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.

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.
METHODS: 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.
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.
CONCLUSIONS: 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.

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.
METHODS: 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.
CONCLUSIONS: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

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.

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.

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.
CONCLUSIONS: 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.

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.

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.