bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.04.17.537172

On-demand low-frequency stimulation for seizure control: efficacy and behavioral implications

Paschen, E.·Kleis, P.·Vieira, D. M.·Heining, K.·Boehler, C.·Egert, U.·Haeussler, U.·Haas, C. A.
View original source ↗Export metadata ↓

Abstract

Mesial temporal lobe epilepsy (MTLE), the most common form of focal epilepsy in adults, is often refractory to medication and associated with hippocampal sclerosis. Deep brain stimulation represents an alternative treatment option for drug-resistant patients who are ineligible for resective brain surgery. In clinical practice, closed-loop stimulation at high frequencies is applied to interrupt ongoing seizures, yet with a high incidence of false detections, the drawback of delayed seizure-suppressive intervention and limited success in sclerotic tissue. More recently, hippocampal low-frequency stimulation (LFS) has been shown to reduce excitability in clinical settings and prevent seizures in experimental MTLE when applied continuously. However, as the hippocampus is important for navigation and memory, it would be beneficial to stimulate it only on-demand to reduce its exposure to LFS pulses, and to investigate LFS-related effects on cognition. Using the intrahippocampal kainate mouse model, which recapitulates the key features of MTLE, we developed an on-demand LFS setup and investigated its effects on spontaneous seizure activity and hippocampal function. Specifically, our online detection algorithm monitored epileptiform activity in hippocampal local field potential recordings and identified short epileptiform bursts preceding focal seizure clusters, triggering hippocampal LFS to stabilize the network state. In addition, we investigated the acute influence of LFS on behavioral performance, including anxiety-like behavior in the light-dark box test, spatial and non-spatial memory in the object location memory and novel object recognition test, as well as spatial navigation and long-term memory in the Barnes maze. Compared to open-loop stimulation protocols, on-demand LFS was more efficient in preventing focal seizure clusters, as the strong anti-epileptic effect was achieved with a reduced stimulation load. In behavioral tests, chronically epileptic mice were as mobile as healthy controls but showed increased anxiety, an altered spatial learning strategy and impaired memory performance. Most importantly, our experiments ruled out deleterious effects of hippocampal LFS on cognition and even showed alleviation of deficits in long-term memory recall. Taken together, our findings may provide a promising alternative to current therapies, overcoming some of their major limitations, and inspire further investigation of LFS for seizure control in MTLE.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Paschen, E., Kleis, P., Vieira, D. M., Heining, K., Boehler, C., Egert, U., Haeussler, U., Haas, C. A.. 2023-04-17. On-demand low-frequency stimulation for seizure control: efficacy and behavioral implications. https://doi.org/10.1101/2023.04.17.537172

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↗