bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.05.25.115576

Regional activity in the rat anterior cingulate cortex and insula during persistence and quitting in a physical-effort task

Porter, B. S.·Li, K.·Hillman, K. L.
View original source ↗Export metadata ↓

Abstract

As animals carry out behaviors, particularly costly ones, they must constantly assess whether or not to persist in the behavior or quit. The anterior cingulate cortex (ACC) has been shown to assess the value of behaviors and to be especially sensitive to physical effort costs. Complimentary to these functions, the insula is thought to represent the internal state of the animal including factors such as hunger, thirst, and fatigue. Utilizing a novel weight lifting task for rats, we characterized the local field potential (LFP) activity of the ACC and anterior insula (AI) during effort expenditure. In the task male rats are challenged to work for sucrose reward, which costs progressively more effort over time to obtain. Rats are able to quit the task at any point. We found modest shifts in LFP theta (7-9 Hz) activity as the task got progressively more difficult in terms of absolute effort expenditure. However, when the LFP data were analyzed based on the rats relative progress towards quitting the task, or performance state, substantial shifts in LFP power in the theta and gamma (55-100 Hz) frequency bands were observed in ACC and AI. Both ACC and AI theta power decreased as the rats got closer to quitting, while ACC and AI gamma power increased. Furthermore, coherency between ACC and AI in the delta (2-4 Hz) range shifted alongside the rats performance state. Overall we show that ACC and AI LFP activity changes correlate to the rats relative performance state in an effort-based task. Significance StatementAnimals need to assess whether or not a behavior is worth pursuing based on their internal states (e.g., hunger, fatigue) and the costs and benefits of the behavior. However, internal states often change as behaviors are carried out, such as becoming fatigued, necessitating constant reassessment as to whether to continue the behavior or quit. We characterized brain activity in the anterior cingulate cortex and insula, brain regions involved in cost-benefit decision making and internal state representations, respectively, as rats carried out a challenging physical-effort task. Both brain regions showed significant shifts in activity as the rats approached their quitting point. Our study provides one of the first characterizations of neural activity as an animal decides to quit an effortful task.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Porter, B. S., Li, K., Hillman, K. L.. 2020-05-26. Regional activity in the rat anterior cingulate cortex and insula during persistence and quitting in a physical-effort task. https://doi.org/10.1101/2020.05.25.115576

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↗