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Invasive brain recordings using many electrodes across a wide range of tasks provide a unique opportunity to study the role of oscillatory patterning and functional connectivity. We used large-scale recordings (stereo EEG) within and beyond the human hippocampus to investigate the role of distinct frequency oscillations during real-time execution of visual, attention and memory tasks in eight epileptic patients. We found that activity patterns in the hippocampus showed task and frequency dependent properties. Importantly, we found distinct connectivity signatures, in particular concerning parietal-hippocampal connectivity, thus revealing large scale synchronization of networks involved in memory tasks. Comparing the power per frequency band, across tasks and hippocampal regions (anterior/posterior) we confirmed a main effect of frequency band (p = 0.002). Gamma band activity was higher for visuo-spatial memory tasks in the anterior hippocampus. Further, we found that alpha and beta band activity in posterior hippocampus had larger modulation for high memory load visual tasks (p = 0.004). Three functional connectivity task related networks were identified: (dorsal) parietal-hippocampus (visual attention and memory), ventral stream- hippocampus and hippocampal-frontal connections (mainly tasks involving face recognition or object based search). These findings support the critical role of oscillatory patterning in the hippocampus during visual and memory tasks and suggests the presence of task related spectral and functional connectivity signatures. These results show that the use of large scale human intracranial recordings can validate the role of oscillatory and functional connectivity patterns across a broad range of cognitive domains.

ObjectiveDirect electrical stimulations of cerebral cortex are a traditional part of stereoelectroencephalography (SEEG) practice, but their value as a predictive factor for seizure outcome has never been carefully investigated.Patients and methodWe retrospectively analysed a cohort of 346 patients operated on for drug-resistant focal epilepsy after SEEG exploration. As potential predictors we included: aetiology, MRI data, age of onset, duration of epilepsy, age at surgery, topography of surgery and whether a seizure was induced by either low frequency electrical stimulation (LFS) or high frequency electrical stimulation.ResultsOf 346 patients, 63.6% had good outcome (no seizure recurrence, Engel I). Univariate analysis demonstrated significant correlation with favourable outcome (Engel I) for: aetiology, positive MRI and seizure induced by stimulation. At multivariate analysis, informative MRI, type II focal cortical dysplasia and tumour reduced the risk of seizure recurrence (SR) by 47%, 58% and 81%, respectively. Compared with the absence of induced seizures, the occurrence of ictal events after LFS significantly predicts a favourable outcome on seizures, with only 44% chance of disabling SR at last follow-up.ConclusionAmong the already known predictors outcome, seizure induction by LFS therefore represents a positive predictive factor for seizure outcome after surgery.

In patients diagnosed with pharmaco-resistant epilepsy, cerebral areas responsible for seizure generation can be defined by performing implantation of intracranial electrodes. The identification of the epileptogenic zone (EZ) is based on visual inspection of the intracranial electroencephalogram (IEEG) performed by highly qualified neurophysiologists. New computer-based quantitative EEG analyses have been developed in collaboration with the signal analysis community to expedite EZ detection. The aim of the present report is to compare different signal analysis approaches developed in four different European laboratories working in close collaboration with four European Epilepsy Centers. Computer-based signal analysis methods were retrospectively applied to IEEG recordings performed in four patients undergoing pre-surgical exploration of pharmaco-resistant epilepsy. The four methods elaborated by the different teams to identify the EZ are based either on frequency analysis, on nonlinear signal analysis, on connectivity measures or on statistical parametric mapping of epileptogenicity indices. All methods converge on the identification of EZ in patients that present with fast activity at seizure onset. When traditional visual inspection was not successful in detecting EZ on IEEG, the different signal analysis methods produced highly discordant results. Quantitative analysis of IEEG recordings complement clinical evaluation by contributing to the study of epileptogenic networks during seizures. We demonstrate that the degree of sensitivity of different computer-based methods to detect the EZ in respect to visual EEG inspection depends on the specific seizure pattern.

Converging data that attribute a central role to sleep in memory consolidation have increased the interest to understand the characteristics of the hippocampal sleep and their relations with the processing of new information. Neural synchronization between different brain regions is thought to be implicated in long-term memory consolidation by facilitating neural communication and by promoting neural plasticity. However, the majority of studies have focused their interest on intra-hippocampal, rhinal–hippocampal or cortico-hippocampal synchronization, while inter-hemispheric synchronization has been so far neglected. To clarify the features of spontaneous human hippocampal activity and to investigate inter-hemispheric hippocampal synchronization across vigilance states, pre-sleep wakefulness and nighttime sleep were recorded from right and left homologous hippocampal loci using stereo-EEG techniques. Hence, quantitative and inter-hemispheric coherence analyses of hippocampal activity across sleep and waking states were carried out. The results showed the presence of delta activity in human hippocampal spontaneous EEG also during wakefulness. The activity in the delta range exhibited a peculiar bimodal distribution, namely a low frequency non-oscillatory activity (up to 2Hz) synchronized between hemispheres mainly during wake and REM sleep, and a faster oscillatory rhythm (2–4Hz). The latter was less synchronized between the hippocampi and seemed reminiscent of animal RSA (rhythmic slow activity). Notably, the low-delta activity showed high inter-hemispheric hippocampal coherence during REM sleep and, to a lesser extent, during wakefulness, paralleled by a (unexpected) decrease of coherence during NREM sleep. Therefore, low-delta hippocampal state-dependent synchronization starkly contrasts with neocortical behavior in the same frequency range. Further studies might shed light on the role of these low frequency rhythms in the encoding processes during wakefulness and in the consolidation processes during subsequent sleep. ► We found delta activity in the human hippocampus across all vigilance states. ► Two types of delta activity are originated in the hippocampus. ► Low delta is synchronous between the hippocampi during the activated states. ► High delta is weakly in phase between hemispheres. ► High delta is an oscillatory background rhythm while low delta is a transient rhythm.

Objective To assess factors associated with favorable seizure outcome after surgery for symptomatic epileptic spasms and improve knowledge on pathophysiology of this seizure type. Methods Inclusion criteria were: (1) age between 6 months and 15 years at surgery; (2) active epileptic spasms; (3) follow‐up after surgery >1 year. Results We retrospectively studied 80 children (aged 1.3 ± 2 years at seizure onset; 5.8 ± 4 years at surgery, 11.7 ± 5.7 years at last follow up). Magnetic resonance imaging (MRI) revealed structural abnormalities in 77/80 patients (96.3%; unilateral in 69: 89.6%). We performed invasive recordings in 24 patients (30%). In 21 patients in whom MRI or histopathology detected a lesion, electrodes exploring it constantly captured initial ictal activity at spasm onset. Fifty‐eight patients (72.5%) underwent unilobar and 22 (27.5%) multilobar or hemispheric procedures. At last follow‐up, 49 patients (61.3%) were in Engel class I. Multivariate logistic models showed completeness of resection of the seizure onset zone (OR = 0.016, 95%CI: 0.002, 0.122) and of the MRI visible lesion (OR = 0.179, 95% CI: 0.032, 0.999) to be significantly associated with Engel class IA outcome. Unfavorable outcome was associated with an older age at surgery, when it reflected a longer duration of epilepsy (OR = 1.383, 95% CI: 0.994,1.926). Interpretation Data emerging from invasive recordings and the good seizure outcome following removal of discrete epileptogenic lesions support a focal cortical origin of spasms. In patients with discrete epileptogenic lesions, the pragmatic approach to surgery should follow the same principles applied to focal epilepsy favoring, whenever possible, unilobar, one‐stage resections.

There is compelling evidence indicating that sleep plays a crucial role in the consolidation of new declarative, hippocampus-dependent memories. Given the increasing interest in the spatiotemporal relationships between cortical and hippocampal activity during sleep, this study aimed to shed more light on the basic features of human sleep in the hippocampus. We recorded intracerebral stereo-EEG directly from the hippocampus and neocortical sites in five epileptic patients undergoing presurgical evaluations. The time course of classical EEG frequency bands during the first three NREM-REM sleep cycles of the night was evaluated. We found that delta power shows, also in the hippocampus, the progressive decrease across sleep cycles, indicating that a form of homeostatic regulation of delta activity is present also in this subcortical structure. Hippocampal sleep was also characterized by: i) a lower relative power in the slow oscillation range during NREM sleep compared to the scalp EEG; ii) a flattening of the time course of the very low frequencies (up to 1 Hz) across sleep cycles, with relatively high levels of power even during REM sleep; iii) a decrease of power in the beta band during REM sleep, at odds with the typical increase of power in the cortical recordings. Our data imply that cortical slow oscillation is attenuated in the hippocampal structures during NREM sleep. The most peculiar feature of hippocampal sleep is the increased synchronization of the EEG rhythms during REM periods. This state of resonance may have a supportive role for the processing/consolidation of memory.

PurposeTo review recent advances in structural MRI post-processing for pediatric drug-resistant epilepsy, with emphasis on artificial intelligence–driven and quantitative techniques, including MELD-Graph, MAP18, FLAT1, and SUPR-FLAIR, and to evaluate their impact on lesion detection, epileptogenic zone localization, and presurgical planning.MethodsNovel post-processing approaches were examined with respect to their computational foundations, imaging requirements, and diagnostic performance. Techniques employing machine learning, deep learning, voxel-based morphometry, cortical surface projection, and FLAIR/T1 ratio mapping were assessed for their applicability in children and their integration into multimodal evaluation pathways alongside electrophysiology and functional imaging.ResultsAdvanced post-processing tools substantially increase sensitivity for detecting subtle cortical abnormalities, particularly in MRI-negative pediatric epilepsy. MELD-Graph identify features of focal cortical dysplasia through automated surface-based analysis and deep neural network classification, achieving notable lesion detection even when conventional MRI findings are normal. MAP18 provides complementary voxel-wise morphometric assessment, improving specificity and benefiting from optimized structural sequences. FLAT1 enhances lesion conspicuity by quantifying FLAIR/T1 signal relationships, while SUPR-FLAIR improves visualization of cortical signal abnormalities through normalized FLAIR intensity projection onto the cortical surface. When incorporated into multimodal diagnostic workflows, these methods refine epileptogenic zone localization, inform individualized surgical strategies, and can reduce reliance on invasive testing.ConclusionAdvanced structural MRI post-processing is transforming the neuroradiological evaluation of pediatric drug-resistant epilepsy. By revealing subtle cortical abnormalities not visible on conventional imaging, these tools support more precise lesion characterization and surgical planning. Ongoing efforts toward standardization, clinical validation, and workflow integration will be essential to ensure widespread adoption and maximize clinical impact within precision-medicine approaches to pediatric epilepsy.Advanced structural MRI post-processing tools significantly improve the detection of subtle epileptogenic lesions like focal cortical dysplasia in pediatric epilepsy.Accurate localization requires multimodal integration of structural, metabolic, and functional data, with electrical imaging.Imaging informs personalized surgical planning by mapping eloquent cortex and predicting post-surgical seizure and cognitive outcomes using virtual models

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

Objective To retrospectively evaluate seizure outcome in a case-series of patients with nodular heterotopy (NH)-related epilepsy treated by stereo-EEG (SEEG)-guided radio-frequency thermocoagulation (RF-THC) of the NH. Methods Five patients (three male, age 5–33 years) with drug-resistant focal epilepsy presented a single NH at brain MRI. Following video-EEG monitoring, patients underwent SEEG recording to better identify the epileptogenic zone. All patients received RF-THC of the NH, using contiguous contacts of the electrodes employed for recording. The contacts for RF-THC lesions were chosen according to anatomical (intranodular position) and electrical (intranodular ictal low-voltage fast activity) criteria. Results At SEEG recordings, ictal discharge originated from the NH alone in three cases and from the NH and ipsilateral hippocampus in one case. In the remaining case, different sites of ictal onset, including the NH, were identified within the left frontal lobe. No adverse effects related to the RF-THC procedures were observed, apart from a habitual seizure that occurred during coagulation in one patient. Postprocedural sustained seizure freedom was detected in four cases (mean follow-up 33.5 months). In the case with left frontal multifocal ictal activity, RF-THC of the NH provided no benefit on seizures, and the patient is seizure-free after left frontal lobe resection. Conclusions SEEG-guided RF-THC proved to be a safe and effective option in our small case-series of NH-related focal epilepsy. The indications to this treatment were strictly dependent on findings of intracerebral recording by SEEG, which can define the role of the NH in the generation of the ictal discharge.

Distinguishing epileptic seizures from parasomnias is challenging due to overlapping motor features. This study evaluated a SlowFast deep learning model using video recordings of 167 individuals to classify Sleep-Related Hypermotor Epilepsy, Disorders of Arousal, and REM Sleep Behavior Disorder. The model achieved a mean accuracy of 83.3% across three data splits. This work represents an initial step toward developing automated tools to support clinicians in assessing sleep-related motor events.