# Behavioral assessment of pain in rodents: advances from evoked responses to spontaneous states and multimodal approaches

**Authors:** Meiqi Li, Yongxin Bao, Mingsen Chen

PMC · DOI: 10.3389/fpain.2026.1739384 · Frontiers in Pain Research · 2026-02-09

## TL;DR

The paper reviews advances in rodent pain assessment, comparing traditional and modern methods to improve objectivity and translational value.

## Contribution

It introduces multimodal and AI-driven approaches to enhance pain measurement in preclinical research.

## Key findings

- Traditional pain assays often lack objectivity and sensitivity.
- Modern techniques using video, neural, and physiological data improve translational outcomes.
- Multimodal datasets and AI pipelines can better align preclinical and clinical pain research.

## Abstract

Pain is widely recognized as a leading global health problem that markedly diminishes quality of life. Although assessment lies at the core of pain medicine, robust quantification remains difficult. In preclinical research, commonly used behavioral assays often blur the distinction between spontaneous pain and stimulus-evoked responses. Here, we review recent advances, clarify the conceptual and operational boundaries between spontaneous and evoked pain, and provide a multidimensional comparison of major traditional behavioral paradigms. To address shortcomings in objectivity and reproducibility, we also summarize emerging evaluation strategies. Finally, leveraging bioinformatics and machine learning, we identify pain-associated metrics and propose building multimodal datasets and AI-driven feature-extraction pipelines to enhance the translational value of animal data for clinical pain research.

Created in BioRender. i, L. (2026) https://BioRender.com/ab5yjcf.Illustration comparing traditional and modern approaches to pain measurement in research. The left side shows traditional methods like thermal, mechanical, and chemical stimuli on mice, indicating flaws like observer bias and low sensitivity. The right side highlights modern techniques using video, neural, and physiological input modalities with improved outcomes, such as increased assay sensitivity and successful phase two trials. Includes graphics of equipment, mice, and data analysis. The overall theme suggests modernization leads to better cross-species alignment and fewer failures in drug approval processes.

Created in BioRender. i, L. (2026) https://BioRender.com/ab5yjcf.

## Linked entities

- **Species:** Mus musculus (taxon 10090)

## Full-text entities

- **Genes:** CACNA1H (calcium voltage-gated channel subunit alpha1 H) [NCBI Gene 8912] {aka CACNA1HB, Cav3.2, ECA6, EIG6, HALD4}, GRIN2B (glutamate ionotropic receptor NMDA type subunit 2B) [NCBI Gene 2904] {aka DEE27, EIEE27, GluN2B, MRD6, NMDAR2B, NR2B}, NTRK2 (neurotrophic receptor tyrosine kinase 2) [NCBI Gene 4915] {aka DEE58, EIEE58, GP145-TrkB, OBHD, TRKB, trk-B}, TRPA1 (transient receptor potential cation channel subfamily A member 1) [NCBI Gene 8989] {aka ANKTM1, FEPS, FEPS1, p120}, SCN9A (sodium voltage-gated channel alpha subunit 9) [NCBI Gene 6335] {aka ETHA, FEB3B, GEFSP7, HSAN2D, NE-NA, NENA}, NGF (nerve growth factor) [NCBI Gene 4803] {aka Beta-NGF, HSAN5, NGFB}, CREB1 (cAMP responsive element binding protein 1) [NCBI Gene 1385] {aka CREB, CREB-1}, CPM (carboxypeptidase M) [NCBI Gene 1368], MAPK1 (mitogen-activated protein kinase 1) [NCBI Gene 5594] {aka ERK, ERK-2, ERK2, ERT1, MAPK2, NS13}, NTRK1 (neurotrophic receptor tyrosine kinase 1) [NCBI Gene 4914] {aka MTC, TRK, TRK1, TRKA, Trk-A, p140-TrkA}, TNF (tumor necrosis factor) [NCBI Gene 7124] {aka DIF, IMD127, TNF-alpha, TNFA, TNFSF2, TNLG1F}
- **Diseases:** CCI (MESH:D020208), neurological (MESH:D009461), ischemia (MESH:D007511), MGS (MESH:D004482), facial pain (MESH:D005157), allodynia (MESH:D006930), acute (MESH:D000208), SCI (MESH:D013119), erythromelalgia (MESH:D004916), migraine (MESH:D008881), tissue injury (MESH:D017695), neuropathy (MESH:D009422), analgesia (MESH:D000699), trigeminal neuralgia (MESH:D014277), contusion (MESH:D003288), HSAN (MESH:D009477), visceral pain (MESH:D059265), chronic pain (MESH:D059350), mucositis (MESH:D052016), Neuropathic pain (MESH:D009437), SNL (MESH:D061227), familial episodic pain syndrome (MESH:C538101), CPA (MESH:C537786), hypersensitivity (MESH:D004342), DN (MESH:D003929), cancer (MESH:D009369), postoperative pain (MESH:D010149), colitis (MESH:D003092), ischemic (MESH:D002545), sensory abnormalities (MESH:D012678), osteolysis (MESH:D010014), joint pain (MESH:D018771), anxiety (MESH:D001007), CPP (MESH:D020288), neuroinflammation (MESH:D000090862), edema (MESH:D004487), chronic inflammation (MESH:D007249), mono-arthritis (MESH:D001168), injuries (MESH:D014947), chronic degenerative (MESH:D019636), BCP (MESH:D001859), demyelination (MESH:D003711), peripheral nerve injury (MESH:D059348), Inflammatory pain (MESH:D010146), compression (MESH:D009408), nerve damage (MESH:D000080902)
- **Chemicals:** dopamine (MESH:D004298), GABA (MESH:D005680), H+ (MESH:D006859), acetic acid (MESH:D019342), CPP (MESH:C014896), formalin (MESH:D005557), calcium (MESH:D002118), glycine (MESH:D005998), ATP (MESH:D000255), PGE2 (MESH:D015232), lactate (MESH:D019344), corticosterone (MESH:D003345), PNAS (MESH:D020135), eicosanoids (MESH:D015777), BioRender (-), LTB4 (MESH:D007975), AITC (MESH:C004471), carrageenan (MESH:D002351)
- **Species:** Sus scrofa (pig, species) [taxon 9823], Oryctolagus cuniculus (domestic rabbit, species) [taxon 9986], Rattus norvegicus (brown rat, species) [taxon 10116], Mus musculus (house mouse, species) [taxon 10090], Rodentia (rodent, order) [taxon 9989], Canis lupus familiaris (dog, subspecies) [taxon 9615], Homo sapiens (human, species) [taxon 9606]
- **Mutations:** I228M
- **Cell lines:** SNL L5 — Rattus norvegicus (Rat), Rat adrenal gland pheochromocytoma, Cancer cell line (CVCL_C128)

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12926469/full.md

## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12926469/full.md

## References

214 references — full list in the complete paper: https://tomesphere.com/paper/PMC12926469/full.md

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Source: https://tomesphere.com/paper/PMC12926469