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Advancements in Animal Welfare Research at ETH Zurich
At ETH Zurich, researchers are harnessing artificial intelligence to better analyze laboratory mice behavior, a development that not only enhances research efficiency but also aims to minimize the number of animals used in experiments.
Animal researchers face a unique challenge: assessing the well-being of their subjects through behavioral observations since they cannot directly inquire about their feelings as one could with humans. Led by Professor Johannes Bohacek from the Institute for Neuroscience at ETH Zurich, a team has introduced an innovative method that significantly enhances the way mouse behavior is evaluated.
This novel approach employs automated behavioral analysis powered by machine vision and artificial intelligence. Mice are recorded on video, and these recordings are processed through automated systems. Traditionally, analyzing animal behavior has necessitated extensive manual effort, often consuming days of painstaking work. Recently, leading laboratories have begun shifting toward these automated methods for behavior analysis.
Addressing the Data Dilemma
However, the transition to automated analysis generates a significant challenge: the vast amounts of data collected. With more data comes a higher chance of misinterpretation, where algorithms might incorrectly classify behaviors as significant. In response to this dilemma, researchers have typically been encouraged to increase sample sizes to mitigate the effects of potential artifacts and yield reliable outcomes.
The new methodology developed by ETH researchers allows for insightful analysis of subtle behavioral differences using fewer subjects. This approach not only enhances the ability to derive meaningful conclusions but also contributes to the principles of the 3Rs—replace, reduce, refine—promoted by ETH Zurich and other research bodies. The goal is to substitute animal research with alternative techniques or lessen the use of animals through improved experimental designs.
Focusing on Behavioral Stability
The ETH team’s method is adept at recognizing distinct behavioral patterns in mice while also examining the transitions between different behaviors. Mice exhibit specific behaviors such as rearing up on their hind legs when curious or sticking to cage walls when feeling wary. Interestingly, even a stationary mouse can reveal much about its state—indicating either heightened alertness or uncertainty.
Behavioral transitions are particularly telling. If a mouse frequently shifts between patterns, it may indicate stress or nervousness, whereas consistent behavior changes suggest confidence and relaxation. The researchers have devised a mathematical framework that combines these behavior transitions into a single, quantifiable value, thereby enhancing the statistical reliability of their analyses.
Enhancing Research Comparability
Professor Bohacek, who specializes in neuroscience and stress research, is investigating how different brain mechanisms influence an animal’s capacity to manage stress. “If we can utilize behavioral analyses to predict an individual’s stress-handling abilities, we can delve deeper into the underlying neural mechanisms,” he notes. This research may also inform potential therapeutic strategies for vulnerable human populations.
With this innovative approach, the team at ETH Zurich has already gained insights into how mice respond to stressors and medications. Their statistical enhancements allow them to detect even subtle behavioral variations among individual animals, revealing how acute and chronic stress affect behavior differently and are associated with diverse brain mechanisms.
The methodology also promotes greater standardization across tests, facilitating better comparison of results from different research initiatives, thereby advancing the collective understanding in the field.
Advancing Animal Welfare in Scientific Research
Professor Bohacek emphasizes that the integration of artificial intelligence and machine learning in behavioral analysis promotes ethical and efficient biomedical research practices. For several years, his team has been dedicated to advancing the 3R principles through the establishment of the 3R Hub at ETH Zurich, which aims to improve animal welfare in research settings.
“Celebrating our success with this new method marks a significant milestone for the 3R Hub,” states Oliver Sturman, the Hub’s head and a co-author of this study. The Hub now plays a crucial role in disseminating this methodology to researchers within and beyond ETH Zurich. “Implementing new 3R strategies can often be a significant challenge for many laboratories,” he adds. The 3R Hub aims to ease this transition by providing practical support to enhance animal welfare in research endeavors.
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www.sciencedaily.com