【Curr Protoc】<AbstractText>The IntelliCage is a home-cage-based radiofrequency identification (RFID) test system for studying l...

The IntelliCage is a home-cage-based radiofrequency identification (RFID) test system for studying learning behavior of group-housed mice. Specifically, the mice must learn where and how to get water....

Automated home-cage systems measure individual behavior in social groups for days to months. Among these systems, the IntelliCage has become a widely used platform for longitudinal and socially embedded behavioral phenotyping. Yet the analysis layer often remains less standardized than the experiment itself: raw exports, phase definitions, exclusion rules, time alignment, and derived behavioral metrics are transformed by lab-specific scripts that are difficult to audit, compare, or reuse. We present ic-analysis, an open-source Python toolkit for standardized, scriptable, and shareable IntelliCage workflows. The toolkit separates user-defined experiment metadata and workflow scripts from a reusable analysis core with modular analysis and plotting functions, allowing users to flexibly assemble experiment-specific pipelines without editing package internals. Due to its modular design, the analysis core can be applied to a broad range of experimental paradigms, including general activity, exploratory, motivational, cognitive, and social readouts, rather than being limited to a single fixed protocol. It aligns biological phase windows across staggered cage runs and exports plots together with quantitative result tables, applied settings, and audit files that support reproducible and FAIR reporting. Here, we demonstrate this flexible design in a realistic synthetic place-learning/place-reversal experiment with two mouse groups and deliberately offset cage starts. The workflow recovered the implanted behavioral differences while preserving the required experimental-time alignment. Group A showed stronger endpoint saccharin preference (81.7 +/- 1.7% vs. 27.4 +/- 3.4%, p = 1.9e-), higher liquid uptake, faster place-learning onset (56.6 +/- 8.1 vs. 278.3 +/- 33.2 visits), and better reversal performance (64.1 +/- 1.3% vs. 25.6 +/- 1.7% rewarded correct-corner visits) compared to Group B. This demonstration shows how standardized, explicitly defined readouts can turn complex IntelliCage exports into interpretable behavioral profiles while preserving the analysis history needed for inspection and reuse. ic-analysis therefore provides both a working analysis scaffold and an extensible, community-friendly route toward IntelliCage workflows that are easier to reproduce, compare, extend, and share.

The rapidly evolving field of rodent behavior research led to the development of the IntelliCage, enabling observer-independent experiments with mice. Here, we present a detailed protocol for IntelliCage-based automated cognitive profiling of mice that navigates users through all experimental steps. In addition to instructions on how to prepare, execute, and analyze the experiment, we provide empirically informed recommendations and notes that may help new users plan robust and reproducible Inte...

Gastaldi et al. introduce an open-source analysis pipeline for IntelliCage data that can process and analyze different challenges. Additionally, they provide three challenges that evaluate important cognitive domains in neuropsychiatric disorders. These…

Automated home-cage systems measure individual behavior in social groups for days to months. Among these systems, the IntelliCage has become a widely used platform for longitudinal and socially embedd...

Automated home-cage systems measure individual behavior in social groups for days to months. Among these systems, the IntelliCage has become a widely used platform for longitudinal and socially embedded behavioral phenotyping. Yet the analysis layer often remains less standardized than the experiment itself: raw exports, phase definitions, exclusion rules, time alignment, and derived behavioral metrics are transformed by lab-specific scripts that are difficult to audit, compare, or reuse. We present ic-analysis, an open-source Python toolkit for standardized, scriptable, and shareable IntelliCage workflows. The toolkit separates user-defined experiment metadata and workflow scripts from a reusable analysis core with modular analysis and plotting functions, allowing users to flexibly assemble experiment-specific pipelines without editing package internals. Due to its modular design, the analysis core can be applied to a broad range of experimental paradigms, including general activity, exploratory, motivational, cognitive, and social readouts, rather than being limited to a single fixed protocol. It aligns biological phase windows across staggered cage runs and exports plots together with quantitative result tables, applied settings, and audit files that support reproducible and FAIR reporting. Here, we demonstrate this flexible design in a realistic synthetic place-learning/place-reversal experiment with two mouse groups and deliberately offset cage starts. The workflow recovered the implanted behavioral differences while preserving the required experimental-time alignment. Group A showed stronger endpoint saccharin preference (81.7 +/- 1.7% vs. 27.4 +/- 3.4%, p = 1.9e-), higher liquid uptake, faster place-learning onset (56.6 +/- 8.1 vs. 278.3 +/- 33.2 visits), and better reversal performance (64.1 +/- 1.3% vs. 25.6 +/- 1.7% rewarded correct-corner visits) compared to Group B. This demonstration shows how standardized, explicitly defined readouts can turn complex IntelliCage exports into interpretable behavioral profiles while preserving the analysis history needed for inspection and reuse. ic-analysis therefore provides both a working analysis scaffold and an extensible, community-friendly route toward IntelliCage workflows that are easier to reproduce, compare, extend, and share.

Mice have been demonstrated to learn from each other in social interactions, the extent to which this takes place and the strategies involved, however, largely remain to be elucidated beyond spatially and temporally confined tests of social memory retention. Here, we present a method which utilizes and modifies 1) a commercially available tool for automated behavioral testing, the IntelliCage and 2) an open-source solution for 24/7 live animal tracking, the Live Mouse Tracker, to create a powerful method for the investigation of learning behavior in semi-naturalistic group settings. We see a wide range of possible applications, such as for instance the investigation of learning in social interactions, which we present here as an example. In the present study, co-learning did not facilitate place learning over individual learning. While automated annotation of behaviors was effective, markerless animal identification proved unreliable in a highly enriched environment with manifold opportunity of occlusion from video tracking. In response, we here present a rationale for identifying the reliable portion of tracking data, to which we confine the behavioral analysis. Co-learning animals engaged more often in some prosocial interactions with their teammates than with other animals, but they did overall not interact with each other more frequently than with individual learners. Correlative analysis of learning behavior and social interactions did not reveal any particular association between behavior and learning success. While the mechanisms of social learning in mice could not be conclusively elucidated within the scope of this study, we report on the development of a promising tool for presenting manifold learning tasks while tracking their individual and social behaviors to mice in a fully automated manner. Further, we discuss limitations of the current configuration and present an outlook on further improving the method.

Mice have been demonstrated to learn from each other in social interactions, the extent to which this takes place and the strategies involved, however, largely remain to be elucidated beyond spatially and temporally confined tests of social memory retention. Here, we present a method which utilizes and modifies 1) a commercially available tool for automated behavioral testing, the IntelliCage and 2) an open-source solution for 24/7 live animal tracking, the Live Mouse Tracker, to create a powerful method for the investigation of learning behavior in semi-naturalistic group settings. We see a wide range of possible applications, such as for instance the investigation of learning in social interactions, which we present here as an example. In the present study, co-learning did not facilitate place learning over individual learning. While automated annotation of behaviors was effective, markerless animal identification proved unreliable in a highly enriched environment with manifold opportunity of occlusion from video tracking. In response, we here present a rationale for identifying the reliable portion of tracking data, to which we confine the behavioral analysis. Co-learning animals engaged more often in some prosocial interactions with their teammates than with other animals, but they did overall not interact with each other more frequently than with individual learners. Correlative analysis of learning behavior and social interactions did not reveal any particular association between behavior and learning success. While the mechanisms of social learning in mice could not be conclusively elucidated within the scope of this study, we report on the development of a promising tool for presenting manifold learning tasks while tracking their individual and social behaviors to mice in a fully automated manner. Further, we discuss limitations of the current configuration and present an outlook on further improving the method.