Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.08.03.668371

Unfolding spatiotemporal representations of 3D visual perception in the human brain

Abstract

Although visual input is initially recorded in two dimensions on our retinas, we perceive and interact with the world in three dimensions. Achieving 3D perception requires the brain to integrate 2D spatial representations with multiple depth cues, such as binocular disparity. However, most studies typically examine 2D and depth information in isolation, leaving the integrated nature of 3D spatial encoding largely underexplored. In this study, we collected a densely sampled multimodal neuroimaging dataset from 10 participants (8 with EEG and fMRI; 2 with fMRI only) across multiple sessions while they viewed stereoscopic 3D stimuli through red-green anaglyph glasses. Participants first completed a behavioral session including depth judgement tasks and a novel cube adjustment task to quantify and calibrate individual depth perception in units of binocular disparity. Then during two EEG and two fMRI sessions, participants passively viewed stimuli presented at 64 systematically sampled 3D locations, yielding over 66,000 trials in total across ten participants. Combining this multimodal dataset with computational methods via representational similarity analysis, we examined how 2D, depth-related, 3D feature-level, and geometric distance representations unfold across time and brain space. We found that the human brain represents 3D visual space not only by encoding position-in-depth as an additional dimension alongside 2D location, but also by constructing richer forms of 3D spatial structure. Specifically, 2D spatial features were represented earliest and most broadly, depth-related representations were weaker and more spatially restricted, and 3D feature representations were sparse and heterogeneous but detectable at the individual feature level. Critically, geometric distance analyses revealed that neural coding extended beyond separable feature dimensions, showing robust integrated 2D geometric representations and more selective evidence for integrated 3D geometric representations. These findings suggest that human 3D spatial perception is supported by a progression from dominant 2D coding to depth-related and 3D representations, with additional evidence for geometric structure in 3D space, contributing to a more comprehensive understanding of the spatiotemporal organization of neural representations that support 3D perception. Additionally, our novel large dataset will be made openly available to support future research on 3D perception and spatial cognition.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lu, Z., Golomb, J. D.. 2025-08-04. Unfolding spatiotemporal representations of 3D visual perception in the human brain. https://doi.org/10.1101/2025.08.03.668371

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↗