Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.05.09.491140

Distinct cortical networks subserve spatio-temporal sampling in vision through different oscillatory rhythms

Abstract

Although visual input arrives continuously, sensory information is segmented into discrete events. Here, the neural correlates of spatiotemporal binding in male/female human subjects were investigated with MEG using two tasks where separate flashes were presented on each trial but were perceived, in a bi-stable way, as either a single, or two separate, events. The first task (two-flash fusion: TFF) involved judging one versus two flashes while in the second task (apparent motion: AM) participants judged coherent motion versus two stationary flashes. Results indicate two different functional networks underlying two unique aspects of visual temporal binding. In the TFF task, involving an integration window of {approx}50 ms, evoked responses differed as a function of perceptual interpretation by {approx}25 ms after stimuli presentation. Multivariate decoding of subjective perception based on prestimulus oscillatory phase was significant for alpha-band activity in the right medial temporal (MT) area, with the strength of pre-stimulus connectivity between early visual areas and MT being predictive of performance. In contrast, the longer integration window ({approx}130 ms) for AM showed evoked field differences only {approx}250 ms after stimuli onset. Phase decoding of the perceptual outcome in the AM task was strongest for theta-band activity, localized to a right intra-parietal sulcus (IPS) source. Pre-stimulus connectivity between MT and IPS seeds in the theta band best predicted perceptual outcome. Overall, these results show a strong relationship between specific spatiotemporal binding windows and specific oscillations, linked to the information flow between different areas of the "where" and "when" visual processing pathways. Significance StatementMultiple neural rhythms seem relevant for sampling visual information across space and time, but the cortical networks underlying these fundamental computational principles of the visual system remain unexplored. We filled this gap by employing source-level multivariate decoding and connectivity analyses of magnetoencephalographic data recorded during an integration/segregation task of temporal and spatio-temporal events. We identified a first and faster network involving early visual areas (V2 to MT/V5) that determines the basic temporal resolution of visual perception at the speed of the alpha rhythm, and a second slower network involving parietal regions (IPS) that had a key role in the integration of more complex spatiotemporal events at a theta speed. These findings elucidate the neural mechanisms that transfer sensory information into temporal sequences.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ronconi, L., Balestrieri, E., Baldauf, D., Melcher, D.. 2022-05-10. Distinct cortical networks subserve spatio-temporal sampling in vision through different oscillatory rhythms. https://doi.org/10.1101/2022.05.09.491140

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↗