bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.07.03.601129

Cross-validating the electrophysiological markers of early face categorization

Abstract

Human face categorization has been widely studied using electroencephalogram (EEG) and event related potentials (ERP). Within this context, the N170 ERP component has emerged as the earliest and most robust neural marker of face categorization, documented in thousands of studies conducted over nearly three decades. However, in the last decade there has been a surge in research using the fast periodic visual stimulation (FPVS) methodology to investigate face categorization. FPVS studies have consistently reported robust bilateral face categorization responses over the occipitotemporal cortex with a right hemispheric dominance, closely mirroring the N170 scalp topography. Yet, the question remains whether the neural response elicited in FPVS can be considered a proxy for the N170 or if it might be driven by different components. To address this issue, we recorded the electrophysiological signals of human observers who viewed natural images of faces and non-face objects during FPVS and three different ERP paradigms. We quantified the FPVS response in the frequency domain and extracted ERP visual components, including the P1, N170 and P2 in response to face stimuli, from both the ERP paradigms as well as the time domain of the FPVS response. Our results revealed little relationship between any single ERP component and the FPVS frequency response. Across methodologies, only the peak-to-peak differences between N170 and P2 components significantly and consistently explained the FPVS frequency response. Our data show that the FPVS frequency response is not singularly contingent on any isolated ERP component, such as the N170, but rather reflects a later complex neural integration. These findings raise crucial methodological and theoretical considerations on the relationship between FPVS and ERP responses, urging caution when interpreting the neurofunctional role of both electrophysiological signals. While both markers are indicative of human face categorization, they appear to capture different stages in this cognitive process.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yildirim-Keles, F. Z., Stacchi, L., Caldara, R.. 2024-07-05. Cross-validating the electrophysiological markers of early face categorization. https://doi.org/10.1101/2024.07.03.601129

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↗