Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/280321

Synaptic transmission modulates while non-synaptic processes govern the transition from pre-ictal to seizure activity in vitro

Bikson, M.·Ruiz-Nuno, A.·Miranda, D.·Kronberg, G.·Jiruska, P.·Fox, J. E.·Jefferys, J. G. R.
View original source ↗Export metadata ↓

Abstract

It is well established that non-synaptic mechanisms can generate electrographic seizures after blockade of synaptic function. We investigated the interaction of intact synaptic activity with non-synaptic mechanisms in the isolated CA1 region of rat hippocampal slices using the \"elevated-K+\" model of epilepsy. Elevated K+ ictal bursts share waveform features with other models of electrographic seizures, including non-synaptic models where chemical synaptic transmission is suppressed, such as the low-Ca2+model. These features include a prolonged (several seconds) negative field shift associated with neuronal depolarization and superimposed population spikes. When population spikes are disrupted for up to several seconds, intracellular recording demonstrated that the prolonged suppression of population spikes during ictal activity was due to depolarization block of neurons. Elevated-K+ ictal bursts were often preceded by a build-up of \"pre-ictal\" epileptiform discharges that were characterized as either \"slow-transition\" (localized and with a gradual increase in population spike amplitude, reminiscent non-synaptic neuronal aggregate formation, presumed mediated by extracellular K+ concentrations ([K+])o accumulation), or \"fast-transition\" (with a sudden increase in population spike amplitude, presumed mediated by field effects). When ictal activity had a fast-transition it was preceded by fast-transition pre-ictal activity; otherwise population spikes developed gradually at ictal event onset. Addition of bicuculline, a GABAA receptor antagonist, suppressed population spike generation during ictal activity, reduced pre-ictal activity, and increased the frequency of ictal discharges. Nipecotic acid and NNC-711, both of which block GABA re-uptake, increased population spike amplitude during ictal bursts and promoted the generation of preictal activity. By contrast, addition of ionotropic glutamate-receptor antagonists (NBQX, D-APV) had no consistent effect on ictal burst waveform or frequency and did not fully suppress pre-ictal activity. Similarly, CGP 55848, a GABAB receptor antagonist, has no significant effect on pre-ictal activity or burst frequency (although it did increase burst duration slightly). Our results are consistent with the hypothesis that non-synaptic mechanisms underpin the generation of ictal bursts in CA1 and that GABAA synaptic mechanisms can shape event development by delaying event initiation and counteracting depolarization block.

Explore related subjects

neurosciencePreprint

Keep this discovery

BibTeXRIS

Bikson, M., Ruiz-Nuno, A., Miranda, D., Kronberg, G., Jiruska, P., Fox, J. E., Jefferys, J. G. R.. 2018-03-11. Synaptic transmission modulates while non-synaptic processes govern the transition from pre-ictal to seizure activity in vitro. https://doi.org/10.1101/280321

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↗