Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.06.08.447611

Saccades and pre-saccadic stimulus repetition alter cortical network topology and dynamics: evidence from EEG and graph theoretical analysis

Ghaderi, A. H.·Niemeier, M.·Crawford, J. D.
View original source ↗Export metadata ↓

Abstract

Parietal and frontal cortex are involved in saccade generation, but their output signals also modify visual signals throughout cortex. These signals produce well-documented behavioral phenomena (saccades, saccadic suppression, various perisaccadic perceptual distortions) but their underlying influence on cortical network dynamics is not known. Here, we combined electroencephalography (EEG) with frequency-dependent source localization and graph theory analysis (GTA) to understand how saccades and pre-saccadic visual stimuli interactively alter cortical network dynamics in humans. 21 participants viewed series of 1-3 vertical or horizontal grids, followed by grid with the opposite orientation just before a horizontal saccade or continued fixation. EEG signals from the presaccadic interval (cue + 200ms, or equivalent fixation period) were used for source localization. Source localization (saccade - fixation) identified bilateral dorsomedial frontoparietal activity across frequency bands, whereas stimulus repetition produced band-specific modulations in left prefrontal, posterior parietal, and central-superior frontal and/or parietal cortex, with significant saccade-repetition interactions in frontal and parietal regions. GTA analysis revealed a saccade-specific functional network with major hubs in inferior parietal cortex (alpha) and the frontal eye fields (beta), and major saccade-repetition interactions in left prefrontal (theta) and supramarginal gyrus (gamma). Overall, quantitative measures of whole-brain network topology and dynamics (segregation, integration, synchronization, complexity) were enhanced during the presaccadic interval, but repetition interactions reduced synchronization and complexity. These results show that presaccadic signals have widespread, coherent influence on cortical network dynamics, likely responsible for both saccade production and the perceptual phenomena associated with saccades. HighlightsO_LISource localization & graph theory were used to analyze presaccadic EEG signals C_LIO_LIPresaccadic signals produced band-specific modulations/hubs in parietofrontal cortex C_LIO_LIFrontal/parietal eye fields showed extensive functional connectivity across all lobes C_LIO_LIPresaccadic stimulus repetition further modulated functional network connectivity C_LIO_LISaccades and repetition both influenced network clustering, integration, & complexity C_LI

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ghaderi, A. H., Niemeier, M., Crawford, J. D.. 2021-06-09. Saccades and pre-saccadic stimulus repetition alter cortical network topology and dynamics: evidence from EEG and graph theoretical analysis. https://doi.org/10.1101/2021.06.08.447611

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↗