bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.05.14.594166

Shared spatial selectivity in early visual cortex and face-selective brain regions

Morsi, A. Y.·Chow-Wing-Bom, H. T.·Schwarzkopf, D. S.·Goffaux, V.·Dekker, T. M.·Greenwood, J. A.
View original source ↗Export metadata ↓

Abstract

Face recognition relies on dedicated brain regions that are widely considered to show unique selectivity, including a disproportionate vulnerability to face inversion and a relative invariance to stimulus location. This proposed spatial invariance contrasts with accounts of common visuospatial coding whereby high-level category-selective areas inherit spatial properties from earlier regions. Critically, early regions (V1-V3) show characteristic retinotopic variations, with greater cortical sampling along the horizontal than vertical meridian and in the lower than upper field, mirroring established behavioural advantages for face recognition at these locations. We examined whether face-selective regions (OFA, pFus, mFus) share these spatial anisotropies, and whether these properties could drive the observed variations in face recognition. Using wide-field retinotopic mapping with upright and inverted faces ({+/-}21{degrees} eccentricity), we estimated population receptive fields (pRFs) and visual-field coverage. Though pRFs were substantially larger in face-selective regions than in V1-V3, pRF sizes did not vary in line with behavioural anisotropies. In contrast, both early and face-selective regions showed higher pRF numbers and greater visual-field coverage along the horizontal meridian and in the lower field. These sampling differences provide a plausible neural substrate for behavioural anisotropies in face recognition. We also show that pRF numbers in mFus were greater for upright than inverted faces, likely contributing to the perceptual advantage for upright faces. Together, our findings indicate that variations in visual-field sampling within face-selective cortex parallel those of early visual areas, supporting a hierarchical model in which the spatial selectivity of category-selective areas is built on that of earlier regions. Significance statementHigh-level face-selective cortex is often treated as functionally and spatially distinct from early visual areas. We show instead that these regions all share systematic patterns of spatial biases. Using population receptive field (pRF) analyses, both early and face-selective regions showed greater sampling (higher pRF numbers) and increased visual-field coverage along the horizontal than vertical meridian, and in the lower than upper visual field, matching the pattern of anisotropies in face-recognition performance. This relationship was absent for pRF sizes, indicating that behavioural anisotropies are more closely linked to sampling density and coverage. This common pattern of spatial sampling embeds specialised face-processing systems within a hierarchical network in which high-level regions retain fundamental aspects of visuospatial organisation from early visual cortex.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Morsi, A. Y., Chow-Wing-Bom, H. T., Schwarzkopf, D. S., Goffaux, V., Dekker, T. M., Greenwood, J. A.. 2024-05-14. Shared spatial selectivity in early visual cortex and face-selective brain regions. https://doi.org/10.1101/2024.05.14.594166

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↗