Towards automated homecage monitoring of group housed rats
Rodent social behavior is important in research on neuropsychiatric disorders, but major limitations hamper progress.
Read More arrow_forwardIn behavioral neuroscience, understanding social interactions in rodents is key
to unlocking insights into neurodevelopmental and psychiatric disorders. Traditional behavioral assays often
rely on manual observations or rudimentary tracking systems that fail to capture the nuances of complex social
interactions.
However, with the advancements in EthoVision’s social tracker, powered by Noldus’ advanced deep learning algorithms, researchers can analyze social behaviors with unprecedented accuracy and efficiency. This advancement takes your behavioral research to the next level by capturing spontaneous, naturalistic behaviors in stress-free conditions.
EthoVision is the only commercially available system that does everything you could want, seamlessly and easily, without compromising on animal welfare.
Why is measuring social behavior important?
Social behavior is a fundamental aspect of neuroscience research. It plays a
crucial role in the study of conditions such as autism spectrum disorders, schizophrenia, and anxiety-related
disorders.
Observing social interactions like grooming, chasing, and huddling provides invaluable insights into brain function and dysfunction. By evaluating behavior in a social setting rather than isolation, researchers can assess how genetic, pharmacological, or environmental factors influence these critical interactions.
A growing trend in behavioral neuroscience
In recent years, there has been a growing trend toward obtaining more
ethologically relevant behavioral data. Researchers are moving away from artificial experimental setups and
placing greater emphasis on studying animals in environments that closely resemble their natural conditions.
This shift is accompanied by a focus on improving housing and handling practices to ensure minimal stress and
maximal validity of behavioral data. By designing studies with these principles in mind, researchers can collect
more accurate, translationally relevant data that better reflects real-world behavior in both animals and
humans.
EthoVision: a revolution in social behavior tracking
EthoVision has long been the gold standard in video tracking software, but the latest versions represent a major breakthrough. With its enhanced deep learning algorithms, EthoVision has several key benefits for those looking to measure social interaction.
- Accurate tracking with minimal intervention: Unlike traditional tracking methods that require colored markers, EthoVision can accurately track two animals with minimal intervention. Simple physical markers, such as shaving a small section of the animal's back or placing a non-invasive marker on the tail, are sufficient for reliable identification.
- Advanced deep learning tracking for social behavior: EthoVision extends its neural network models to social interaction studies, enabling two-subject tracking with great accuracy. This allows researchers to study complex dynamics like social hierarchies and pair-wise interactions more effectively.
- Robust performance in complex conditions: Whether in high-density environments or under varied lighting conditions, EthoVision’s deep learning engine ensures reliable tracking, making it adaptable to diverse experimental setups.
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- A cost-effective solution
- Powerful data selection
- Most cited video tracking system
New innovations with EthoVision 19
With this new release we’ve made the social interaction capabilities of our tracker even better. The improved Re-ID is better at distinguishing your animals as they act. This means that your data is ready within seconds of finishing your recording.
Furthermore, we’ve added some often-requested parameters to analyze. You can now define and measure the following parameters:
- Following. Detected when the subject (actor) follows a conspecific (receiver) within the specified distance, the actor's nose is oriented towards the receiver's center point within the specified angle, and both animals move faster than specified.
- Leaving. Detected when the subject (actor) moves toward a conspecific (receiver) within the specified distance, the angle between the actor's center-nose vector and the receiver's center point is less than specified, and the receiver moves at a velocity lower than specified.
- Approach. Whether in high-density environments or under varied lighting conditions, EthoVision’s deep learning engine ensures reliable tracking, making it adaptable to diverse experimental setups.
- Social contact. Detected when the nose point of the subject (actor) is near a conspecific (receiver) and the actor's center-nose vector is oriented toward the receiver's body points by less than the specified angle.
Extending research with home cage monitoring
While traditional open-field arenas provide valuable data, they can introduce artificial constraints that impact the validity of behavioral studies. Home cage(-like) monitoring provides a more ethologically relevant setting, allowing for the continuous observation of natural behaviors over extended periods. When combining EthoVision with PhenoTyper, researchers benefit from a powerful integrated behavioral system that offers you clear insights into:
Social bonding and group interactions: Without the stress of novel environments, researchers can assess stable social relationships, dominance hierarchies, and affiliative behaviors in freely interacting groups.
Circadian rhythms and sleep patterns: By tracking locomotor activity across light-dark cycles, researchers gain a deeper understanding of sleep-wake behaviors, an essential factor in studies on neurodegenerative diseases and mental health disorders.
Stereotypies and abnormal behaviors: Long-term monitoring increases the likelihood of detecting repetitive behaviors, self-grooming patterns, or aggression, which are crucial indicators in models of autism and obsessive-compulsive disorder.
Cognitive functions in a low-stress environment: Integrating automated cognitive tasks within the home cage allows for the assessment of learning, memory, and decision-making in a setting that minimizes experimenter interference.
This combination provides a robust and well-integrated platform that streamlines behavioral studies, making data collection more efficient and reliable for preclinical research.
The benefits of EthoVision over open source
Compared with open-source tools such as DeepLabCut, EthoVision offers a much more straightforward path to reliable social interaction data. Rather than spending time building, training, and troubleshooting your own workflow, researchers can start measuring behavior immediately with a system that works out of the box.
That ease of use does not come at the expense of flexibility. EthoVision works across different arena types and rodent species without requiring model retraining for each new setup, making it easier to apply consistently across studies. At the same time, users have access to PhD-level scientific support, so help is available from experts who understand both the software and the experimental questions behind the data.
Just as importantly, EthoVision is a platform trusted by thousands of researchers worldwide. That proven track record gives confidence that the system delivers robust, reproducible results for social behavior research.
Implications for preclinical research
The integration of deep learning-powered tracking with home cage(-like) monitoring directly addresses some of the most pressing challenges in preclinical research:
- Reducing stress-induced variability: Traditional behavioral assessments require handling, which can introduce stress and confound results. By monitoring animals in their home environment, EthoVision eliminates this factor, ensuring more reliable data.
- Enhancing study efficiency and scalability: Manual observation is time-consuming and subjective. EthoVision automates tracking and analysis, allowing researchers to scale up studies and collect more data in less time.
- Providing continuous, real-time insights: Many neurological disorders develop gradually. With continuous monitoring over extended periods, researchers can detect subtle behavioral shifts that would otherwise be missed in short-term studies.
- Delivering more translatable results: Behavioral data collected in a naturalistic setting better reflects real-world conditions, improving the translational value of findings to human applications.
- Eliminating human scoring errors: Subjective behavioral scoring is prone to inconsistencies. EthoVision’s deep learning-based tracking standardizes measurements, ensuring consistency across experiments.
Conclusion
EthoVision is not just another tracking system, it is designed to address the
real challenges behavioral researchers face today. With over 30 years of experience, Noldus has built solutions
from the ground up, guided by validated data and continuous research input.
By integrating advanced deep learning capabilities with home-cage monitoring in the PhenoTyper, we provide a
powerful platform for collecting high-quality, short- and long-term behavioral data with minimal intervention.
Refining experimental methodology and accelerating breakthroughs in neuroscience, ultimately leading to more
effective treatments for neuropsychiatric disorders.
Ready to see how EthoVision can improve your research? Contact us today for a demo or visit
noldus.com/ethovision-xt to explore how we can help you improve your behavioral research.
Socially housing mice webinar
The benefits of deep learning based tracking
The days of contour tracking are behind us. With the power of AI algorithms you can track rodents with great accuracy. In this webinar you can learn more about why this method is superior for you research application.
