Forestwalk: A Machine Learning Workflow Brings New Insights Into Posture and Balance in Rodent Beam Walking.
Saved in:
| Title: | Forestwalk: A Machine Learning Workflow Brings New Insights Into Posture and Balance in Rodent Beam Walking. |
|---|---|
| Authors: | Tozzi, Francesca (AUTHOR), Zhang, Yan‐Ping (AUTHOR), Narayanan, Ramanathan (AUTHOR), Roqueiro, Damian (AUTHOR), O'Connor, Eoin C. (AUTHOR) |
| Source: | European Journal of Neuroscience. Mar2025, Vol. 61 Issue 5, p1-20. 20p. |
| Subjects: | Machine learning, Rodents, Neural development, Equilibrium testing, Random forest algorithms, Posture, Treatment effectiveness, Pose estimation (Computer vision) |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
| 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. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 0953816X |
| DOI: | 10.1111/ejn.70033 |
