ArticleThe European journal of neuroscience2025
Forestwalk: A Machine Learning Workflow Brings New Insights Into Posture and Balance in Rodent Beam Walking.
Article in The European journal of neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Does advancement in marker-less pose-estimation mean more quality research? A systematic review.Frontiers in behavioral neuroscience · 2025Pooled it
- Cross-species identification of conserved and divergent locomotor kinematic strategies using AutoGaitA.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Time-dependent changes in stepping performance and velocity following partial dopaminergic lesions in the SNc of male and female rats.PloS one · 2026Article
- Polyherbal Therapy Restores Sensorimotor Function in Chronic Stress-induced Oxidative Damage.Archives of Razi Institute · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The beam walk is widely used to study coordination and balance in rodents. While the task has ethological validity, the main endpoints of "foot slip counts" and "time to cross" are prone to human-rater variability and offer limited sensitivity and specificity. We asked if machine learning-based methods could reveal previously hidden, but biologically relevant, insights from the task. Marker-less pose estimation, using DeepLabCut, was deployed to label 13 anatomical key points on mice traversing the beam. Next, we automated classical endpoint detection, including foot slips, with high recall (> 90%) and precision (> 80%). Using data derived from key point tracking, a total of 395 features were engineered and a random forest classifier deployed that, together with skeletal visualizations, could test for group differences and identify determinant features. This workflow, named Forestwalk, uncovered pharmacological treatment effects in C57BL/6J mice, revealed phenotypes in transgenic mice used to study Angelman syndrome and SLC6A1-related neurodevelopmental disorder, and will facilitate a deeper understanding of how the brain controls balance in health and disease.
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Registered trials
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.