post-stroke epilepsy: The complex organization of epileptogenic zone

Introduction

Only a few studies have focused on tailored resection in post-stroke epilepsy, in which hemispherectomy and hemispherotomy are the most recognized treatments.

Case description

We describe the case of a patient with drug-resistant, presumed perinatal, post-stroke epilepsy and moderate right hemiparesis. The seizures were stereotyped, both spontaneous and induced by sudden noises and somatosensory stimuli. Considering the discordant anatomic-electro-clinical data - left perisylvian malacic lesion with electrical onset over the left mesial fronto-central leads - and the patient's functional preservation, SEEG was performed. SEEG revealed sub-continuous abnormalities in the perilesional regions. Several seizures were recorded, with onset over the premotor area, rapidly involving the motor and insular-opercular regions. We decided for a combined surgical approach, SEEG-guided radiofrequency thermocoagulation, on the fronto-mesial structure but also on the central operculum, followed by resective surgery including only the fronto-mesial structures.

Discussion and conclusion

The SEEG allowed to localize the epileptogenic zone far away from the anatomical lesion but connected to part of it. A combined surgical approach tailored on SEEG results allowed a good outcome (Engel Ib) without additional deficits.

Automated Identification of Stereoelectroencephalography Contacts and Measurement of Factors Influencing Accuracy of Frame Stereotaxy

OBJECTIVE

Stereoelectroencephalography (SEEG) is an established invasive diagnostic technique for use in patients with drug-resistant focal epilepsy evaluated before resective epilepsy surgery. The factors that influence the accuracy of electrode implantation are not fully understood. Adequate accuracy prevents the risk of major surgery complications. Precise knowledge of the anatomical positions of individual electrode contacts is crucial for the interpretation of SEEG recordings and subsequent surgery.

METHODS

We developed an image processing pipeline to localize implanted electrodes and detect individual contact positions using computed tomography (CT), as a substitute for time-consuming manual labeling. The algorithm automates measurement of parameters of the electrodes implanted in the skull (bone thickness, implantation angle and depth) for use in modeling of predictive factors that influence implantation accuracy.

RESULTS

Fifty-four patients evaluated by SEEG were analyzed. A total of 662 SEEG electrodes with 8,745 contacts were stereotactically inserted. The automated detector localized all contacts with better accuracy than manual labeling (p < 0.001). The retrospective implantation accuracy of the target point was 2.4 ± 1.1 mm. A multifactorial analysis determined that almost 58% of the total error was attributable to measurable factors. The remaining 42% was attributable to random error.

CONCLUSION

SEEG contacts can be reliably marked by our proposed method. The trajectory of electrodes can be parametrically analyzed to predict and validate implantation accuracy using a multifactorial model.

SIGNIFICANCE

This novel, automated image processing technique is a potentially clinically important, assistive tool for increasing the yield, efficiency, and safety of SEEG.

An in-vivo validation of ESI methods with focal sources

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

Precise electrode localization is important for maximizing the utility of intracranial EEG data. Electrodes are typically localized from post-implantation CT artifacts, but algorithms can fail due to low signal-to-noise ratio, unrelated artifacts, or high-density electrode arrays. Minimizing these errors usually requires time-consuming visual localization and can still result in inaccurate localizations. In addition, surgical implantation of grids and strips typically introduces non-linear brain deformations, which result in anatomical registration errors when post-implantation CT images are fused with the pre-implantation MRI images. Several projection methods are currently available, but they either fail to produce smooth solutions or do not account for brain deformations. To address these shortcomings, we propose two novel algorithms for the anatomical registration of intracranial electrodes that are almost fully automatic and provide highly accurate results. We first present GridFit, an algorithm that simultaneously localizes all contacts in grids, strips, or depth arrays by fitting flexible models to the electrodes' CT artifacts. We observed localization errors of less than one millimeter (below 8% relative to the inter-electrode distance) and robust performance under the presence of noise, unrelated artifacts, and high-density implants when we ran ~6000 simulated scenarios. Furthermore, we validated the method with real data from 20 intracranial patients. As a second registration step, we introduce CEPA, a brain-shift compensation algorithm that combines orthogonal-based projections, spring-mesh models, and spatial regularization constraints. When tested with real data from 15 patients, anatomical registration errors were smaller than those obtained for well-established alternatives. Additionally, CEPA accounted simultaneously for simple mechanical deformation principles, which is not possible with other available methods. Inter-electrode distances of projected coordinates smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. Moreover, in an additional validation procedure, we found that modeling resting-state high-frequency activity (75-145 Hz ) in five patients further supported our new algorithm. Together, GridFit and CEPA constitute a versatile set of tools for the registration of subdural grid, strip, and depth electrode coordinates that provide highly accurate results even in the most challenging implantation scenarios. The methods presented here are implemented in the iElectrodes open-source toolbox, making their use simple, accessible, and straightforward to integrate with other popular toolboxes used for analyzing electrophysiological data.

Modules in connectomes of phase-synchronization comprise anatomically contiguous, functionally related regions

Modules in brain functional connectomes are essential to balancing segregation and integration of neuronal activity. Connectomes are the complete set of pairwise connections between brain regions. Non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) have been used to identify modules in connectomes of phase-synchronization. However, their resolution is suboptimal because of spurious phase-synchronization due to EEG volume conduction or MEG field spread. Here, we used invasive, intracerebral recordings from stereo-electroencephalography (SEEG, N = 67), to identify modules in connectomes of phase-synchronization. To generate SEEG-based group-level connectomes affected only minimally by volume conduction, we used submillimeter accurate localization of SEEG contacts and referenced electrode contacts in cortical gray matter to their closest contacts in white matter. Combining community detection methods with consensus clustering, we found that the connectomes of phase-synchronization were characterized by distinct and stable modules at multiple spatial scales, across frequencies from 3 to 320 Hz. These modules were highly similar within canonical frequency bands. Unlike the distributed brain systems identified with functional Magnetic Resonance Imaging (fMRI), modules up to the high-gamma frequency band comprised only anatomically contiguous regions. Notably, the identified modules comprised cortical regions involved in shared repertoires of sensorimotor and cognitive functions including memory, language and attention. These results suggest that the identified modules represent functionally specialised brain systems, which only partially overlap with the brain systems reported with fMRI. Hence, these modules might regulate the balance between functional segregation and functional integration through phase-synchronization.

Uncovering the fast, directional signal flow through the human temporal pole during semantic processing

The temporal pole (TP) plays a central role in semantic memory, yet its neural machinery is unknown. Intracerebral recordings in patients discriminating visually the gender or actions of an actor, yielded gender discrimination responses in the ventrolateral (VL) and tip (T) regions of right TP. Granger causality revealed task-specific signals travelling first forward from VL to T, under control of orbitofrontal cortex (OFC) and neighboring prefrontal cortex, and then, strongly, backwards from T to VL. Many other cortical regions provided inputs to or received outputs from both TP regions, often with longer delays, with ventral temporal afferents to VL signaling the actor's physical appearance. The TP response timing reflected more that of the connections to VL, controlled by OFC, than that of the input leads themselves. Thus, visual evidence for gender categories, collected by VL, activates category labels in T, and consequently, category features in VL, indicating a two-stage representation of semantic categories in TP.

Intermediate stimulation frequencies for language mapping using Stereo-EEG

OBJECTIVE

Identification of eloquent cortices is a prerequisite for the surgical plan but may be challenging, in particular for language areas (LAs), considering the complexity of language function and organization. Electrical intracerebral stimulations (ES) during Stereo-electroencephalography are an essential tool in the localization of LAs and high frequency ES (HFS, 50 Hz) are current gold standard. Low frequencies (1 Hz) are not effective. We aim to investigate different ES frequencies for establishing their utility in localizing LAs.

METHODS

We implemented an observational and prospective study evaluating frequencies lower than 50 and higher than 1 Hz; indicated as "intermediate" frequencies (IFS) performed at 6, 9 and 12 Hz and lasting 15 seconds. We included ten patients and carried out a standardized protocol comparing IFS to HFS.

RESULTS

Eighty-six ES were carried out in LAs, positive for a language interference in 61.6% without noteworthy difference between IFS and HFS. Among these, 53.3% IFS vs 21.7% HFS yielded no after-discharge.

CONCLUSIONS

IFS were similarly effective as HFS, with lower incidence of ADs. Their longer duration facilitated more accurate clinical testing.

SIGNIFICANCE

Our results are promising, suggesting that IFS can be useful in the study of LAs.

Assessing the coupling between local neural activity and global connectivity fluctuations: Application to human intracranial electroencephalography during a cognitive task

Cognitive-relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face-recognition task performed in patients with drug-resistant epilepsy and monitored with intracranial electroencephalography (EEG). Based on our observations, we developed a novel analytical framework (named "local-global" framework) to statistically correlate the brain activity in every recorded gray-matter region with the widespread connectivity fluctuations as proxy to identify concurrent local activations and global brain phenomena that may plausibly reflect a common functional network during cognition. The application of the local-global framework to the data from three subjects showed that similar connectivity fluctuations found across patients were mainly coupled to the local activity of brain areas involved in face information processing. In particular, our findings provide preliminary evidence that the reported global measures might be a novel signature of functional brain activity reorganization when a stimulus is processed in a task context regardless of the specific recorded areas.

Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms

Introduction

Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.

Results

We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.

Conclusion

We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.

A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy

Stereotactic electroencephalography (SEEG) is an increasingly utilized method for invasive monitoring in patients with medically intractable epilepsy. Yet, the lack of standardization for labeling electrodes hinders communication among clinicians. A rational clustering of contacts based on anatomy rather than arbitrary physical leads may help clinical neurophysiologists interpret seizure networks. We identified SEEG electrodes on post‐implant CTs and registered them to preoperative MRIs segmented according to an anatomical atlas. Individual contacts were automatically assigned to anatomical areas independent of lead. These contacts were then organized using a hierarchical anatomical schema for display and interpretation. Bipolar‐referenced signal cross‐correlations were used to compare the similarity of grouped signals within a conventional montage versus this anatomical montage. As a result, we developed a hierarchical organization for SEEG contacts using well‐accepted, free software that is based solely on their post‐implant anatomical location. When applied to three example SEEG cases for epilepsy, clusters of contacts that were anatomically related collapsed into standardized groups. Qualitatively, seizure events organized using this framework were better visually clustered compared to conventional schemes. Quantitatively, signals grouped by anatomical region were more similar to each other than electrode‐based groups as measured by Pearson correlation. Further, we uploaded visualizations of SEEG reconstructions into the electronic medical record, rendering them durably useful given the interpretable electrode labels. In conclusion, we demonstrate a standardized, anatomically grounded approach to the organization of SEEG neuroimaging and electrophysiology data that may enable improved communication among and across surgical epilepsy teams and promote a clearer view of individual seizure networks.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas.

Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys.

The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes.

In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.

Tracing in vivo the dorsal loop of the optic radiation: convergent perspectives from tractography and electrophysiology compared to a neuroanatomical ground truth

The temporo-parietal junction (TPJ) is a cortical area contributing to a multiplicity of visual, language-related, and cognitive functions. In line with this functional richness, also the organization of the underlying white matter is highly complex and includes several bundles. The few studies tackling the outcome and neurological burdens of surgical operations addressing TPJ document the presence of language disturbances and visual field damages, with the latter hardly recovered in time. This observation advocates for identifying and functionally monitoring the optic radiation (OR) bundles that cross the white matter below the TPJ. In the present study, we adopted a multimodal approach to address the anatomo-functional correlates of the OR's dorsal loop. In particular, we combined cadavers' dissection with tractographic and electrophysiological data collected in drug-resistant epileptic patients explored by stereoelectroencephalography (SEEG). Cadaver dissection allowed us to appreciate the course and topography of the dorsal loop. More surprisingly, both tractographic and electrophysiological observations converged on a unitary picture highly coherent with the data obtained by neuroanatomical observation. The combination of diverse and multimodal observations allows overcoming the limitations intrinsic to single methodologies, defining a unitary picture which makes it possible to investigate the dorsal loop both presurgically and at the individual patient level, ultimately contributing to limit the postsurgical damages. Notwithstanding, such a combined approach could serve as a model of investigation for future neuroanatomical inquiries tackling white matter fibers anatomy and function through SEEG-derived neurophysiological data.

FreeSurfer and 3D Slicer-Assisted SEEG Implantation for Drug-Resistant Epilepsy

Objective

Our study aimed to develop an approach to improve the speed and resolution of cerebral-hemisphere and lesion modeling and evaluate the advantages and disadvantages of robot-assisted surgical planning software.

Methods

We applied both conventional robot planning software (method 1) and open-source auxiliary software (FreeSurfer and 3D Slicer; method 2) to model the brain and lesions in 19 patients with drug-resistant epilepsy. The patients' mean age at implantation was 21.4 years (range, 6–52 years). Each patient received an average of 12 electrodes (range, 9–16) between May and November 2021. The electrode-implantation plan was designed based on the models established using the two methods. We statistically analyzed and compared the duration of designing the models and planning the implantation using these two methods and performed the surgeries with the implantation plan designed using the auxiliary software.

Results

A significantly longer time was needed to reconstruct a cerebral-hemisphere model using method 1 (mean, 206 s) than using method 2 (mean, 20 s) (p < 0.05). Both methods identified a mean of 1.4 lesions (range, 1–5) in each patient. Overall, using method 1 required longer (mean, 130 s; range, 48–436) than using method 2 (mean, 68.1 s; range, 50–104; p < 0.05). In addition, the clarity of the model based on method 1 was lower than that based on method 2. To devise an electrode-implantation plan, it took 9.1–25.5 min (mean, 16) and 6.6–14.8 min (mean, 10.2) based on methods 1 and 2, respectively (p < 0.05). The average target point error of 231 electrodes amounted to 1.90 mm ± 0.37 mm (range, 0.33–3.61 mm). The average entry point error was 0.89 ± 0.26 mm (range, 0.17–1.67 mm). None of the patients presented with intracranial hemorrhage or infection, and no other serious complications were observed.

Conclusions

FreeSurfer and 3D Slicer-assisted SEEG implantation is an excellent approach to enhance modeling speed and resolution, shorten the electrode-implantation planning time, and boost the efficiency of clinical work. These well-known, trusted open-source programs do not have explicitly restricted licenses. These tools, therefore, seem well suited for clinical-research applications under the premise of approval by an ethics committee, informed consent of the patient, and clinical judgment of the surgeon.

BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis

Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high spatial resolution within suspected epileptogenic zones. Neurosurgeons or technicians face the challenge of conducting a workflow of post-processing operations with the multimodal data (e.g., MRI, CT, and EEG) after the implantation surgery, such as brain surface reconstruction, electrode contact localization, and SEEG data analysis. Several software or toolboxes have been developed to take one or more steps in the workflow but without an end-to-end solution. In this study, we introduced BrainQuake, an open-source Python software for the SEEG spatiotemporal analysis, integrating modules and pipelines in surface reconstruction, electrode localization, seizure onset zone (SOZ) prediction based on ictal and interictal SEEG analysis, and final visualizations, each of which is highly automated with a user-friendly graphical user interface (GUI). BrainQuake also supports remote communications with a public server, which is facilitated with automated and standardized preprocessing pipelines, high-performance computing power, and data curation management to provide a time-saving and compatible platform for neurosurgeons and researchers.

LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

Intracerebral electrical stimulations of the temporal lobe: A stereoelectroencephalography study

The functional anatomy of the anteromesial portion of the temporal lobe and its involvement in epilepsy can be explored by means of intracerebral electrical stimulations. Here, we aimed to expand the knowledge of its physiological and pathophysiological symptoms by conducting the first large-sample systematic analysis of 1529 electrical stimulations of this anatomical region. We retrospectively analysed all clinical manifestations induced by intracerebral electrical stimulations in 173 patients with drug-resistant focal epilepsy with at least one electrode implanted in this area. We found that high-frequency stimulations were more likely to evoke electroclinical manifestations (p < .0001) and also provoked 'false positive' seizures. Multimodal symptoms were associated with EEG electrical modification (after discharge) (p < .0001). Visual symptoms were not associated with after discharge (p = .0002) and were mainly evoked by stimulation of the hippocampus (p = .009) and of the parahippocampal gyrus (p = .0212). 'False positive seizures' can be evoked by stimulation of the hippocampus, parahippocampal gyrus and amygdala, likely due to their intrinsic low epileptogenic threshold. Visual symptoms evoked in the hippocampus and parahippocampal gyrus, without EEG changes, are physiological symptoms and suggest involvement of these areas in the visual ventral stream. Our findings provide meaningful guidance in the interpretation of intracranial EEG studies of the temporal lobe.

fMRI-Based Effective Connectivity in Surgical Remediable Epilepsies: A Pilot Study

Simultaneous EEG-fMRI can contribute to identify the epileptogenic zone (EZ) in focal epilepsies. However, fMRI maps related to Interictal Epileptiform Discharges (IED) commonly show multiple regions of signal change rather than focal ones. Dynamic causal modeling (DCM) can estimate effective connectivity, i.e. the causal effects exerted by one brain region over another, based on fMRI data. Here, we employed DCM on fMRI data in 10 focal epilepsy patients with multiple IED-related regions of BOLD signal change, to test whether this approach can help the localization process of EZ. For each subject, a family of competing deterministic, plausible DCM models were constructed using IED as autonomous input at each node, one at time. The DCM findings were compared to the presurgical evaluation results and classified as: "Concordant" if the node identified by DCM matches the presumed focus, "Discordant" if the node is distant from the presumed focus, or "Inconclusive" (no statistically significant result). Furthermore, patients who subsequently underwent intracranial EEG recordings or surgery were considered as having an independent validation of DCM results. The effective connectivity focus identified using DCM was Concordant in 7 patients, Discordant in two cases and Inconclusive in one. In four of the 6 patients operated, the DCM findings were validated. Notably, the two Discordant and Invalidated results were found in patients with poor surgical outcome. Our findings provide preliminary evidence to support the applicability of DCM on fMRI data to investigate the epileptic networks in focal epilepsy and, particularly, to identify the EZ in complex cases.

SYLVIUS: A multimodal and multidisciplinary platform for epilepsy surgery

BACKGROUND AND OBJECTIVE

We present SYLVIUS, a software platform intended to facilitate and improve the complex workflow required to diagnose and surgically treat drug-resistant epilepsies. In complex epilepsies, additional invasive information from exploration with stereoencephalography (SEEG) with deep electrodes may be needed, for which the input from different diagnostic methods and clinicians from several specialties is required to ensure diagnostic efficacy and surgical safety. We aim to provide a software platform with optimal data flow among the different stages of epilepsy surgery to provide smooth and integrated decision making.

METHODS

The SYLVIUS platform provides a clinical workflow designed to ensure seamless and safe patient data sharing across specialities. It integrates tools for stereo visualization, data registration, transfer of electrode plans referred to distinct datasets, automated postoperative contact segmentation, and novel DWI tractography analysis. Nineteen cases were retrospectively evaluated to track modifications from an initial plan to obtain a final surgical plan, using SYLVIUS.

RESULTS

The software was used to modify trajectories in all 19 consulted cases, which were then imported into the robotic system for the surgical intervention. When available, SYLVIUS provided extra multimodal information, which resulted in a greater number of trajectory modifications.

CONCLUSIONS

The architecture presented in this paper streamlines epilepsy surgery allowing clinicians to have a digital clinical tool that allows recording of the different stages of the procedure, in a common multimodal 2D/3D setting for participation of different clinicians in defining and validating surgical plans for SEEG cases.

Distributed source modeling of intracranial stereoelectro-encephalographic measurements

Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈-3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.

Postoperative intracerebral haematomas following stereotactic biopsies: Poor planning or poor execution?

BACKGROUND

Postoperative intracerebral haematomas represent a serious complication following stereotactic biopsy. We investigated the possible underlying causes - poor planning or poor execution - of postoperative intracerebral haematomas following stereotactic biopsies.

METHODS

We performed a technical investigation using a retrospective single-centre consecutive series of robot-assisted stereotactic biopsies for a supratentorial diffuse glioma in adults. Each actual biopsy trajectory was reviewed to search for a conflict with an anatomical structure at risk.

RESULTS

From 379 patients, 12 (3.2%) presented with a postoperative intracerebral haematoma ≥20 mm on postoperative CT-scan (3 requiring surgical evacuation); 11 of them had available intraoperative imaging (bi-planar stereoscopic teleangiography x-rays at each biopsy site). The actual biopsy trajectory was similar to the planned biopsy trajectory in these 11 cases. In 72.7% (8/11) of these cases, the actual biopsy trajectory was found to contact a structure at risk (blood vessel and cerebral sulcus) and identified as the intracerebral haematoma origin.

CONCLUSIONS

Robot-assisted stereotactic biopsy is an accurate procedure. Postoperative intracerebral haematomas mainly derive from human-related errors during trajectory planning.

Classification of Stereo-EEG Contacts in White Matter vs. Gray Matter Using Recorded Activity

For epileptic patients requiring resective surgery, a modality called stereo-electroencephalography (SEEG) may be used to monitor the patient's brain signals to help identify epileptogenic regions that generate and propagate seizures. SEEG involves the insertion of multiple depth electrodes into the patient's brain, each with 10 or more recording contacts along its length. However, a significant fraction (≈ 30% or more) of the contacts typically reside in white matter or other areas of the brain which can not be epileptogenic themselves. Thus, an important step in the analysis of SEEG recordings is distinguishing between electrode contacts which reside in gray matter vs. those that do not. MRI images overlaid with CT scans are currently used for this task, but they take significant amounts of time to manually annotate, and even then it may be difficult to determine the status of some contacts. In this paper we present a fast, automated method for classifying contacts in gray vs. white matter based only on the recorded signal and relative contact depth. We observe that bipolar referenced contacts in white matter have less power in all frequencies below 150 Hz than contacts in gray matter, which we use in a Bayesian classifier to attain an average area under the receiver operating characteristic curve of 0.85 ± 0.079 (SD) across 29 patients. Because our method gives a probability for each contact rather than a hard labeling, and uses a feature of the recorded signal that has direct clinical relevance, it can be useful to supplement decision-making on difficult to classify contacts or as a rapid, first-pass filter when choosing subsets of contacts from which to save recordings.

Robot-assisted versus stereotactic frame-based stereoelectroencephalography in medically refractory epilepsy

AIM

To explore the difference between robot assisted (RA) and stereotactic frame based (SF) stereoelectroencephalography (SEEG) in patients with medically refractory epilepsy.

METHODS

We undertook a retrospective review of 33 SEEG cases at our center, of which 14 were SF performed from March to October 2018 and 19 were RA performed from November 2018 to December 2019. Detailed review of medical histories and operative records as well as imaging and trajectory plans was carried out for each patient, and the results related to each technique compared. A multiple linear regression model was used to test for variables that significantly influenced placement error.

RESULTS

Compared to the SF group, the RA group had a higher mean number of electrodes per patient (10.7 ± 2.8 versus 6.4 ± 0.8, P < 0.0001) and a significantly shorter mean operative time (127.3 ± 40.7 versus 152.7 ± 13.6 min, P = 0.033). For the RA group, the intracranial implantation length was positively correlated with target point error (p = 0.000), depth error (p = 0.043), and two-dimensional (2D) radial error (p = 0.041). Conversely, skull thickness was negatively correlated with the TP error (p = 0.004), depth error (p = 0.037) and 2D radial error (p = 0.000). We also analyzed the mean entry point, target point, depth and 2D radial errors, the complication rates, and the results of epileptogenic zone (EZ) localization and Engel class. The results showed no difference in these aspects between the SF group and the RA group.

CONCLUSION

This study suggests that, compared to stereotactic frame based SEEG, robot assisted SEEG is significantly more efficient and comparable in safety and effectiveness.

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

Objective

This retrospective, cross‐sectional study evaluated the feasibility and potential benefits of incorporating deep‐learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug‐resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG.

Methods

A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross‐validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier‐predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier‐predicted lesions was considered colocalization.

Results

In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity = 100%). Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. The algorithm correctly detected seven of eight of these FCDs (86%). In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted.

Significance

There was a high degree of colocalization between automated lesion detection and sEEG. We have created a framework for incorporation of deep‐learning–based MRI lesion detection into sEEG implantation planning. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories.

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase–amplitude coupling (PAC) or by n:m-cross–frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

Genuine interareal cross-frequency coupling (CFC) can be identified from human resting state activity using magnetoencephalography, stereoelectroencephalography, and novel network approaches. CFC couples slow theta and alpha oscillations to faster oscillations across brain regions.

EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss

Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because such algorithms only aim to minimize the temporal mean-squared-error (MSE) under generic penalties. Instead of using temporal MSE according to conventional mathematical models, this paper introduces a novel reconstruction algorithm based on generative adversarial networks with the Wasserstein distance (WGAN) and a temporal-spatial-frequency (TSF-MSE) loss function. The carefully designed TSF-MSE-based loss function reconstructs signals by computing the MSE from time-series features, common spatial pattern features, and power spectral density features. Promising reconstruction and classification results are obtained from three motor-related EEG signal datasets with different sampling rates and sensitivities. Our proposed method significantly improves classification performances of EEG signals reconstructions with the same sensitivity and the average classification accuracy improvements of EEG signals reconstruction with different sensitivities. By introducing the WGAN reconstruction model with TSF-MSE loss function, the proposed method is beneficial for the requirements of high classification performance and low cost and is convenient for the design of high-performance brain computer interface systems.

Simultaneous human intracerebral stimulation and HD-EEG, ground-truth for source localization methods

Precisely localizing the sources of brain activity as recorded by EEG is a fundamental procedure and a major challenge for both research and clinical practice. Even though many methods and algorithms have been proposed, their relative advantages and limitations are still not well established. Moreover, these methods involve tuning multiple parameters, for which no principled way of selection exists yet. These uncertainties are emphasized due to the lack of ground-truth for their validation and testing. Here we present the Localize-MI dataset, which constitutes the first open dataset that comprises EEG recorded electrical activity originating from precisely known locations inside the brain of living humans. High-density EEG was recorded as single-pulse biphasic currents were delivered at intensities ranging from 0.1 to 5 mA through stereotactically implanted electrodes in diverse brain regions during pre-surgical evaluation of patients with drug-resistant epilepsy. The uses of this dataset range from the estimation of in vivo tissue conductivity to the development, validation and testing of forward and inverse solution methods.

iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes

OBJECTIVE

The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes.

APPROACH

iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment.

MAIN RESULTS

All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes.

SIGNIFICANCE

iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.

Stereoelectroencephalography: retrospective analysis of 742 procedures in a single centre

This retrospective description of a surgical series is aimed at reporting on indications, methodology, results on seizures, outcome predictors and complications from a 20-year stereoelectroencephalography (SEEG) activity performed at a single epilepsy surgery centre. Prospectively collected data from a consecutive series of 742 SEEG procedures carried out on 713 patients were reviewed and described. Long-term seizure outcome of SEEG-guided resections was defined as a binomial variable: absence (ILAE classes 1–2) or recurrence (ILAE classes 3–6) of disabling seizures. Predictors of seizure outcome were analysed by preliminary uni/bivariate analyses followed by multivariate logistic regression. Furthermore, results on seizures of these subjects were compared with those obtained in 1128 patients operated on after only non-invasive evaluation. Survival analyses were also carried out, limited to patients with a minimum follow-up of 10 years. Resective surgery has been indicated for 570 patients (79.9%). Two-hundred and seventy-nine of 470 patients operated on (59.4%) were free of disabling seizures at least 2 years after resective surgery. Negative magnetic resonance and post-surgical lesion remnant were significant risk factors for seizure recurrence, while type II focal cortical dysplasia, balloon cells, glioneuronal tumours, hippocampal sclerosis, older age at epilepsy onset and periventricular nodular heterotopy were significantly associated with seizure freedom. Twenty-five of 153 patients who underwent radio-frequency thermal coagulation (16.3%) were optimal responders. Thirteen of 742 (1.8%) procedures were complicated by unexpected events, including three (0.4%) major complications and one fatality (0.1%). In conclusion, SEEG is a safe and efficient methodology for invasive definition of the epileptogenic zone in the most challenging patients. Despite the progressive increase of MRI-negative cases, the proportion of seizure-free patients did not decrease throughout the years.

Three-dimensional neuronavigation in SEEG-guided epilepsy surgery

OBJECTIVES

Epilepsy surgery is mainly cortical surgery and the precise definition of the epileptogenic zone on the complex cortical surface is of paramount importance. Stereoelectroencephalography (SEEG) may delineate the epileptogenic zone even in cases of non-lesional epilepsy. The aim of our study was to present a technique of 3D neuronavigation based on the brain surface and SEEG electrodes reconstructions using FSL and 3DSlicer software.

PATIENTS AND METHODS

Our study included 26 consecutive patients operated on for drug-resistant epilepsy after SEEG exploration between January 2015 and December 2017. All patients underwent 1.5 T pre-SEEG MRI, post-SEEG CT, DICOM data post-processing using FSL and 3DSlicer, preoperative planning on 3DSlicer, and intraoperative 3D neuronavigation. Accuracy and precision of 3D SEEG reconstruction and 3D neuronavigation was assessed.

RESULTS

We identified 125 entry points of SEEG electrodes during 26 operations. The accuracy of 3D reconstruction was 0.8 mm (range, 0-2 mm) with a precision of 1.5 mm. The accuracy of 3D SEEG neuronavigation was 2.68 mm (range, 0-6 mm). The precision of 3D neuronavigation was 1.48 mm.

CONCLUSION

3D neuronavigation for SEEG-guided epilepsy surgery using free software for post-processing of common MRI sequences is possible and a reliable method even with navigation systems without a brain extraction tool.

Effective accuracy of stereoelectroencephalography: robotic 3D versus Talairach orthogonal approaches

OBJECTIVE

Stereoelectroencephalography (SEEG) was first developed in the 1950s by Jean Talairach using 2D angiography and a frame-based, orthogonal approach through a metallic grid. Since then, various other frame-based and frameless techniques have been described. In this study the authors sought to compare the traditional orthogonal Talairach 2D angiographic approach with a frame-based 3D robotic procedure that included 3D angiographic interoperative imaging guidance. MRI was used for both procedures during surgery, but MRI preplanning was done only in the robotic 3D technique.

METHODS

All study patients suffered from drug-resistant focal epilepsy and were treated at the same center by the same neurosurgical team. Fifty patients who underwent the 3D robotic procedure were compared to the same number of historical controls who had previously been successfully treated with the Talairach orthogonal procedure. The effectiveness and absolute accuracy, as well as safety, of the two procedures were compared. Moreover, in the 3D robotic group, the reliability of the preoperative MRI to avoid vascular structures was evaluated by studying the rate of trajectory modification following the coregistration of the intraoperative 3D angiographic data onto the preoperative MRI-based trajectory plans.

RESULTS

Effective accuracy (96.5% vs 13.7%) and absolute accuracy (1.15 mm vs 4.00 mm) were significantly higher in the 3D robotic group than in the Talairach orthogonal group. Both procedures showed excellent safety results (no major complications). The rate of electrode modification after 3D angiography was 43.8%, and it was highest for frontal and insular locations.

CONCLUSIONS

The frame-based, 3D angiographic, robotic procedure described here provided better accuracy for SEEG implantations than the traditional Talairach approach. This study also highlights the potential safety advantage of trajectory planning using intraoperative frame-based 3D angiography over preoperative MRI alone.

Experience-based SEEG planning: from retrospective data to automated electrode trajectories suggestions

StereoElectroEncephaloGraphy (SEEG) is a minimally invasive technique that consists of the insertion of multiple intracranial electrodes to precisely identify the epileptogenic focus. The planning of electrode trajectories is a cumbersome and time-consuming task. Current approaches to support the planning focus on electrode trajectory optimisation based on geometrical constraints but are not helpful to produce an initial electrode set to begin with the planning procedure. In this work, the authors propose a methodology that analyses retrospective planning data and builds a set of average trajectories, representing the practice of a clinical centre, which can be mapped to a new patient to initialise planning procedure. They collected and analysed the data from 75 anonymised patients, obtaining 30 exploratory patterns and 61 mean trajectories in an average brain space. A preliminary validation on a test set showed that they were able to correctly map 90% of those trajectories and, after optimisation, they have comparable or better values than manual trajectories in terms of distance from vessels and insertion angle. Finally, by detecting and analysing similar plans, they were able to identify eight planning strategies, which represent the main tailored sets of trajectories that neurosurgeons used to deal with the different patient cases.

IntrAnat Electrodes: A Free Database and Visualization Software for Intracranial Electroencephalographic Data Processed for Case and Group Studies

In some cases of pharmaco-resistant and focal epilepsies, intracranial recordings performed epidurally (electrocorticography, ECoG) and/or in depth (stereoelectroencephalography, SEEG) can be required to locate the seizure onset zone and the eloquent cortex before surgical resection. In SEEG, each electrode contact records brain’s electrical activity in a spherical volume of 3 mm diameter approximately. The spatial coverage is around 1% of the brain and differs between patients because the implantation of electrodes is tailored for each case. Group studies thus need a large number of patients to reach a large spatial sampling, which can be achieved more easily using a multicentric approach such as implemented in our F-TRACT project (f-tract.eu). To facilitate group studies, we developed a software—IntrAnat Electrodes—that allows to perform virtual electrode implantation in patients’ neuroanatomy and to overlay results of epileptic and functional mapping, as well as resection masks from the surgery. IntrAnat Electrodes is based on a patient database providing multiple search criteria to highlight various group features. For each patient, the anatomical processing is based on a series of software publicly available. Imaging modalities (Positron Emission Tomography (PET), anatomical MRI pre-implantation, post-implantation and post-resection, functional MRI, diffusion MRI, Computed Tomography (CT) with electrodes) are coregistered. The 3D T1 pre-implantation MRI gray/white matter is segmented and spatially normalized to obtain a series of cortical parcels using different neuroanatomical atlases. On post-implantation images, the user can position 3D models of electrodes defined by their geometry. Each electrode contact is then labeled according to its position in the anatomical atlases, to the class of tissue (gray or white matter, cerebro-spinal fluid) and to its presence inside or outside the resection mask. Users can add more functionally informed labels on contact, such as clinical responses after electrical stimulation, cortico-cortical evoked potentials, gamma band activity during cognitive tasks or epileptogenicity. IntrAnat Electrodes software thus provides a means to visualize multimodal data. The contact labels allow to search for patients in the database according to multiple criteria representing almost all available data, which is to our knowledge unique in current SEEG software. IntrAnat Electrodes will be available in the forthcoming release of BrainVisa software and tutorials can be found on the F-TRACT webpage.

Automatic segmentation of stereoelectroencephalography (SEEG) electrodes post-implantation considering bending

Purpose

The accurate and automatic localisation of SEEG electrodes is crucial for determining the location of epileptic seizure onset. We propose an algorithm for the automatic segmentation of electrode bolts and contacts that accounts for electrode bending in relation to regional brain anatomy.

Methods

Co-registered post-implantation CT, pre-implantation MRI, and brain parcellation images are used to create regions of interest to automatically segment bolts and contacts. Contact search strategy is based on the direction of the bolt with distance and angle constraints, in addition to post-processing steps that assign remaining contacts and predict contact position. We measured the accuracy of contact position, bolt angle, and anatomical region at the tip of the electrode in 23 post-SEEG cases comprising two different surgical approaches when placing a guiding stylet close to and far from target point. Local and global bending are computed when modelling electrodes as elastic rods.

Results

Our approach executed on average in 36.17 s with a sensitivity of 98.81% and a positive predictive value (PPV) of 95.01%. Compared to manual segmentation, the position of contacts had a mean absolute error of 0.38 mm and the mean bolt angle difference of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.59^{\circ }$$\end{document}0.59∘ resulted in a mean displacement error of 0.68 mm at the tip of the electrode. Anatomical regions at the tip of the electrode were in strong concordance with those selected manually by neurosurgeons, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ICC(3,k)=0.76$$\end{document}ICC(3,k)=0.76, with average distance between regions of 0.82 mm when in disagreement. Our approach performed equally in two surgical approaches regardless of the amount of electrode bending.

Conclusion

We present a method robust to electrode bending that can accurately segment contact positions and bolt orientation. The techniques presented in this paper will allow further characterisation of bending within different brain regions.

Electronic supplementary material

The online version of this article (10.1007/s11548-018-1740-8) contains supplementary material, which is available to authorized users.

Spatiotemporal evolution of focal epileptiform activity from surface and laminar field recordings in cat neocortex

New devices that use targeted electrical stimulation to treat refractory localization-related epilepsy have shown great promise, although it is not well known which targets most effectively prevent the initiation and spread of seizures. To better understand how the brain transitions from healthy to seizing on a local scale, we induced focal epileptiform activity in the visual cortex of five anesthetized cats with local application of the GABA_A blocker picrotoxin while simultaneously recording local field potentials on a high-resolution electrocorticography array and laminar depth probes. Epileptiform activity appeared in the form of isolated events, revealing a consistent temporal pattern of ictogenesis across animals with interictal events consistently preceding the appearance of seizures. Based on the number of spikes per event, there was a natural separation between seizures and shorter interictal events. Two distinct spatial regions were seen: an epileptic focus that grew in size as activity progressed, and an inhibitory surround that exhibited a distinct relationship with the focus both on the surface and in the depth of the cortex. Epileptiform activity in the cortical laminae was seen concomitant with activity on the surface. Focus spikes appeared earlier on electrodes deeper in the cortex, suggesting that deep cortical layers may be integral to recruiting healthy tissue into the epileptic network and could be a promising target for interventional devices. Our study may inform more effective therapies to prevent seizure generation and spread in localization-related epilepsies. NEW & NOTEWORTHY We induced local epileptiform activity and recorded continuous, high-resolution local field potentials from the surface and depth of the visual cortex in anesthetized cats. Our results reveal a consistent pattern of ictogenesis, characterize the spatial spread of the epileptic focus and its relationship with the inhibitory surround, and show that focus activity within events appears earliest in deeper cortical layers. These findings have potential implications for the monitoring and treatment of refractory epilepsy.

Kurtosis-Based Detection of Intracranial High-Frequency Oscillations for the Identification of the Seizure Onset Zone

Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time-frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80-250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.