Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/784215

Age-related changes in human emotional egocentricity: evidence from multi-level neuroimaging

Riva, F.·Lenger, M.·Kronbichler, M.·Lamm, C.·Silani, G.
View original source ↗Export metadata ↓

Abstract

Emotional egocentric bias (EEB) occurs when, due to a partial failure in self-other distinction, empathy for anothers emotions is influenced by our own emotional state. Recent studies have demonstrated that this bias is higher in children, adolescents and older adults than in young adults. In the latter, overcoming emotional egocentrism has been associated with significant activity in the right supramarginal gyrus (rSMG), as well as increased connectivity between rSGM and somatosensory and visual cortices. Investigations on the neural correlates of EEB in adolescents and older adults are missing. We filled this gap, by asking female participants from three different age groups (adolescents, young adults and older adults, N=92) to perform a well-validated EEB task (Silani et al., 2013) in an MRI scanner. A multi-level analysis approach of MRI data including functional segregation, effective connectivity and structural analyses was adopted. Results revealed higher EEB in older compared to young adults and a comparable EEB in adolescents and young adults. Age-related differences in EEB were associated with differences in task-related rSMG connectivity with somatosensory cortices, especially with S2, which acted as a partial mediator between age and EEB. These findings provide further evidence for the crucial role of the rSMG in self-other distinction in the emotional domain, and suggest that the age-related decline in overcoming EEB is best explained by changes in rSMG connectivity rather than decreased regional activity in that area. This advocates a more systematic investigation of task-related connectivity in studies on aging and life-span development of social-cognitive phenomena.\n\nSignificance StatementEmpathy comprises both the ability to identify and share anothers emotional state, and the ability to disentangle ones own from the others emotional state. When self- and other-related emotions are conflicting, empathy might be negatively influenced by egocentric tendencies. This phenomenon is referred to as emotional egocentric bias (EEB), with previous research showing that its extent changes across the life-span. Here, we provide evidence that age-related differences in EEB are mainly associated with age-related changes in rSMG effective connectivity, and in particular that higher EEB in older adults is associated to lower rSMG effective connectivity with somatosensory cortices. These findings suggest the importance, particularly in aging, of intact functional connectivity for optimal socio-cognitive functioning.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Riva, F., Lenger, M., Kronbichler, M., Lamm, C., Silani, G.. 2019-09-26. Age-related changes in human emotional egocentricity: evidence from multi-level neuroimaging. https://doi.org/10.1101/784215

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↗