Evidence map›Paper›PMID 39814742›Full record

ArticleScientific data2025

A human single-neuron dataset for object recognition.

Runnan Cao, Peter Brunner, Nicholas J Brandmeir, Jon T Willie, Shuo Wang

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Runnan CaoDepartment of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA. r.cao@wustl.edu.ORCID http://orcid.org/0000-0001-5827-9903
Peter BrunnerDepartment of Neurosurgery, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Nicholas J BrandmeirDepartment of Neurosurgery, West Virginia University, Morgantown, WV, 26506, USA.
Jon T WillieDepartment of Neurosurgery, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Shuo WangDepartment of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA. shuowang@wustl.edu.

Funding

Training and DisseminationP41EB018783 · NIBIB · WADSWORTH CENTER · PI Theresa M. Vaughan · 2014 to 2026
$15.4M
An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in HumansU01NS128612 · NINDS · MAYO CLINIC ROCHESTER · PI Peter Brunner, Kai Miller · 2022 to 2026
$4.5M
BCI2000: Software Resource for Adaptive Neurotechnology ResearchU24NS109103 · NINDS · WASHINGTON UNIVERSITY · PI BRUNNER, PETER · 2019 to 2024
$2.8M
Model-Based Investigation of Aberrant Neural Face Representation in AutismR01MH129426 · NIMH · WASHINGTON UNIVERSITY · PI Shuo Wang · 2022 to 2026
$1.9M
Mechanisms of Amygdala-Mediated Memory Enhancement in HumansR01MH120194 · NIMH · WASHINGTON UNIVERSITY · PI WILLIE, JON TIMOTHY · 2020 to 2024
$1.8M
BCI2000+: A Software Platform for Adaptive NeurotechnologiesR01EB026439 · NIBIB · WASHINGTON UNIVERSITY · PI BRUNNER, PETER · 2018 to 2022
$1.5M
Dynamics and Causal Functions of Large-Scale Cortical and Subcortical NetworksU01NS108916 · NINDS · WADSWORTH CENTER · PI WOLPAW, JONATHAN RICKEL · 2018 to 2019
$1.4M
Optimizing Pharmacotherapy with Noninvasive Wearable Sensors and Subscalp EEGUH3NS123066 · NINDS · MAYO CLINIC ROCHESTER · PI Benjamin H Brinkmann, Gregory A Worrell · 2025 to 2026
$1.4M
Non-invasive vagus nerve stimulation to mitigate subarachnoid hemorrhage induced inflammationR21NS128307 · NINDS · WASHINGTON UNIVERSITY · PI BRUNNER, PETER, LEUTHARDT, ERIC CLAUDE · 2023 to 2024
$428k
Delineating the neural computational network for object recognitionK99EY036650 · NEI · WASHINGTON UNIVERSITY · PI CAO, RUNNAN · 2024 to 2025
$228k
National Science Foundation (NSF) BCS-1945230NEI NIH HHS K99 EY036650NIBIB NIH HHS P41 EB018783NIBIB NIH HHS R01 EB026439NIMH NIH HHS R01 MH120194NIMH NIH HHS R01 MH129426NINDS NIH HHS R21 NS128307NINDS NIH HHS U01 NS108916NINDS NIH HHS U01 NS128612NINDS NIH HHS U24 NS109103NINDS NIH HHS UH3 NS123066U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01EB026439U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01MH120194U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01MH129426U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) K99EY036650
6 · The paper itself

Abstract

Object recognition is fundamental to how we interact with and interpret the world around us. The human amygdala and hippocampus play a key role in object recognition, contributing to both the encoding and retrieval of visual information. Here, we recorded single-neuron activity from the human amygdala and hippocampus when neurosurgical epilepsy patients performed a one-back task using naturalistic object stimuli. We employed two sets of naturalistic object images from leading datasets extensively used in primate neural recordings and computer vision models: we recorded 1204 neurons using the ImageNet stimuli, which included broader object categories (10 different images per category for 50 categories), and we recorded 512 neurons using the Microsoft COCO stimuli, which featured a higher number of images per category (50 different images per category for 10 categories). Together, our extensive dataset, offering the highest spatial and temporal resolution currently available in humans, will not only facilitate a comprehensive analysis of the neural correlates of object recognition but also provide valuable opportunities for training and validating computational models.

Indexed as

AmygdalaHippocampusNeuronsPattern Recognition, VisualEpilepsyHumans

Identifiers

PMID39814742
PMCPMC11735812

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.