Evidence map›Paper›PMID 40832811›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Cracking the Valence Code: Patterned Facial Kinematics and Neural Signatures of Emotional Expressions in Mice.

Yujia Chen, Ruiqing Hou, Zhinan Chen, Junli Lu, Si Chen, Shisheng Xiong, Jianfeng Feng, Trevor W Robbins, Haitao Yan, Xiao Xiao

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

10 authors.

Yujia ChenBehavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Department of Endocrinology, Huadong Hospital, Fudan University, Shanghai, 200433, China.ORCID https://orcid.org/0009-0009-2767-7154
Ruiqing HouBehavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Department of Endocrinology, Huadong Hospital, Fudan University, Shanghai, 200433, China.
Zhinan ChenCollege of Future Information Technology, Fudan University, Shanghai, 200433, China.
Junli LuBehavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Department of Endocrinology, Huadong Hospital, Fudan University, Shanghai, 200433, China.
Si ChenDepartment of Educational Psychology, Faculty of Education, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, 999077, China.
Shisheng XiongCollege of Future Information Technology, Fudan University, Shanghai, 200433, China.
Jianfeng FengBehavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Department of Endocrinology, Huadong Hospital, Fudan University, Shanghai, 200433, China.
Trevor W RobbinsDepartment of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK.
Haitao YanState Key Laboratory of National Security Specially Needed Medicines, Beijing, 100039, China.
Xiao XiaoBehavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Department of Endocrinology, Huadong Hospital, Fudan University, Shanghai, 200433, China.ORCID https://orcid.org/0000-0002-4328-0634

Funding

111 Project B18015National Key R&D Program of China 2019YFA0709504National Key R&D Program of China 2021ZD0202805National Natural Science Foundation of China 32471083Pudong Elite Talent ProgramShanghai Center for Brain Science and Brain-Inspired TechnologyShanghai Young Oriental Talent ProgramThe Innovative Research Team of High-level Local Universities in Shanghai
6 · The paper itself

Abstract

Despite advances in linking mouse facial expressions to emotional states, the specific facial features and neural signatures remain elusive. An artificial intelligence (AI)-based framework that decodes mouse facial expressions is presented, revealing stable valence and arousal dimensions analogous to those described in human emotion models. Facial expressions emerge as robust indicators of positive and negative emotional responses, validated through pharmacological manipulations, while responses to hallucinogens highlight the potential of valence-specific prototype modeling for interpreting previously uncharacterized emotional states. Using automated multikeypoint tracking, patterned facial kinematics that are consistent within the same emotional valence are identified. Ear dynamics, in particular, emerge as critical features, offering distinct and sensitive markers of subtle emotional distinctions. Neurocorrelational analyses and optogenetic inhibition targeting the ventral tegmental area further demonstrate the intricate link between facial expressions and valence-specific neural activity in dopaminergic and GABAergic neurons. These findings establish a precise, high-temporal-resolution platform for objectively decoding murine emotional states, advancing the understanding of emotional processing mechanisms and informing the development of mood-regulating therapies.

Indexed as

EmotionsFacial ExpressionAnimalsArtificial IntelligenceBiomechanical PhenomenaMaleMiceMice, Inbred C57BLVentral Tegmental AreaAI‐driven emotion recognitionmouse facial expressionsneural signatures in the VTAoptogenetic modulationvalence‐specific patterns

Identifiers

PMID40832811
PMCPMC12622411

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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.