Suppression of cortical electrostimulation artifacts using pre-whitening and null projection.

Objective.Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods.Approach.We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects.Main results.In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%-80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement.Significance.PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

Intracranial intraoperative radiotherapy (IORT): evaluation of electrocorticography and peri-operative seizure risk.

BACKGROUND: Intra-operative radiotherapy (IORT) for brain metastases (BMs) and primary brain tumors has emerged as an adjuvant radiation modality that allows for consolidation of care into a single anesthetic episode with surgical resection. Yet, there is a paucity of data regarding the impact that IORT may have on peri-operative and long-term seizure risk. METHODS: A retrospective analysis of patients receiving IORT during tumor resection was performed via registry including data regarding peri-operative anti-seizure medications and anesthetic agents. Intra-operative neuromonitoring was performed using electrocorticography (ECoG) captured before-, during-, and after-IORT then analyzed for evidence of seizure or significant baseline changes. Kaplan-Meir estimations were used for overall survival analysis relative to documented clinical seizure incidence post-IORT. RESULTS: Of the 24 consecutive patients treated with IORT during tumor resection included, 18 (75%) patients were diagnosed with BMs while 6 (25%) had newly-diagnosed glioblastoma. Mean and median survival times were 487 and 372 days, respectively. Clinical seizures occurred in 3 patients post-IORT, 2 BMs patients within 9 months and 1 glioblastoma patient at 14 months. IORT time represented 9.5% of anesthetic time. ECoG recordings were available for 5 patients (4 BMs; 1 glioblastoma), with mean recording durations of 13% of the total anesthetic time and no evidence of high-frequency oscillations or seizure activity. CONCLUSIONS: IORT is an option for delivery of definitive radiation in surgically resected brain tumors without increasing the peri-operative or long-term risk of seizure. ECoG data during the delivery of radiation fail to demonstrate any electrophysiological changes in response to ionizing radiation.

Computational modeling allows unsupervised classification of epileptic brain states across species.

Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.

Motor seizure semiology.

Motor semiology is a major component of epilepsy evaluation, which provides essential information on seizure classification and helps in seizure localization. The typical motor seizures include tonic, clonic, tonic-clonic, myoclonic, atonic, epileptic spasms, automatisms, and hyperkinetic seizures. Compared to the "positive" motor signs, negative motor phenomena, for example, atonic seizures and Todd's paralysis are also crucial in seizure analysis. Several motor signs, for example, version, unilateral dystonia, figure 4 sign, M2e sign, and asymmetric clonic ending, are commonly observed and have significant clinical value in seizure localization. The purpose of this chapter is to review the localization value and pathophysiology associated with the well-defined motor seizure semiology using updated knowledge from intracranial electroencephalographic recordings, particularly stereoelectroencephalography.

Automated sleep classification with chronic neural implants in freely behaving canines.

Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.

Functional geometry of auditory cortical resting state networks derived from intracranial electrophysiology.

Understanding central auditory processing critically depends on defining underlying auditory cortical networks and their relationship to the rest of the brain. We addressed these questions using resting state functional connectivity derived from human intracranial electroencephalography. Mapping recording sites into a low-dimensional space where proximity represents functional similarity revealed a hierarchical organization. At a fine scale, a group of auditory cortical regions excluded several higher-order auditory areas and segregated maximally from the prefrontal cortex. On mesoscale, the proximity of limbic structures to the auditory cortex suggested a limbic stream that parallels the classically described ventral and dorsal auditory processing streams. Identities of global hubs in anterior temporal and cingulate cortex depended on frequency band, consistent with diverse roles in semantic and cognitive processing. On a macroscale, observed hemispheric asymmetries were not specific for speech and language networks. This approach can be applied to multivariate brain data with respect to development, behavior, and disorders.

High gamma coherence between task-responsive sensory-motor cortical regions in a motor reaction-time task.

Electrical activity at high gamma frequencies (70-170 Hz) is thought to reflect the activity of small cortical ensembles. For example, high gamma activity (often quantified by spectral power) can increase in sensory-motor cortex in response to sensory stimuli or movement. On the other hand, synchrony of neural activity between cortical areas (often quantified by coherence) has been hypothesized as an important mechanism for inter-areal communication, thereby serving functional roles in cognition and behavior. Currently, high gamma activity has primarily been studied as a local amplitude phenomenon. We investigated the synchronization of high gamma activity within sensory-motor cortex and the extent to which underlying high gamma activity can explain coherence during motor tasks. We characterized high gamma coherence in sensory-motor networks and the relationship between coherence and power by analyzing electrocorticography (ECoG) data from human subjects as they performed a motor response to sensory cues. We found greatly increased high gamma coherence during the motor response compared with the sensory cue. High gamma power poorly predicted high gamma coherence, but the two shared a similar time course. However, high gamma coherence persisted longer than high gamma power. The results of this study suggest that high gamma coherence is a physiologically distinct phenomenon during a sensory-motor task, the emergence of which may require active task participation.NEW & NOTEWORTHY Motor action after auditory stimulus elicits high gamma responses in sensory-motor and auditory cortex, respectively. We show that high gamma coherence reliably and greatly increased during motor response, but not after auditory stimulus. Underlying high gamma power could not explain high gamma coherence. Our results indicate that high gamma coherence is a physiologically distinct sensory-motor phenomenon that may serve as an indicator of increased synaptic communication on short timescales (∼1 s).

Electrical Source Imaging of Somatosensory Evoked Potentials from Intracranial EEG Signals.

Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.

Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery.

BACKGROUND AND OBJECTIVES: Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain's structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship affects the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control after surgery. METHODS: We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using presurgery functional data from intracranial EEG (iEEG) recordings, presurgery and postsurgery structural data from T1-weighted MRI, and presurgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using the Spearman rank correlation and analyzed this structure-function coupling at 2 spatial scales: (1) global iEEG network level and (2) individual iEEG electrode contacts using virtual surgeries. We retrospectively predicted postoperative seizure freedom by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model. RESULTS: We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free patients compared with those with seizure recurrence (p = 0.002, d = 0.76, area under the receiver operating characteristic curve [AUC] = 0.78 [95% CI 0.62-0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p = 0.007, d = 0.96, AUC = 0.73 [95% CI 0.58-0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 (95% CI 0.67-0.94). These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 (95% CI 0.82-1.0), accuracy of 92%, sensitivity of 93%, and specificity of 91%. DISCUSSION: Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into presurgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery. CLASSIFICATION OF EVIDENCE: This is a Class IV retrospective case series showing that structure-function mapping may help determine the outcome from surgical resection for treatment-resistant focal epilepsy.

Spreading Depolarizations Suppress Hematoma Growth in Hyperacute Intracerebral Hemorrhage in Mice.

BACKGROUND: Spreading depolarizations (SDs) occur in all types of brain injury and may be associated with detrimental effects in ischemic stroke and subarachnoid hemorrhage. While rapid hematoma growth during intracerebral hemorrhage triggers SDs, their role in intracerebral hemorrhage is unknown. METHODS: We used intrinsic optical signal and laser speckle imaging, combined with electrocorticography, to investigate the effects of SD on hematoma growth during the hyperacute phase (0-4 hours) after intracortical collagenase injection in mice. Hematoma expansion, SDs, and cerebral blood flow were simultaneously monitored under normotensive and hypertensive conditions. RESULTS: Spontaneous SDs erupted from the vicinity of the hematoma during rapid hematoma growth. We found that hematoma growth slowed down by >60% immediately after an SD. This effect was even stronger in hypertensive animals with faster hematoma growth. To establish causation, we exogenously induced SDs (every 30 minutes) at a remote site by topical potassium chloride application and found reduced hematoma growth rate and final hemorrhage volume (18.2±5.8 versus 10.7±4.1 mm3). Analysis of cerebral blood flow using laser speckle flowmetry revealed that suppression of hematoma growth by spontaneous or induced SDs coincided and correlated with the characteristic oligemia in the wake of SD, implicating the vasoconstrictive effect of SD as one potential mechanism of action. CONCLUSIONS: Our findings reveal that SDs limit hematoma growth during the early hours of intracerebral hemorrhage and decrease final hematoma volume.

ECoG spiking activity and signal dimension are early predictive measures of epileptogenesis in a translational mouse model of traumatic brain injury.

The latency between traumatic brain injury (TBI) and the onset of epilepsy (PTE) represents an opportunity for counteracting epileptogenesis. Antiepileptogenesis trials are hampered by the lack of sensitive biomarkers that allow to enrich patient's population at-risk for PTE. We aimed to assess whether specific ECoG signals predict PTE in a clinically relevant mouse model with ∼60% epilepsy incidence. TBI was provoked in adult CD1 male mice by controlled cortical impact on the left parieto-temporal cortex, then mice were implanted with two perilesional cortical screw electrodes and two similar electrodes in the hemisphere contralateral to the lesion site. Acute seizures and spikes/sharp waves were ECoG-recorded during 1 week post-TBI. These early ECoG events were analyzed according to PTE incidence as assessed by measuring spontaneous recurrent seizures (SRS) at 5 months post-TBI. We found that incidence, number and duration of acute seizures during 3 days post-TBI were similar in PTE mice and mice not developing epilepsy (No SRS mice). Control mice with cortical electrodes (naïve, n = 5) or with electrodes and craniotomy (sham, n = 5) exhibited acute seizures but did not develop epilepsy. The daily number of spikes/sharp waves at the perilesional electrodes was increased similarly in PTE (n = 15) and No SRS (n = 8) mice vs controls (p < 0.05, n = 10) from day 2 post-injury. Differently, the daily number of spikes/sharp waves at both contralateral electrodes showed a progressive increase in PTE mice vs No SRS and control mice. In particular, spikes number was higher in PTE vs No SRS mice (p < 0.05) at 6 and 7 days post-TBI, and this measure predicted epilepsy development with high accuracy (AUC = 0.77, p = 0.03; CI 0.5830-0.9670). The cut-off value was validated in an independent cohort of TBI mice (n = 12). The daily spike number at the contralateral electrodes showed a circadian distribution in PTE mice which was not observed in No SRS mice. Analysis of non-linear dynamics at each electrode site showed changes in dimensionality during 4 days post-TBI. This measure yielded the best discrimination between PTE and No SRS mice (p < 0.01) at the cortical electrodes contralateral to injury. Data show that epileptiform activity contralateral to the lesion site has the the highest predictive value for PTE in this model reinforcing the hypothesis that the hemisphere contralateral to the lesion core may drive epileptogenic networks after TBI.

Normative brain mapping using scalp EEG and potential clinical application.

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.

The seizure severity score: a quantitative tool for comparing seizures and their response to therapy.

Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.

Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.

Objective.Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field.Approach.In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task.Main results.We show that (1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; (2) individual word decoding in reconstructed speech achieves 92%-100% accuracy (chance level is 8%); (3) direct reconstruction from sensorimotor brain activity produces intelligible speech.Significance.These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG.

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Low impedance electrodes improve detection of high frequency oscillations in the intracranial EEG.

OBJECTIVE: Epileptic fast ripple oscillations (FR, 250-500 Hz) indicate epileptogenic tissue with high specificity. However, their low amplitude makes detection demanding against noise. Since thermal noise is reduced by low impedance electrodes (LoZ), we investigate here whether this noise reduction is relevant in the FR frequency range. METHODS: We analyzed intracranial electrocorticography during neurosurgery of 10 patients where a low impedance electrode was compared to a standard electrode (HiZ) with equal surface area during stimulation of the somatosensory evoked potential, which evokes a robust response in the FR frequency range. To estimate the noise level, we computed the difference between sweep 2n and sweep 2n + 1 for all sweeps. RESULTS: The power spectral density of the noise spectrum improved for the LoZ over all frequencies. In the FR range, the median noise level improved from HiZ (0.153 µV) to LoZ (0.089 µV). For evoked FR, the detection rate improved (91% for HiZ vs. 100% for LoZ). CONCLUSIONS: Low impedance electrodes for intracranial EEG reduce noise in the FR frequency range and may thereby improve FR detection. SIGNIFICANCE: Improving the measurement chain may enhance the diagnostic value of FR as biomarkers for epileptogenic tissue.

Timing of Allocentric and Egocentric Spatial Processing in Human Intracranial EEG.

Spatial reference frames (RFs) play a key role in spatial cognition, especially in perception, spatial memory, and navigation. There are two main types of RFs: egocentric (self-centered) and allocentric (object-centered). Although many fMRI studies examined the neural correlates of egocentric and allocentric RFs, they could not sample the fast temporal dynamics of the underlying cognitive processes. Therefore, the interaction and timing between these two RFs remain unclear. Taking advantage of the high temporal resolution of intracranial EEG (iEEG), we aimed to determine the timing of egocentric and allocentric information processing and describe the brain areas involved. We recorded iEEG and analyzed broad gamma activity (50-150 Hz) in 37 epilepsy patients performing a spatial judgment task in a three-dimensional circular virtual arena. We found overlapping activation for egocentric and allocentric RFs in many brain regions, with several additional egocentric- and allocentric-selective areas. In contrast to the egocentric responses, the allocentric responses peaked later than the control ones in frontal regions with overlapping selectivity. Also, across several egocentric or allocentric selective areas, the egocentric selectivity appeared earlier than the allocentric one. We identified the maximum number of egocentric-selective channels in the medial occipito-temporal region and allocentric-selective channels around the intraparietal sulcus in the parietal cortex. Our findings favor the hypothesis that egocentric spatial coding is a more primary process, and allocentric representations may be derived from egocentric ones. They also broaden the dominant view of the dorsal and ventral streams supporting egocentric and allocentric space coding, respectively.

Does seizure propagate within or across intrinsic brain networks? An intracranial EEG study.

BACKGROUND: Understanding the spatiotemporal propagation profiles of seizures is crucial for the preoperative assessment of epilepsy patients. The present study aimed to investigate whether seizures exhibit propagation patterns that align with intrinsic networks (INs). METHODS: A quantitative analysis was conducted to examine ictal fast activity (IFA). The Epileptogenicity Index (EI) was employed to assess the epileptogenicity, spectral features, and temporal characteristics of IFA. Intra-network and inter-network comparisons were made regarding the IFA-related metrics. Additionally, the metrics were correlated with Euclidean distance. Network connection maps were generated to visualize seizures originating from different INs, allowing for comparisons between distinct groups. RESULTS: Data for 81 seizures in 43 subjects were captured using stereoelectroencephalography implantation. Three metrics were compared: EI, time involvement (TI), and energy ratio index (ERI). Intra-network channels exhibited higher EI, earlier involvement of IFA, and stronger high-frequency energy. These findings were further validated through subgroup analyses stratified by neuropathology, seizure type, and seizure origination lobe. Correlation analyses revealed a negative association between distance and both EI and ERI, while distance exhibited a positive correlation with TI. Seizures originating from different INs exhibited varying propagation characteristics. CONCLUSIONS: The study findings highlight the dominant role of intra-network dynamics over inter-network during seizure propagation. These results contribute to our understanding of seizure dynamics and their relationship with INs.

Slow-wave modulation analysis during states of unconsciousness using the novel tau-modulation method.

Objective. Slow-wave modulation occurs during states of unconsciousness and is a large-scale indicator of underlying brain states. Conventional methods typically characterize these large-scale dynamics by assuming that slow-wave activity is sinusoidal with a stationary frequency. However, slow-wave activity typically has an irregular waveform shape with a non-stationary frequency, causing these methods to be highly unpredictable and inaccurate. To address these limitations, we developed a novel method using tau-modulation, which is more robust than conventional methods in estimating the modulation of slow-wave activity and does not require assumptions on the shape or stationarity of the underlying waveform.Approach. We propose a novel method to estimate modulatory effects on slow-wave activity. Tau-modulation curves are constructed from cross-correlation between slow-wave and high-frequency activity. The resultant curves capture several aspects of modulation, including attenuation or enhancement of slow-wave activity, the temporal synchrony between slow-wave and high-frequency activity, and the rate at which the overall brain activity oscillates between states.Main results. The method's performance was tested on an open electrocorticographic dataset from two monkeys that were recorded during propofol-induced anesthesia, with electrodes implanted over the left hemispheres. We found a robust propagation of slow-wave modulation along the anterior-posterior axis of the lateral aspect of the cortex. This propagation preferentially originated from the anterior superior temporal cortex and anterior cingulate gyrus. We also found the modulation frequency and polarity to track the stages of anesthesia. The algorithm performed well, even with non-sinusoidal activity and in the presence of real-world noise.Significance. The novel method provides new insight into several aspects of slow-wave modulation that have been previously difficult to evaluate across several brain states. This ability to better characterize slow-wave modulation, without spurious correlations induced by non-sinusoidal signals, may lead to robust and physiologically-plausible diagnostic tools for monitoring brain functions during states of unconsciousness.

Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes.

Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline's visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.