Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.08.18.670921

Closed-Loop Connectivity Best Supports Angular Tuning and Sleep Dynamics in a Biophysical Thalamocortical Circuit Model

Abstract

Despite recent advancements in mapping thalamic and cortical projections, the specific organization of intrathalamic and corticothalamic connectivity remains elusive. Current experimental approaches cannot definitively determine whether these connections are arranged in reciprocal (closed-) or non-reciprocal (open-loop) circuits. We developed a biophysically detailed multi-compartmental model of the mouse whisker pathway, built on anatomical and physiological data. We showed that closed-loop intrathalamic projections between the thalamocortical (TC) relay neurons in the ventral posteromedial nucleus and the inhibitory neurons in the thalamic reticular nucleus (TRN) best reproduce thalamic spiking and local field potential responses across awake and sleep states. Increasing the percentage of closed-loop projections regulates the angular tuning in the awake state, while also supporting spindle oscillations during sleep. We also showed that direct activation of closed-loop corticothalamic feedback (CT[->]TC and CT[->]TRN), simulating TC inputs, sharpens the angular tuning in the thalamus. These results contribute to resolving a long-standing question regarding the organization of intrathalamic projections, offering insights into how thalamo-cortical circuits balance precise sensory tuning with robust oscillatory rhythms across behavioral states. Moreover, all model resources are open-source and available to other researchers interested in studying thalamocortical circuits. Author summaryA long standing question in the study of thalamocortical interactions is whether neurons in the thalamus form so-called open- or closed-loops when they project within the thalamus and to the cortex. In this study, we used a detailed computational model of the thalamic neurons in the whisker pathway of the mouse with realistic biophysics to investigate this question. We evaluated the impact of different connectivity arrangements in reproducing the activity of thalamic neurons observed during wakefulness and sleep. Our results show that a closed-loop circuit arrangement provides the best alternative to reproduce wake and sleep neuronal responses, highlighting the importance of computational modeling as a tool to disentangle thalamic circuit organization. We also showed that closed-loop projections from the cortex to the thalamus help further amplify the selectivity of thalamic neurons, suggesting a similar organization of corticothalamic projections. We hope our predictions can inform future experiments, and elucidate principles of thalamocortical connectivity that can be generalized across other thalamocortical motifs besides the whisker pathway and across species.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Moreira, J. V., Borges, F. d. S., Atherton, Z., Crandall, S. R., Varela, C., Dura-Bernal, S.. 2025-08-22. Closed-Loop Connectivity Best Supports Angular Tuning and Sleep Dynamics in a Biophysical Thalamocortical Circuit Model. https://doi.org/10.1101/2025.08.18.670921

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗