Intracranial human brain recordings from multiple implanted depth electrodes using stereo-EEG (sEEG) technology for seizure localization provide unique local field potential signals (LFP) sampled with standard macro- and special micro-electrode contacts. Over one hundred macro- and micro-contact LFP signals localized in particular brain regions were recorded from each sEEG monitoring case as patients engaged in an automated battery of verbal memory and non-verbal gaze movement tasks. Subject eye and vocal responses in both visual and auditory task versions were automatically detected in Polish, Czech, and Slovak languages with accurate timing of the correct and incorrect verbal responses using our web-based transcription tool. The behavioral events, LFP and pupillometric signals were synchronized and stored in a standard BIDS data structure with corresponding metadata. Each dataset contains recordings from at least one battery task performed over at least one day. The same set of 180 common nouns in the three languages was used across different battery tasks and recording days to enable the analysis of selective responses to specific word stimuli.

• MeSH
• elektroencefalografie MeSH
• jazyk (prostředek komunikace) MeSH
• kognice * MeSH
• lidé MeSH
• mozek * fyziologie   MeSH
• pohyby očí MeSH
• technologie sledování pohybu očí MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• dataset MeSH

Precise localization of the epileptogenic zone is pivotal for planning minimally invasive surgeries in drug-resistant epilepsy. Here, we present a graph neural network (GNN) framework that integrates interictal intracranial EEG features, electrode topology, and MRI features to automate epilepsy surgery planning. We retrospectively evaluated the model using leave-one-patient-out cross-validation on a dataset of 80 drug-resistant epilepsy patients treated at St. Anne's University Hospital (Brno, Czech Republic), comprising 31 patients with good postsurgical outcomes (Engel I) and 49 with poor outcomes (Engel II-IV). The GNN predictions demonstrated a significantly better (P < 0.05, Mann-Whitney-U test) area under the precision-recall curve in patients with good outcomes (area under the precision-recall curve: 0.69) compared with those with poor outcomes (area under the precision-recall curve: 0.33), indicating that the model captures clinically relevant targets in successful cases. In patients with poor outcomes, the graph neural network proposed alternative intervention sites that diverged from the original clinical plans, highlighting its potential to identify alternative therapeutic targets. We show that topology-aware GNNs significantly outperformed (P < 0.05, Wilcoxon signed-rank test) traditional neural networks while using the same intracranial EEG features, emphasizing the importance of incorporating implantation topology into predictive models. These findings uncover the potential of GNNs to automatically suggest targets for epilepsy surgery, which can assist the clinical team during the planning process.

• Klíčová slova
• MRI, epilepsy, graph neural networks, iEEG, surgery,
• Publikační typ
• časopisecké články MeSH

Data comprise intracranial EEG (iEEG) brain activity represented by stereo EEG (sEEG) signals, recorded from over 100 electrode channels implanted in any one patient across various brain regions. The iEEG signals were recorded in epilepsy patients (N = 10) undergoing invasive monitoring and localization of seizures when they were performing a battery of four memory tasks lasting approx. 1 hour in total. Gaze tracking on the task computer screen with estimating the pupil size was also recorded together with behavioral performance. Each dataset comes from one patient with anatomical localization of each electrode contact. Metadata contains labels for the recording channels with behavioral events marked from all tasks, including timing of correct and incorrect vocalization of the remembered stimuli. The iEEG and the pupillometric signals are saved in BIDS data structure to facilitate efficient data sharing and analysis.

• MeSH
• elektrody MeSH
• elektrokortikografie * MeSH
• epilepsie patofyziologie   MeSH
• lidé MeSH
• mozek fyziologie   MeSH
• oční fixace MeSH
• paměť fyziologie   MeSH
• pupila MeSH
• technologie sledování pohybu očí MeSH
• záchvaty patofyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• dataset MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

The electrophysiological EEG features such as high frequency oscillations, spikes and functional connectivity are often used for delineation of epileptogenic tissue and study of the normal function of the brain. The epileptogenic activity is also known to be suppressed by cognitive processing. However, differences between epileptic and healthy brain behavior during rest and task were not studied in detail. In this study we investigate the impact of cognitive processing on epileptogenic and non-epileptogenic hippocampus and the intracranial EEG features representing the underlying electrophysiological processes. We investigated intracranial EEG in 24 epileptic and 24 non-epileptic hippocampi in patients with intractable focal epilepsy during a resting state period and during performance of various cognitive tasks. We evaluated the behavior of features derived from high frequency oscillations, interictal epileptiform discharges and functional connectivity and their changes in relation to cognitive processing. Subsequently, we performed an analysis whether cognitive processing can contribute to classification of epileptic and non-epileptic hippocampus using a machine learning approach. The results show that cognitive processing suppresses epileptogenic activity in epileptic hippocampus while it causes a shift toward higher frequencies in non-epileptic hippocampus. Statistical analysis reveals significantly different electrophysiological reactions of epileptic and non-epileptic hippocampus during cognitive processing, which can be measured by high frequency oscillations, interictal epileptiform discharges and functional connectivity. The calculated features showed high classification potential for epileptic hippocampus (AUC = 0.93). In conclusion, the differences between epileptic and non-epileptic hippocampus during cognitive processing bring new insight in delineation between pathological and physiological processes. Analysis of computed iEEG features in rest and task condition can improve the functional mapping during pre-surgical evaluation and provide additional guidance for distinguishing between epileptic and non-epileptic structure which is absolutely crucial for achieving the best possible outcome with as little side effects as possible.

• Klíčová slova
• cognitive processing, functional connectivity, high frequency oscillation (HFO), hippocampus, interictal epileptiform discharge, pharmacoresistant epilepsy,
• Publikační typ
• časopisecké články MeSH