Křemen, Václav

Abstract Objective Interictal epileptiform spikes (IESs) are widely used biomarkers of epileptogenic tissue in presurgical epilepsy evaluation. However, their reliability for seizure onset zone (SOZ) localization in high‐density intracranial electroencephalography (iEEG) remains inconsistent. It is underexplored whether this variability reflects methodological differences across detection algorithms or a limitation of IESs as a biomarker. This study evaluates whether IES detection alone provides sufficient spatial specificity for accurate SOZ localization in iEEG. Methods We systematically benchmarked eight established IES detection algorithms using iEEG recordings from 35 patients with drug‐resistant focal epilepsy. To provide a clinically meaningful reference, algorithm performance was evaluated against the clinically defined SOZ. To refine detection outputs and move beyond only IES detection, we applied masking strategies to isolate informative IES subsets based on rate, amplitude, frequency, and co‐occurrence with high‐frequency oscillations (HFOs). Conceptually, masking acts as a spatial refinement strategy that progressively restricts diffuse IES activity to more focal, pathologically relevant event populations. Results Despite substantial variability in detection characteristics, SOZ localization performance (ratio of events originating from SOZ divided by total number of events) converged within a narrow range (.23–.51), suggesting that IESs exhibit an intrinsic ceiling in SOZ localization performance when used alone. Amplitude‐based masking yielded modest improvements, whereas frequency‐based masking reduced performance. In contrast, IES‐HFO co‐occurrence markedly improved localization, particularly when combined with rate‐based masking, achieving a median SOZ localization ratio of .89, exceeding IESs (.37) and HFOs (.63) evaluated independently. Significance These findings demonstrate that IES‐based SOZ localization may require moving beyond detection‐only approaches. The integration of informed, biomarker‐driven masking strategies provides a more robust framework for identifying the activity most relevant to the epileptogenic network and paves the way for future incorporation into the clinical decision‐making pipeline.

Abstract Drug-resistant epilepsy affects tens of millions of people worldwide and is associated with considerable morbidity and mortality. Thalamic deep brain stimulation and cortical responsive neurostimulation are proven treatments for focal epilepsy. Both have been used to target a range of thalamic nuclei; yet, the roles of these thalamic nuclei in focal seizure generation remain incompletely understood. Thirteen patients with drug-resistant focal epilepsy undergoing intracranial EEG were consented to undergo investigation of thalamocortical networks. Sampled regions included cortical, mesial temporal, and thalamic brain regions. Visual and spectral analyses were performed to identify seizure onset patterns and correlate thalamic and cortical seizure activity. Thalamic ictal discharges were observed in 89% of seizures. Of these, 56% demonstrated synchronous thalamocortical activity with distinct patterns. These onset patterns included hypersynchronous spiking, low-voltage fast activity, ictal baseline shifts, and broadband suppression. Multiple thalamic nuclei were involved in ictal organization and propagation, with the specific nuclei depending on the cortical seizure network. The thalamus plays a crucial role in focal onset seizure generation and propagation, with distinct seizure onset patterns and nuclei involved. These findings support exploring a broader range of thalamic nuclei in epilepsy neurostimulation and have implications for seizure detection settings in intracranial sensing devices.

Abstract Introduction The relationship between sleep and brain metabolism remains unclear. This study aimed to examine whether overnight sleep features, using a home sleep apnea system based on peripheral arterial tonometry, are associated with regional FDG-PET changes in older adults. Methods We analyzed cognitively unimpaired adults (≥60 years) from the Mayo Clinic Study of Aging (MCSA) cohort who were not receiving sleep apnea treatment. Participants underwent overnight sleep assessment with the WatchPAT system, followed by FDG-PET imaging the next morning. FDG-PET data were co-registered to 3T MRI and automatically segmented into 122 anatomical regions. PET signal was referenced to the pons (SUVR). We computed voxel-weighted median FDG signal across 47 bilateral cortical and subcortical regions of interest (ROIs), excluding brainstem and cerebellum. Associations between sleep architecture and sleep apnea/hypoxemia burden and regional metabolism were assessed using partial Spearman’s rank correlation, adjusting for age. Results Thirty-seven participants were included (70.3% female; median [IQR] age: 72.0 [68.0–78.0] years). Wake after sleep onset (WASO) showed widespread negative associations with FDG uptake in cortical regions (p< 0.05), particularly the ventromedial/ventrolateral (rs=–0.47 to –0.33) prefrontal cortex, lateral temporal (rs=–0.38 to –0.33), and occipital (rs=–0.47 to –0.37) cortices. Total sleep time (TST) was positively associated with medial structures (p< 0.05), including the posterior cingulate (rs=0.39), retrosplenial cortex (rs=0.48), hippocampus/parahippocampal (rs=0.34 to 0.36), calcarine cortex (ρ=0.38), thalamus (rs=0.37), and caudate (rs=0.43). Hypoxic burden was positively associated with medial occipital regions (rs=0.35 to 0.39) and AHI-4% with cuneus (rs=0.33). Regional gray matter volume did not mediate these associations. WASO correlated with Insomnia Severity Index (rs=0.43, p=0.008) and self-reported refreshing sleep (rs=0.046). Conclusion Overnight sleep macrostructure features (WASO and TST) and hypoxic burden are significantly associated with brain metabolism, potentially underlying symptoms of sleep disturbance and cognitive aging. Support (if any) Mayo Clinic Alzheimer’s Disease Research Center Development Award (P30 AG062677), R01 AG056366, U01 AG006786, R01 AG056366, R01 AG034676, R37 AG011378, R01 AG041851, R01 NS097495, R01 AG068206, P30 AG062677, R01 HL065176. CLARA project work with funding from the European Union’s HORIZON EUROPE research and innovation programme (No 101136607).

Objective The effects of deep brain stimulation (DBS) manifest across multiple timescales, spanning seconds to months, and involve direct electrical effects, neuroplasticity, and network reorganization. In epilepsy, the delayed impact of DBS on seizures presents challenges for optimization. Single‐pulse stimulation and resulting brain stimulation evoked potentials (BSEPs) provide a means to assess effective connectivity and network excitability. This study integrates BSEPs and short trials of DBS during stereoelectroencephalography (sEEG) to map seizure network engagement, modulate network dynamics, and monitor excitability and interictal abnormalities for biomarker informed neuromodulation. Methods Ten individuals with drug resistant epilepsy undergoing clinical sEEG were enrolled in this retrospective cohort study of epilepsy neuromodulation biomarkers. Each patient underwent a trial of high frequency (145Hz) thalamic DBS. BSEPs were acquired before and after DBS trials. Baseline BSEP amplitude assessed seizure network engagement, and modulation of amplitude (pre vs post DBS) assessed change in network excitability. Interictal epileptiform discharges were tracked by an automated classifier. Results Baseline BSEPs delineated distinct patterns of network engagement between thalamic subfields with maximal frontotemporal engagement achieved with stimulation of the anterior nucleus of the thalamus‐ventral anterior nucleus junction. DBS delivered for >1.5 hours reduced BSEP amplitudes compared to baseline, and the degree of modulation correlated with baseline connectivity strength. Shorter DBS trials did not induce reliable BSEP amplitude suppression, but did immediately suppress interictal epileptiform discharge rates in well‐connected seizure networks. Interpretation BSEPs and trials of DBS during sEEG provide novel network biomarkers to evaluate the modulation of large‐scale networks across multiple timescales, advancing biomarker informed neuromodulation. ANN NEUROL 2026;99:748–760

Abstract Objective This study reports development and validation of a deep learning seizure detection algorithm for two‐channel subscalp electroencephalographic (EEG) recordings. Ultra‐long‐term monitoring of people with epilepsy may produce new insights into the timing and pattern of their seizures and may pave the way for novel therapeutic options. Although EEG is the accepted standard for epilepsy monitoring, ultra‐long‐term EEG recordings generate a massive quantity of data and are not reasonably reviewable in full by human readers. Methods The convolutional neural network‐bidirectional long short‐term memory (CNN‐BiLSTM) hybrid algorithm uses nine layers, operating on channel spectrograms 5 min in length with 50% overlap. Retrospective subscalp EEG data from 16 patients recorded at three centers were available for algorithm development and testing. EEG was recorded for a median of 63 days (range = 9–508), and a median of 17 seizures (range = 0–96) were recorded. Training data were augmented with scalp EEG seizures, and performance was benchmarked against a conventional spectral power classifier algorithm. We also evaluated an intrapatient training–testing approach where the algorithm was trained on the first 45% of data. Results The CNN‐BiLSTM trained on scalp EEG and subscalp EEG achieved an area under the receiver operating characteristic curve (AUROC) of .98 and an area under the precision–recall curve (AUPRC) of .50, which correspond to 94% sensitivity with 1.11 false detections per day. The same model trained on iEEG achieved only AUROC = .94 and AUPRC = .36. The conventional spectral band power detector achieved AUROC = .93 and AUPRC = .38. The CNN‐BiLSTM detector trained on the earliest half of the subscalp EEG data achieved AUROC = .93 and AUPRC = .37, corresponding to 87% sensitivity and 5.9 false detections per day. Significance High sensitivity and specificity are possible in automated seizure detection in two‐channel subscalp EEG data using a CNN‐BiLSTM framework. Performance of the detector is superior using subcutaneous EEG data for training rather than intracranial EEG, but addition of scalp EEG seizures for training was helpful.

Abstract 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.

Abstract Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures that often originate within limbic networks involving amygdala and hippocampus. The limbic network is involved in crucial physiologic functions involving memory, emotion and sleep. Temporal lobe epilepsy is frequently drug-resistant, and people often experience comorbidities related to memory, mood and sleep. Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is an established therapy for temporal lobe epilepsy. However, the optimal stimulation parameters and their impact on memory, mood and sleep comorbidities remain unclear. We used an investigational brain sensing-stimulation implanted device to accurately track seizures, interictal epileptiform spikes (IES), and memory, mood and sleep comorbidities in five ambulatory subjects. Wireless streaming of limbic network local field potentials (LFPs) and subject behaviour were captured on a mobile device integrated with a cloud environment. Automated algorithms applied to the continuous LFPs were used to accurately cataloged seizures, IES and sleep-wake brain state. Memory and mood assessments were remotely administered to densely sample cognitive and behavioural response during ANT-DBS in ambulatory subjects living in their natural home environment. We evaluated the effect of continuous low-frequency and duty cycle high-frequency ANT-DBS on epileptiform activity and memory, mood and sleep comorbidities. Both low-frequency and high-frequency ANT-DBS paradigms reduced seizures. However, continuous low-frequency ANT-DBS showed greater reductions in IES, electrographic seizures and better sleep and memory outcomes. These results highlight the potential of synchronized brain sensing and dense behavioural tracking during ANT-DBS for optimizing neuromodulation therapy. While studies with larger patient numbers are needed to validate the benefits of low-frequency ANT-DBS, these findings are potentially translatable to individuals currently implanted with ANT-DBS systems.

Abstract Translational large-animal models that can accommodate human-scale implantable devices are essential for advancing neuromodulation therapies in epilepsy. This study establishes a kainic acid (KA)-induced porcine model of mesial temporal lobe epilepsy (mTLE) using clinical imaging, stereotactic surgery, and a fully implantable neural stimulation and recording (INSR) device designed for humans. Seven pigs (six KA-treated and one saline control) underwent MRI-guided stereotactic implantation of electrodes targeting bilateral hippocampus (HPC) and anterior thalamus (ANT), followed by intra-hippocampal KA or saline infusion. Local field potentials (LFP) were recorded continuously with synchronized video monitoring. Seizures and LFP interictal epileptiform-like discharges (IEDs) were quantified using validated automated detectors. Histology was performed in the saline control and the longest surviving KA-treated pig. Intra-hippocampal KA infusion induced acute status epilepticus in all (6/6) treated pigs. Four animals survived to chronic monitoring with spontaneous seizures observed in three pigs (2,733 seizures; mean duration of 27.2 ± 17.6 seconds). IEDs were observed in bilateral HPC of all animals, including saline control, with higher rates in the lesioned HPC (p <0.0001). While the IED morphology is consistent with epileptiform activity; IEDs alone are not specific for epilepsy and physiological transients (e.g. sharp-wave ripples) and injury-related hyperexcitability or strain-specific hyperexcitability cannot be excluded. Histological analysis revealed patchy neuronal loss and cytoarchitectural changes in HPC. This porcine model reproduces electrophysiological features of human mTLE. This approach provides a powerful translational bridge for developing and testing next-generation INSR and neuromodulation strategies in freely behaving large animals.