bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.09.15.558002

Ventromedial Prefrontal Cortex and Basolateral Amygdala Projections to Ventromedial Striatum Encode Active Avoidance Behavior

Abstract

Active avoidance is a fundamental defensive behavior, defined as performing a voluntary action to escape a noxious stimulus. Active avoidance serves as an adaptive response to threat, ensuring safety. In individuals with anxiety disorders and post-traumatic stress disorder, avoidance behavior can become maladaptive when even safe situations are avoided, at great psychosocial cost. Corticostriatal and amygdalostriatal pathways have been implicated in active avoidance, though their role in learning and expression of this behavior is not fully understood. Projections from the ventromedial prefrontal cortex (vmPFC) to the ventromedial striatum (VMS) have been shown to regulate a variety of defensive behaviors; however, their role in active avoidance has not previously been examined. Additionally, basolateral amygdala (BLA) projections to the VMS have been shown to promote active avoidance in studies utilizing pharmacological inactivation and optogenetic inhibition, though in vivo real time neural activity in this pathway has not been recorded during active avoidance learning. Here we utilized fiber photometry for in vivo recordings of neural activity in vmPFC-VMS and BLA-VMS projections during active avoidance learning and expression. We implemented a two-way signaled active avoidance paradigm in which a light cue served as the conditioned stimulus (CS) signaling an impending foot shock. We examined changes in neural activity in these two projections during CS presentation, onset of active avoidance, and conditioned freezing. We found that vmPFC-VMS projections develop learning-related increases in activity at CS onset across training, while BLA-VMS projections do not show learning-related encoding of the CS. Additionally, we found that both vmPFC-VMS and BLA-VMS projections develop an increase in activity at avoidance onset. No changes in neural activity were observed during cued freezing in either vmPFC-VMS or BLA-VMS projections. Together these results indicate that vmPFC-VMS projections encode both CS and avoidance, while BLA-VMS projections may simply encode avoidance. Finally, we utilized optogenetic inhibition of vmPFC-VMS projections during the CS to investigate the necessity of this pathway for expression of active avoidance behavior. We found that inhibition of vmPFC-VMS projections attenuates learned active avoidance behavior, indicating that activity in this pathway is required for proper active avoidance. In summary, our results demonstrate task-relevant encoding of active avoidance behavior in vmPFC-VMS and BLA-VMS projections.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bullock, T. E., Gunaydin, L. A.. 2023-09-15. Ventromedial Prefrontal Cortex and Basolateral Amygdala Projections to Ventromedial Striatum Encode Active Avoidance Behavior. https://doi.org/10.1101/2023.09.15.558002

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↗