bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.08.06.668900

Variable E-field properties of dual-site tACS

Huertas-Penen, S.·Piastra, M. C.·Puonti, O.·Schwab, B. C.
View original source ↗Export metadata ↓

Abstract

BackgroundDual-site transcranial alternating current stimulation (ds-tACS) enables the modulation of interregional functional connectivity by introducing a phase lag between the stimulating currents. However, overlapping electric fields (E-fields), particularly in closely spaced cortical targets like the primary motor cortices (M1s), may unintentionally alter E-field characteristics and confound the interpretation of functional connectivity modulation. ObjectiveWe aimed to systematically evaluate how different phase lags affect key E-field characteristics when using high-definition ds-tACS, particularly when targeting the M1s. We sought to determine which montage configuration best preserved stable E-field characteristics and investigated whether indi-vidualised montage selection could enhance control over E-field consistency. MethodsWe used individualised finite-element method simulations based on MRI-derived head models to quantify the effects of different phase lags on E-field characteristics. E-field magnitude, normal component, spatial distribution, directionality, and effective stimulation area were assessed for nine montages and eight phase lags. ResultsAll E-field properties, including E-field peak magnitude, peak magnitude of the normal component, redistribution, difference to optimal directionality, and effective area of stimulation, were modulated significantly across differ-ent phase lags for all tested montages. Furthermore, we found substantial inter-individual variability in all E-field properties. Individual selection of montages improved critical properties, particularly the E-field directionality. ConclusionsIn contrast to common assumptions, variations in the phase lag can significantly affect key E-field properties of high-definition ds-tACS. Therefore, we recommend considering modulations of the E-field characteristics when comparing physiological or behavioural effects of ds-tACS at different phase lags. Moreover, given the high inter-individual variability, we suggest the individualisation of montages to the most relevant E-field property. HighlightsO_LIDual-site tACS is often used to modulate functional connectivity. C_LIO_LIChanges in E-field characteristics with varying phase lags are undesirable. C_LIO_LIWe used FEM models to quantify these changes across arbitrary phase lags. C_LIO_LIE-field characteristics of all montages varied significantly with the phase lag. C_LIO_LIIndividualised montage selection was able to improve critical characteristics. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/668900v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1c666baorg.highwire.dtl.DTLVardef@8257e4org.highwire.dtl.DTLVardef@1584d7corg.highwire.dtl.DTLVardef@472447_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Huertas-Penen, S., Piastra, M. C., Puonti, O., Schwab, B. C.. 2025-08-09. Variable E-field properties of dual-site tACS. https://doi.org/10.1101/2025.08.06.668900

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↗