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Affective face biases in visual and prefrontal cortex measured with visual entrainment.

Overview

Authors: Nathan M. Petro1, Yi Wei1,2, Ilenia Salsano1, Thomas W. Ward1,3, Hannah J. Okelberry1, Jason A. John1, Ryan Glesinger1, Lucy K. Horne1, Giorgia Picci1,3, Tony W. Wilson1,3
  1. Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
  2. Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, United States
  3. Department of Pharmacology & Neuroscience, Creighton University School of Medicine, Omaha, NE, United States
Institutions: Boys Town National Research Hospital (United States); University of Maryland, College Park (United States); Creighton University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1206
Dates: received 21 October 2025; accepted 6 March 2026; published online 21 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1206 · PMID 42027739 · PMCID PMC13100672 · OpenAlex W7140219025
Open access: diamond, a free copy (OpenAlex)
Status: code on request
Categories: MEG (modality), cognitive (subfield)
Methods: Connectivity, Statistics, Preprocessing, Spectral & time-frequency, fMRI & imaging, Physiology & signal measures
Keywords: visual entrainment, visual competition, affective faces, magnetoencephalography, MEG
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH (R01-MH116782, S10-OD028751, R01-DA056223, R01-DA059542, P20-GM144641)
Citations: not cited yet (Europe PMC); 147 references in the paper

Abstract

Facial expressions are ubiquitous and reliable social cues. Research has shown that affective faces attract attention at the cost of competing visual information, with functional neuroimaging evidence suggesting that the prefrontal cortex plays a critical role in regulating responses to emotional distractors. However, methodological constraints within neuroimaging environments often prevent the measurement of unique neural signals from multiple competing stimuli, limiting the conclusions that can be drawn regarding how affective biases in attention are generated in the brain. In the current study, we used a novel frequency tagging approach with visual entrainment during magnetoencephalography to track the unique neural signals elicited by a task-relevant Gabor patch and a concurrent, spatially overlapping face with either an angry, neutral, or happy expression. The entrainment responses were projected to the cortex using a beamformer, and a competition index was calculated per voxel to determine the bias toward either of the spatially overlapping entrained stimuli. In the prefrontal cortex, we found a stronger Gabor bias for neutral compared to angry and happy expressions, supporting prior functional neuroimaging works which point to the prefrontal cortex as critical to the regulation of emotional distractors. In the calcarine, we found a stronger face bias for angry compared to neutral and happy expressions, replicating prior findings from electroencephalography. The separate entrainment responses were also sensitive to facial expression in several regions commonly implicated in face processing, social cognition, and attention. These data highlight the utility of frequency tagging paradigms for tracking unique neural responses to concurrent and spatially overlapping stimuli, which is critical for the study of social and emotional processing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

Datasets cited

Data and Code Availability

The data used in this article will be made publicly available through the COINS framework at the completion of the study (https://coins.trendscenter.org/). Code may be made available upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 5 keywords, 1 funder, 147 references.

Cite

This paper

Petro, N. M., Wei, Y., Salsano, I., Ward, T. W., Okelberry, H. J., John, J. A., Glesinger, R., Horne, L. K., Picci, G., & Wilson, T. W. (2026). Affective face biases in visual and prefrontal cortex measured with visual entrainment. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1206. https://doi.org/10.1162/imag.a.1206

BibTeX

@article{petro2026affective,
author = {Petro, Nathan M. and Wei, Yi and Salsano, Ilenia and Ward, Thomas W. and Okelberry, Hannah J. and John, Jason A. and Glesinger, Ryan and Horne, Lucy K. and Picci, Giorgia and Wilson, Tony W.},
title = {{Affective face biases in visual and prefrontal cortex measured with visual entrainment}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1206},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1206},
url = {https://doi.org/10.1162/imag.a.1206},
pmid = {42027739},
pmcid = {PMC13100672}
}

RIS

TY - JOUR
AU - Petro, Nathan M.
AU - Wei, Yi
AU - Salsano, Ilenia
AU - Ward, Thomas W.
AU - Okelberry, Hannah J.
AU - John, Jason A.
AU - Glesinger, Ryan
AU - Horne, Lucy K.
AU - Picci, Giorgia
AU - Wilson, Tony W.
TI - Affective face biases in visual and prefrontal cortex measured with visual entrainment
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/21
VL - 4
SP - IMAG.a.1206
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1206
UR - https://doi.org/10.1162/imag.a.1206
LA - en
ER -

CSL-JSON

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