bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.05.26.493596

Seeing social: A neural signature for conscious perception of social interactions

Abstract

Percepts of ambiguous information are subjective and depend on observers traits and mental states. Social information is some of the most ambiguous content we encounter in our daily lives, yet in experimental contexts, percepts of social interactions--i.e., whether an interaction is present and if so, the nature of that interaction--are often dichotomized as correct or incorrect based on experimenter-assigned labels. Here, we investigated the behavioral and neural correlates of conscious social perception using a large dataset in which neurotypical individuals viewed animations of geometric shapes during fMRI and indicated whether they perceived a social interaction or random motion. Critically, rather than experimenter-assigned labels, we used observers own reports of "Social" or "Non-social" to classify percepts and characterize brain activity, including leveraging a particularly ambiguous animation perceived as "Social" by some observers but "Non-social" by others to control for visual input. Observers were biased toward perceiving information as social (versus non-social), and activity across much of the brain was higher during animations ultimately perceived as social. Using "Unsure" reports, we identified several regions that responded parametrically to perceived socialness. Neural responses to social versus nonsocial content diverged early both in time and in the cortical hierarchy. Lastly, individuals with higher internalizing trait scores showed both a higher response bias towards social and an inverse relationship with activity in default-mode and limbic regions while scanning for social information. Findings underscore the subjective nature of social perception and the importance of using observer reports to study percepts of social interactions. Significance StatementSimple animations involving two or more geometric shapes have been used as a gold standard to understand social cognition and impairments thereof. Yet experimenter-assigned labels of what is social versus non-social are frequently used as a ground truth, despite the fact that percepts of such ambiguous social stimuli are highly subjective. Here, we used behavioral and fMRI data from a large sample of neurotypical individuals to show that participants responses reveal subtle behavioral biases, help us study neural responses to social content more precisely, and covary with internalizing trait scores. Our findings underscore the subjective nature of social perception and the importance of considering observer reports in studying its behavioral and neural dynamics.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Varrier, R. S., Finn, E. S.. 2022-05-29. Seeing social: A neural signature for conscious perception of social interactions. https://doi.org/10.1101/2022.05.26.493596

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↗