Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/143123

Decoding The Influence Of Anticipatory States On Visual Perception In The Presence Of Temporal Distractors

Van Ede, F.·Chekroud, S. R.·Stokes, M. G.·Nobre, A. C.
View original source ↗Export metadata ↓

Abstract

While it has long been recognised that anticipatory states amplify early EEG responses to visual targets in humans, it remains unclear how such modulations relate to the actual content of the neural representation, and help prioritise targets among temporally competing distractor stimuli. Using multivariate orientation decoding of high temporal resolution EEG recordings, we first demonstrate that anticipation also increases the amount of stimulus-identity information contained in these early brain responses. By characterising the influence of temporally adjacent distractors on target identity decoding, we additionally reveal that anticipation does not just attenuate distractor interference on target representations but, instead, delay it. Enhanced target decoding and distractor resistance are further predicted by the attenuation of posterior 8-14 Hz alpha oscillations. These findings offer several novel insights into how anticipatory states shape neural representations in service of resolving sensory competition in time, and they highlight the potential of non-invasive multivariate electrophysiology to track cognitive influences on perception in tasks with rapidly changing displays.\n\nHighlightsO_LIAnticipatory states help resolve visual competition in time\nC_LIO_LIAnticipation enhances early target coding and delays distractor interference\nC_LIO_LIAttenuated alpha oscillations also enhance target coding and distractor resistance\nC_LIO_LIEEG decoding is a powerful tool for tracking percepts in rapidly changing displays\nC_LI\n\nSignificance statementWhile the neural mechanisms by which anticipatory states help prioritise inputs that compete in space have received ample scientific investigation, the mechanisms by which the human brain accomplishes such prioritisation for inputs that compete in time remain less well understood. We used high temporal resolution EEG decoding to individuate (and track in time) neural information linked to visual target and distractors stimuli that were presented in close temporal proximity. This revealed that anticipatory states help resolve temporally competing percepts by a combination of enhanced target (but not distractor) coding as well as delayed interference on this target coding caused by temporally adjacent distractors - thus allocating a \"protective temporal window\" for high-fidelity target processing.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

BibTeXRIS

Van Ede, F., Chekroud, S. R., Stokes, M. G., Nobre, A. C.. 2017-05-27. Decoding The Influence Of Anticipatory States On Visual Perception In The Presence Of Temporal Distractors. https://doi.org/10.1101/143123

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↗