Petr Klimes

Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by progressive dementia, brain atrophy, and ultimately death. Using cerebral organoids derived from human-induced pluripotent stem cells (hiPSCs) carrying the familial PSEN1 A246E variant, we investigated the temporal relationship between amyloid-β (Aβ) dysregulation and spontaneous neuronal activity. Multielectrode array recordings from the differentiation day 60 (DD60) to at least DD130 revealed that AD organoids exhibited transient hyperexcitability and hypersynchrony compared with wild-type (WT) controls, followed by a gradual decline in activity. During the enhanced excitability stage, both elevated Aβ42/40 and Aβ aggregate size showed positive correlations with the percentage of active electrodes and the global synchrony index (GSI) in AD organoids. These findings indicate that Aβ dysregulation might contribute to transient network hyperexcitability in early AD. The results also suggest that patient-derived cerebral organoids may serve as a translational model to examine early network dysfunction and inform future investigations of potential Aβ-induced changes in excitability during the preclinical stages of AD.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by progressive dementia, brain atrophy, and ultimately death. Using cerebral organoids derived from human induced pluripotent stem cells (hiPSCs) carrying the familial PSEN1 A246E variant, we investigated the temporal relationship between amyloid-β (Aβ) dysregulation and spontaneous neuronal activity. Multielectrode array recordings from the differentiation day 60 (DD60) to at least DD130 revealed that AD organoids exhibited transient hyperexcitability and hypersynchrony compared with wild-type (WT) controls, followed by a gradual decline in activity. During the enhanced excitability stage, both elevated Aβ42/40 and Aβ aggregate size showed positive correlations with the percentage of active electrodes and the global synchrony index (GSI) in AD organoids. These findings indicate that Aβ dysregulation might contribute to transient network hyperexcitability in early AD. The results also suggest that patient-derived cerebral organoids may serve as a translational model to examine early network dysfunction and inform future investigations of potential Aβ-induced changes in excitability during the preclinical stages of AD.

Objective: Memory impairments are common in neurological disorders, especially drug‐resistant epilepsy, where cognitive deficits such as accelerated long‐term forgetting may occur before surgery due to interictal/ictal discharges, antiseizure medications, and disease progression. Postsurgical memory decline affects 40%–60% of patients. Understanding the neural mechanisms of memory encoding is essential for identifying critical network hubs and guiding surgical interventions that preserve cognitive function. Methods: We analyzed intracranial electroencephalographic recordings from epilepsy patients ( n = 15) performing a verbal memory encoding task to identify key structures involved in successful memory formation. Functional connectivity was computed using multiple features across several frequency bands. Temporal dynamics of connectivity were examined to characterize pre‐encoding and encoding phases. Results: The middle temporal gyrus (MTG) consistently emerged as a verbal memory hub across features and frequency bands. The inferior frontal gyrus (IFG) was identified as a beta‐band‐specific hub via phase synchrony. Temporal analyses revealed that MTG exhibited stable and widespread connectivity differences across all encoding phases, suggesting a role in pre‐encoding network priming. In contrast, IFG displayed a transient decrease in connectivity and spatial extent during early encoding, indicating a more focal and dynamic contribution. Significance: These findings suggest that successful memory encoding relies on widespread, frequency‐specific connectivity patterns centered on MTG, complemented by transient, localized IFG activity. Identifying such hubs may inform surgical planning by highlighting regions critical for cognitive outcomes, ultimately supporting strategies to preserve memory in epilepsy surgery.

Objective. Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic electrode contacts in intracranial electroencephalography (iEEG) data poses significant challenges for automatic localization methods. This review evaluates methodologies for handling the class imbalance in EZ localization studies that use machine learning (ML). Approach. We systematically reviewed studies employing ML to localize the EZ from iEEG data, focusing on strategies for addressing class imbalance in data handling, algorithm design, and evaluation. Results. Out of 2,128 screened studies, 35 fulfilled the inclusion criteria. Across the studies, the iEEG contacts annotated as epileptogenic prior to automatic localization constituted a median of 18.34% of all contacts. However, many of these studies did not adequately address the class imbalance problem. Techniques such as data resampling and cost-sensitive learning were used to mitigate the class imbalance problem, but the chosen evaluation metrics often failed to account for it. Significance. Class imbalance significantly impacts the reliability of EZ localization models. More comprehensive management and innovative approaches are needed to enhance the robustness and clinical utility of these models. Addressing class imbalance in ML models for EZ localization will improve both the predictive performance and reliability of these models.

Objective: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo‐electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high‐frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction.Methods: In 50 patients we analyzed 30 min of SEEG recorded during non–rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1–500 Hz); the second model used low‐frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision‐recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed‐rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test.Results: The AUPRC score of the random chance classifier was .098. The models (broad‐band, low‐frequency, high‐frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution.Significance: Low‐frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

Objective: Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug‐resistant epilepsy patients. Epileptic spikes, high‐frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for preresective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies.Methods: Interictal awake stereoelectroencephalographic recordings of 33 patients with focal drug‐resistant epilepsy with seizure‐free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross‐correlation, and phase consistency) were computed for neighboring electrode pairs.Results: Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures, with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy.Significance: The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery.

Objective: High‐frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high‐frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy‐to‐use novel interictal biomarker of the epileptic tissue.Methods: We evaluated relative entropy and high‐frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure‐free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision‐recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions.Results: Relative entropy in the ripple band (80–250 Hz) achieved an average AUROC of .85. The normalized high‐frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high‐frequency oscillations, relative entropy did not require any patient‐level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency.Significance: Although both relative entropy and high‐frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy‐to‐use promising biomarker of the epileptogenic zone.