Efficacy and safety of stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RF-TC) in the treatment of paediatric drug-resistant epilepsy: A retrospective analysis.

INTRODUCTION: This investigation evaluates the effectiveness and safety of stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RF-TC) as a treatment modality for drug-resistant epilepsy. MATERIAL AND METHODS: A retrospective review of clinical data from 40 paediatric patients with drug-resistant epilepsy, who underwent SEEG-guided RF-TC at our Epilepsy Center between 2020 and 2022, was conducted. This review included the patients' medical history, imaging and electroencephalography results, surgical procedures, and follow-up outcomes. RESULTS: The duration of SEEG monitoring, accompanied by concurrent electrical stimulation tests, varied from 3 days to 4 weeks. Following RF-TC surgery, 4 patients demonstrated temporary neurological impairments, including central facial and tongue weakness, reduced limb strength, and challenges in fine motor hand movements. All these symptoms were related to lesions in the central region, but showed improvement within 2 weeks to 3 months post-surgery. There were no reported instances of status epilepticus, intracranial haemorrhage, or infections. During a follow-up period of 6 months to 2.5 years, seizure control was achieved in 25 patients (62.5%) at 6 months post-surgery, and a > 50% decrease in seizure frequency was observed in 10 patients. In 5 patients where seizure control was not achieved, the management of epilepsy seemed to be independent of factors such as age at surgery, duration of preoperative disease, seizure type, or negative MRI findings ( p > 0.05). Patients with controlled epilepsy exhibited cognitive improvement, with some demonstrating no EEG abnormalities upon follow-up and a decrease in antiepileptic medication. CONCLUSIONS: SEEG-guided RF-TC appears to be a potentially effective and safe therapeutic approach for paediatric patients with drug-resistant epilepsy.

Epileptic network identification: insights from Dynamic Mode Decomposition of sEEG data.

OBJECTIVE: For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial recordings to identify brain networks participating in early seizure organization and propagation (i.e., the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high dimensional sEEG data. We apply here an unsupervised data-driven algorithm, Dynamic Mode Decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free. APPROACH: DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures ("modes") defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce Dynamic Modal Maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the Higher-Frequency Mode-based Norm Index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery. MAIN RESULTS: DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ. SIGNIFICANCE: This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.

Auditory cortex encodes lipreading information through spatially distributed activity.

Watching a speaker's face improves speech perception accuracy. This benefit is enabled, in part, by implicit lipreading abilities present in the general population. While it is established that lipreading can alter the perception of a heard word, it is unknown how these visual signals are represented in the auditory system or how they interact with auditory speech representations. One influential, but untested, hypothesis is that visual speech modulates the population-coded representations of phonetic and phonemic features in the auditory system. This model is largely supported by data showing that silent lipreading evokes activity in the auditory cortex, but these activations could alternatively reflect general effects of arousal or attention or the encoding of non-linguistic features such as visual timing information. This gap limits our understanding of how vision supports speech perception. To test the hypothesis that the auditory system encodes visual speech information, we acquired functional magnetic resonance imaging (fMRI) data from healthy adults and intracranial recordings from electrodes implanted in patients with epilepsy during auditory and visual speech perception tasks. Across both datasets, linear classifiers successfully decoded the identity of silently lipread words using the spatial pattern of auditory cortex responses. Examining the time course of classification using intracranial recordings, lipread words were classified at earlier time points relative to heard words, suggesting a predictive mechanism for facilitating speech. These results support a model in which the auditory system combines the joint neural distributions evoked by heard and lipread words to generate a more precise estimate of what was said.

The high frequency oscillations in the amygdala, hippocampus, and temporal cortex during mesial temporal lobe epilepsy.

The mesial temporal lobe epilepsy (MTLE) seizures are believed to originate from medial temporal structures, including the amygdala, hippocampus, and temporal cortex. Thus, the seizures onset zones (SOZs) of MTLE locate in these regions. However, whether the neural features of SOZs are specific to different medial temporal structures are still unclear and need more investigation. To address this question, the present study tracked the features of two different high frequency oscillations (HFOs) in the SOZs of these regions during MTLE seizures from 10 drug-resistant MTLE patients, who received the stereo electroencephalography (SEEG) electrodes implantation surgery in the medial temporal structures. Remarkable difference of HFOs features, including the proportions of HFOs contacts, percentages of HFOs contacts with significant coupling and firing rates of HFOs, could be observed in the SOZs among three medial temporal structures during seizures. Specifically, we found that the amygdala might contribute to the generation of MTLE seizures, while the hippocampus plays a critical role for the propagation of MTLE seizures. In addition, the HFOs firing rates in SOZ regions were significantly larger than those in NonSOZ regions, suggesting the potential biomarkers of HFOs for MTLE seizure. Moreover, there existed higher percentages of SOZs contacts in the HFOs contacts than in all SEEG contacts, especially those with significant coupling to slow oscillations, implying that specific HFOs features would help identify the SOZ regions. Taken together, our results displayed the features of HFOs in different medial temporal structures during MTLE seizures, and could deepen our understanding concerning the neural mechanism of MTLE.

Stereoelectroencephalography-guided radiofrequency thermocoagulation.

Within the neurosurgeon's armamentarium, stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC) is an elegant tool to manage epilepsy in selected cases. This technique can 1) be curative when targeting small-volume ictal onset zones, 2) be used as a diagnostic tool by observing the consequences of coagulation on seizures or by recording the epileptic network in SEEG, and 3) offer palliative treatment through multiple lesions within a wide epileptic network. It is performed on awake patients, under continuous neurological evaluation, while monitoring impedance, time, and energy delivered. It could offer highly favorable outcomes in some cases, as in periventricular nodular heterotopia where 81% of patients are responders.

The involvement of the piriform cortex in non-lesional temporal lobe epilepsy: an uncommon component of the epileptogenic network.

The piriform cortex is recognized as highly epileptogenic in rodents, yet its electrophysiological role in human epilepsy remains understudied. Recent surgical outcomes have suggested potential benefits in resecting the piriform cortex for cases of medial temporal lobe epilepsy. However, little is known about its electrophysiological activity in human epilepsy. This case-series study aimed to explore the electrophysiological role of the piriform cortex within the epileptogenic network among patients with suspected temporal lobe epilepsy. Participants were recruited from Emory University Hospital or Children's Healthcare of Atlanta, with non-lesional frontotemporal or temporal lobe hypotheses, undergoing stereoelectroencephalographic studies. Specifically, focus was placed on patients with one or more electrode contacts in the piriform cortex. Primary objectives included determining piriform cortex involvement within the electrophysiologically defined epileptogenic network and assessing the effects of electrical stimulation. Twenty-two patients were included in the study. Notably, only one patient exhibited piriform cortex involvement at seizure onset, associated with an olfactory aura. Two patients showed early piriform cortex involvement, while others displayed late or no involvement. Electrical stimulation of the piriform cortex induced after-discharges in three patients and replicated a habitual seizure in one. These findings present a contrast to surgical outcome studies, suggesting that the piriform cortex may not typically play a significant role in the epileptogenic network among patients with non-lesional temporal lobe epilepsy.

Whole-brain simulation of interictal epileptic discharges for patient-specific interpretation of interictal SEEG data.

In patients with refractory epilepsy, the clinical interpretation of stereoelectroencephalographic (SEEG) signals is crucial to delineate the epileptogenic network that should be targeted by surgery. We propose a pipeline of patient-specific computational modeling of interictal epileptic activity to improve the definition of regions of interest. Comparison between the computationally defined regions of interest and the resected region confirmed the efficiency of the pipeline. This result suggests that computational modeling can be used to reconstruct signals and aid clinical interpretation.

Initial United States experience with Medtronic Stealth Autoguide cranial robotic guidance platform.

OBJECTIVE: Stereotactic techniques play an important role in neurosurgery. The development of a miniaturized cranial robot with an efficient workflow and accurate surgical execution is an important step in a broader application of these techniques. Herein, the authors describe their experience with the Medtronic Stealth Autoguide miniaturized cranial robot. METHODS: A retrospective review of 75 cases from 2020 to 2022 was performed. The patients who had undergone surgery utilizing the Stealth Autoguide robot were analyzed for surgical indication and accuracy, operative time, and clinical outcome. The outcomes were defined as follows: for stereoelectroencephalography (SEEG), the electrode placement pattern that identified the seizure focus and did not require any revision or additional leads; for biopsy, the percentage of cases in which diagnostic tissue was obtained; and for laser interstitial thermal therapy (LITT), the percentage of cases in which laser fiber placement was adequate for ablation. Surgical complications were defined as any asymptomatic or symptomatic intracerebral hemorrhage, new neurological deficit, or need for electrode, laser fiber, or biopsy needle repositioning or revision. RESULTS: The Stealth Autoguide robot was utilized in 75 on-label cases, including 40 SEEG cases for seizure focus localization, 19 LITT cases, and 16 stereotactic biopsy cases. The mean real target error (RTE) at the entry was 1.48 ± 0.84 mm for biopsy, 1.36 ± 0.89 mm for Visualase laser fiber placement, and 1.24 ± 0.72 mm for SEEG. The mean RTE at the target was 1.56 ± 0.95 mm for biopsy needle placement, 1.42 ± 0.93 mm for Visualase laser fiber placement, and 1.31 ± 0.87 mm for SEEG electrode placement. The surgical time for unilateral SEEG cases took an average 52 minutes (average 6.5 mins/lead, average 8 electrodes). Bilateral SEEG cases took an average 105 minutes (average 7.5 mins/lead, average 14 electrodes). In the SEEG population, there were no revised or unsuccessful seizure localizations. For biopsy, diagnostic tissue was obtained in 100% of cases. For LITT, fiber placement was adequate for ablation in 100% of cases. There were no cases of symptomatic or asymptomatic intracerebral hemorrhage, and no cases required repositioning or replacement of the laser fiber, electrode, or biopsy needle. One patient experienced transient cranial nerve III palsy following laser ablation that resolved in 10 weeks. A failure of communication between the robotic platform and the Stealth Autoguide as a station required the cancellation of 1 procedure. CONCLUSIONS: The Medtronic Stealth Autoguide robot system is versatile across biopsy, SEEG, and laser ablation indications. Setup and surgical execution are efficient with a high degree of accuracy and consistency.

Beta-band power modulation in the human amygdala differentiates between go/no-go responses in an arm-reaching task.

Objective. Traditionally known for its involvement in emotional processing, the amygdala's involvement in motor control remains relatively unexplored, with sparse investigations into the neural mechanisms governing amygdaloid motor movement and inhibition. This study aimed to characterize the amygdaloid beta-band (13-30 Hz) power between 'Go' and 'No-go' trials of an arm-reaching task.Approach. Ten participants with drug-resistant epilepsy implanted with stereoelectroencephalographic (SEEG) electrodes in the amygdala were enrolled in this study. SEEG data was recorded throughout discrete phases of a direct reach Go/No-go task, during which participants reached a touchscreen monitor or withheld movement based on a colored cue. Multitaper power analysis along with Wilcoxon signed-rank and Yates-correctedZtests were used to assess significant modulations of beta power between the Response and fixation (baseline) phases in the 'Go' and 'No-go' conditions.Main results. In the 'Go' condition, nine out of the ten participants showed a significant decrease in relative beta-band power during the Response phase (p⩽ 0.0499). In the 'No-go' condition, eight out of the ten participants presented a statistically significant increase in relative beta-band power during the response phase (p⩽ 0.0494). Four out of the eight participants with electrodes in the contralateral hemisphere and seven out of the eight participants with electrodes in the ipsilateral hemisphere presented significant modulation in beta-band power in both the 'Go' and 'No-go' conditions. At the group level, no significant differences were found between the contralateral and ipsilateral sides or between genders.Significance.This study reports beta-band power modulation in the human amygdala during voluntary movement in the setting of motor execution and inhibition. This finding supplements prior research in various brain regions associating beta-band power with motor control. The distinct beta-power modulation observed between these response conditions suggests involvement of amygdaloid oscillations in differentiating between motor inhibition and execution.

NeoCoMM: A neocortical neuroinspired computational model for the reconstruction and simulation of epileptiform events.

BACKGROUND: Understanding the pathophysiological dynamics that underline Interictal Epileptiform Events (IEEs) such as epileptic spikes, spike-and-waves or High-Frequency Oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in Local Field Potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs. METHOD: a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns. RESULTS: Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics. CONCLUSION: The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.

[Insular lobe epilepsy. Part 2: presurgical evaluation & surgical interventions with stereo-electroencephalography].

Identification of insular lobe epilepsy (ILE) presents a major clinical challenge in the diagnosis and treatment of drug-resistant focal epilepsies. ILE has diverse clinical presentations due to the multifaceted functions of the insula. Surface EEG findings do not provide straightforward information to predict this deeply-situated origin of seizures; they are even misleading, masquerading as those of other focal epilepsies, such as temporal and frontal ones. Non-invasive imagings may disclose insular abnormalities, but extra-insular abnormalities can coexist or even stand out. Careful reading and a second-look guided by other clinical information are crucial in order not to miss subtle insulo-opercular abnormalities. Furthermore, a possible insular origin of seizures should be considered in MRI-negative frontal/temporal/parietal epilepsies. Therefore, exploration/exclusion of insular-origin seizures is necessary for a great majority of surgical candidates. As for the stereo-electroencephalography, considered as the gold standard method for intra-cranial EEG investigations with suspicion of ILE, planning of electrode positions/trajectories require sufficient knowledge of the functional localization and anatomo-functional connectivity of the insula. Dense sampling within the insula is required in patients with probable ILE, because the seizure-onset zone can be restricted to a single insular gyrus or even a part of it. It is also crucial to explore extra-insular regions on the basis of non-invasive investigation results while considering their anatomo-functional relationships with the insula. From a surgical perspective, differentiating seizures strictly confined to the insula from those extending to the opercula is of particular importance. Pure insular seizures can be treated with less invasive measures, such as radiofrequency thermocoagulation. To conclude, close attention must be paid to the possibility of ILE throughout the diagnostic workup. The precise identification/exclusion of ILE is a prerequisite to provide appropriate and effective surgical treatment in pharmaco-resistant focal epilepsies.

[Insular lobe epilepsy. Part 1: semiology].

The insula is often referred to as "the fifth lobe" of the brain, and its accessibility used to be very limited due to the deep location under the opercula as well as the sylvian vasculature. It was not until the availability of modern stereo-electroencephalography (SEEG) technique that the intracranial electrodes could be safely and chronically implanted within the insula, thereby enabling anatomo-electro-clinical correlations in seizures of this deep origin. Since the first report of SEEG-recorded insular seizures in late 1990s, the knowledge of insular lobe epilepsy (ILE) has rapidly expanded. Being on the frontline for the diagnosis and management of epilepsy, neurologists should have a precise understanding of ILE to differentiate it from epilepsies of other lobes or non-epileptic conditions. Owing to the multimodal nature and rich anatomo-functional connections of the insula, ILE has a wide range of clinical presentations. The following symptoms should heighten the suspicion of ILE: somatosensory symptoms involving a large/bilateral cutaneous territory or taking on thermal/painful character, and cervico-laryngeal discomfort. The latter ranges from slight dyspnea to a strong sensation of strangulation (laryngeal constriction). Other symptoms include epigastric discomfort/nausea, hypersalivation, auditory, vestibular, gustatory, and aphasic symptoms. However, most of these insulo-opercular symptoms can easily be masked by those of extra-insular seizure propagation. Indeed, sleep-related hyperkinetic (hypermotor) epilepsy (SHE) is a common clinical presentation of ILE, which shows predominant hyperkinetic and/or tonic-dystonic features that are often indistinguishable from those of fronto-mesial seizures. Subtle objective signs, such as constrictive throat noise (i.e., laryngeal constriction) or aversive behavior (e.g., facial grimacing suggesting pain), are often the sole clue in diagnosing insular SHE. Insular-origin seizures should also be considered in temporal-like seizures without frank anatomo-electro-clinical correlations. All in all, ILE is not the epilepsy of an isolated island but rather of a crucial hub involved in the multifaceted roles of the brain.

Epilepsy lesion localization method based on brain function network.

Objective: In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. Method: To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. Results: The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Conclusion: Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.

Objective.Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.Approach.In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.Main results.Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.Significance.This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.

Status epilepticus and psychosis: Lessons from SEEG.

Psychotic manifestations are a classic feature of non-convulsive status epilepticus (NCSE) of temporal origin. For several decades now, the various psychiatric manifestations of NCSE have been described, and in particular, the diagnostic challenges they pose. However, studies using stereotactic-EEG (SEEG) recordings are very rare. Only a few cases have been reported, but they demonstrated the anatomical substrate of certain manifestations, including hallucinations, delusions, and emotional changes. The post-ictal origin of some of the manifestations should be emphasized. More generally, SEEG has shown that seizures affecting the temporal and frontal limbic systems can lead to intense emotional experiences and behavioural disturbances.

Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus.

Objective.Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between 'Go' and 'No-go' trials in a Go/No-go arm-reaching task.Approach.Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a 'center-out' Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between 'Go' and 'No-go' trials. The accuracy of five different discriminant functions was evaluated using cross-validation.Main results.The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants.Significance.This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface applications.

Explaining slow seizure propagation with white matter tractography.

Epileptic seizures recorded with stereoelectroencephalography (SEEG) can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and SEEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread, and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared to regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-IV). The seizure onset zone was more tract connected to regions of seizure spread than to non-involved regions (p<0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared to non-involved regions. In seizure free patients only, regions of seizure spread were more tract connected to the seizure onset zone than to other regions of spread (p<0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract connected to regions of early spread compared to regions of late spread in seizure free patients only (p<0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone may be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds may be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship may eventually provide insight into selecting the correct targets for epilepsy surgery.

Deep learning based decoding of single local field potential events.

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

Intracranial neural representation of phenomenal and access consciousness in the human brain.

After more than 30 years of extensive investigation, impressive progress has been made in identifying the neural correlates of consciousness (NCC). However, the functional role of spatiotemporally distinct consciousness-related neural activity in conscious perception is debated. An influential framework proposed that consciousness-related neural activities could be dissociated into two distinct processes: phenomenal and access consciousness. However, though hotly debated, its authenticity has not been examined in a single paradigm with more informative intracranial recordings. In the present study, we employed a visual awareness task and recorded the local field potential (LFP) of patients with electrodes implanted in cortical and subcortical regions. Overall, we found that the latency of visual awareness-related activity exhibited a bimodal distribution, and the recording sites with short and long latencies were largely separated in location, except in the lateral prefrontal cortex (lPFC). The mixture of short and long latencies in the lPFC indicates that it plays a critical role in linking phenomenal and access consciousness. However, the division between the two is not as simple as the central sulcus, as proposed previously. Moreover, in 4 patients with electrodes implanted in the bilateral prefrontal cortex, early awareness-related activity was confined to the contralateral side, while late awareness-related activity appeared on both sides. Finally, Granger causality analysis showed that awareness-related information flowed from the early sites to the late sites. These results provide the first LFP evidence of neural correlates of phenomenal and access consciousness, which sheds light on the spatiotemporal dynamics of NCC in the human brain.

An Unusual Case of Hypothalamic Hamartoma With Nongelastic Seizures and Posterior Cortex Connectivity.

BACKGROUND: To describe a rare seizure semiology originating from a hypothalamic hamartoma in a child, along with unusual ictal onset and connectivity pattern, and provide a review of the pathophysiology of epilepsy associated with hypothalamic hamartoma and management. METHODS: A detailed retrospective chart review and literature search were performed using Pubmed and Embase. RESULTS: We present a case of a three-year-old male who presented with dyscognitive seizures with onset at age 22 months. Stereoelectroencephalography exploration confirmed the onset in hypothalamic hamartoma with rapid propagation to the temporal-parietal-occipital association cortex and precuneus. The patient's epilepsy was cured with laser ablation of the hamartoma. CONCLUSION: Published literature mostly describes a more anterior frontal or temporal epileptic network with primarily gelastic seizures being the hallmark type of seizures associated with hypothalamic hamartoma. We highlight a rare posterior cortex network with an atypical presentation of focal nonmotor seizures with impaired awareness in the setting of a hypothalamic hamartoma. Stereotactic laser ablation of the hamartoma rendered seizure freedom. Early diagnosis and appropriate treatment can lead to seizure freedom.