BACKGROUND:Glioblastoma (GB) is the most aggressive primary brain tumor in adults. Tumor-associated epilepsy at diagnosis (TAE) is common, yet its prognostic significance remains unclear. METHODS:We analyzed a retrospective multicenter test cohort of 855 GB patients (Aachen, Hamburg, Bielefeld) and validated findings in a prospectively collected cohort of 344 patients (Erlangen). Survival was assessed using multivariable Cox regression, propensity score matching, and interaction modeling of TAE and extent of resection (EOR). Molecular profiling included methylation-based classification, epigenetic deconvolution, and spatial transcriptomics. RESULTS:TAE was independently associated with improved survival (HR 0.81, 95% CI 0.69-0.99, P = .036, absolute survival advantage ∼4-5 months). This effect was validated in the independent cohort (C-index 0.68 (95% CI 0.62-0.74) and persisted in propensity-matched analyses (HR 0.74, 95% CI 0.56-0.96, P = .027). Interaction modeling revealed that gross total resection (GTR) improved survival in both groups but particularly in patients with TAE (EOR interaction HR 0.69, 95% CI 0.49-0.99, P = .041). In this subgroup, partial resection provided no significant advantage over biopsy, whereas patients without seizures benefited incrementally from both partial resection and GTR. Molecular analysis demonstrated enrichment of the RTK II subtype, differentiated cell states, and an inflammatory microenvironment in glioblastoma with TAE; tumors without seizures displayed neuronal and stem-like features. Functional validation using Electrogenomics showed that glioblastoma cortical slices with increased inflammatory score exhibited synchronization of action potentials characteristic for seizure-like epileptiform activity. CONCLUSIONS:TAE at diagnosis is a favorable prognostic marker in GB, defining a biologically distinct subgroup. Seizure status modifies the prognostic effect of surgical resection, underscoring the importance of GTR particularly in patients presenting with TAE.

Glioblastoma (GB) integrates into the human brain by exploiting spatially restricted neuronal microenvironments at tumor margins. Combining magnetoencephalography-based functional mapping in patients with GB, spatially resolved biopsies, single-cell RNA sequencing, spatial transcriptomics, and electrophysiological profiling in human cortical slice models, we identify peritumoral connectivity hubs enriched for synaptogenic and immature neuronal programs. Connectivity-rich regions preferentially involve projection-capable excitatory neurons and display convergent activation of the WNK-SPAK-NKCC1 chloride homeostasis, consistent with a reduction in inhibition via GABAergic transmission in peritumoral neurons. GB-conditioned neurons exhibit developmental regression, impaired inhibitory control, and immature dendritic spine remodeling while network-level recordings reveal a GABA-sensitive reorganization of circuit topology. NKCC1 blockade reduces tumor-neuron synaptic integration and dampens functional network connectivity. Clinically, peritumoral degree centrality is an independent predictor of survival beyond established covariates. These findings establish neuronal immaturity as a functional substrate for tumor-neuron connectivity and define actionable biomarkers and pathways at the tumor-brain interface.

Abstract Micro-electrode array (MEA) recordings are widely used to characterize functional connectivity in neural cultures and have gained traction for the analysis of human brain slices. However, the impact of graph construction methodology on the resulting network topology has not been systematically quantified. Here, we benchmark three methods - shared spiking activity, Pearson cross-correlation, and the spike time tiling coefficient (STTC) - across 37 recordings from human cortical slice cultures classified into low, moderate, and high activity groups. We show that method choice alone produces large topological differences (Cohen’s d = 0.86–1.14 for clustering coefficient, d > 1.0 for node count), while higher-order features such as modularity remain stable. Each method exhibits a distinct sensitivity profile: shared spiking detects activity-dependent changes primarily through network size, correlation uniquely captures clustering differences, and STTC combines strong biological sensitivity with negligible parameter dependence across lag windows (all d < 0.1). Within shared spiking, z-score normalization dominates all other parameter choices (d > 1.0 versus bin size effects of d < 0.23), functioning as an implicit analytical null model that fundamentally reshapes the edge set rather than merely rescaling weights. Inter-method edge overlap is low (Jaccard index 0.08–0.45) and activity dependent, demonstrating that these methods identify substantially different connections from identical data. Our results reveal that methodological choices including construction method, threshold, and normalization introduce hidden degrees of freedom with effect sizes comparable to the biological signals being measured. We provide practical recommendations for parameter selection, reporting, and cross-method validation in MEA-based network neuroscience. Author Summary When we record electrical activity from brain tissue using grids of electrodes, we can ask how different sites influence one another and map the tissue as a network of connections. Thanks to novel culturing methods, this approach is increasingly used to study human brain slices. However, deciding what is “connected” is not well defined. Researchers use several different methods, and it has never been clear how much this choice shapes the network they end up describing. Here we compared three widely used methods on 37 recordings from human cortical slices spanning a range of activity levels. We found that the method alone can change the apparent structure of the network as much as real biological differences do. The methods frequently disagreed about which connections exist and some technical choices, including normalization techniques, had surprisingly large effects. Because these hidden choices can rival the biological signal, we provide this benchmarking work with practical recommendations for selecting, reporting, and cross-checking methods, so that network studies of brain tissue become more transparent, comparable, and reproducible.

BACKGROUND:Glioblastomas are functionally integrated into their peritumoral neural environment, and the dynamic functional interaction can be analyzed using network theory, providing insights into the tumor-brain interface. We investigated the peritumoral network connectedness of glioblastomas, revealing its association with distinct epigenetic signatures, its influence on survival, and its susceptibility to modification through surgical treatment. METHODS:Resting-state fMRI was performed on 48 glioblastoma patients. Tumor lesions were segmented, and networks were constructed at 10 mm and 40 mm distances from the tumor margin. These networks were mirrored to the healthy hemisphere to compare lesional and contralesional networks. The difference between lesional and contralesional mean degree centrality was calculated to assess the peritumoral network connectedness. Its correlation with epigenetic signatures and effect on overall survival were analyzed. Surgery-induced changes in the peritumoral network connectedness were evaluated in 7 patients with follow-up data. RESULTS:Mean degree centrality was significantly higher in the lesional compared to the contralesional network (P = .032), indicating a tumor-induced effect on its local environment and reflecting high peritumoral network connectedness. Glioblastomas with a neural high epigenetic signature exhibited increased peritumoral network connectedness (P = .010), which was associated with decreased survival (P = .036). Postoperative peritumoral network connectedness tended to decrease, suggesting that surgical resection disrupts the functional communication between the tumor and its peritumoral environment. CONCLUSIONS:The role of network features in predicting patient survival suggests their clinical relevance as imaging biomarkers for assessing personalized treatment strategies, which may include targeting crucial nodes for disconnection or even neuromodulation of neural circuits.