bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.12.01.518725

Network topology in brain tumor patients with and without structural epilepsy- a prospective MEG-study

Ladisich, B.·Rampp, S.·Trinka, E.·Weisz, N.·Schwartz, C.·Kraus, T.·Sherif, C.·Marhold, F.·Demarchi, G.
View original source ↗Export metadata ↓

Abstract

It has been proposed that functional connectivity (FC) and network topology (NT) are altered in patients with glial brain tumors. So far there is no consensus on the pattern of these changes, furthermore data on FC in patients with brain metastases (BMs) as well as on the presence and absence of tumor-related epilepsy is lacking. We aimed to analyze preoperative NT of newly diagnosed, singular, supratentorial glial brain tumors (GBTs) and BMs with and without structural epilepsy. FC and NT derived from resting state magnetoencephalography (MEG) were compared between patients (PAs) and matched healthy controls (HCs), between GBTs and BMs as well as between patients with and without structural epilepsy. We investigated all patients, who met our inclusion criteria from February 2019 to March 2021. Clinical data was collected from patients electronic medical charts. We analyzed whole brain (wb) connectivity in six frequency bands, calculated three different network topological parameters (node degree (ND), shortest path length (L), clustering coefficient (CC)) and performed a stratification, where differences in the power were to be found. For data analysis we used Fieldtrip, Brain Connectivity Matlab toolboxes and in-house built scripts. We consecutively included 41 patients (21 men), mean age 60.1ys (range 23-82), who were operated on at our institution. Tumor histology included high-grade gliomas (n=18), low-grade gliomas (n=4), dysembryoplastic neuroepithelial tumor (DNET, n=1), BMs (n=14) and others (n=4). Statistical analysis revealed a significant decrease for wb ND in patients compared to healthy controls in every frequency range analyzed at the descriptive and corrected level (p1-30Hz=0.002, p{gamma}=0.002, p{beta}=0.002, p=0.002, p{theta}=0.024, p{delta}=0.002). Furthermore, at the descriptive level, we found a significant augmentation for wb CC (p1-30Hz=0.031, p{delta}=0.013) in PAs compared to HCs, which did not persist the false discovery rate (FDR) correction. There were no differences in the networks of glial brain tumors and metastases identified. However, we found a significant increase in wb CC in patients with structural epilepsy (p{theta}= 0.048), and significantly lower wb ND (p= 0.039) at the uncorrected level. Our data suggests that network topology is altered in brain tumor patients, which is in line with previous studies. Tumor histology per se might not influence the brains functional network, however, tumor-related epilepsy seems to do so. Longitudinal studies and in-depth analysis of possible factors and confounders might be helpful to further substantiate these findings.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ladisich, B., Rampp, S., Trinka, E., Weisz, N., Schwartz, C., Kraus, T., Sherif, C., Marhold, F., Demarchi, G.. 2022-12-02. Network topology in brain tumor patients with and without structural epilepsy- a prospective MEG-study. https://doi.org/10.1101/2022.12.01.518725

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗