Mouse Social Interaction Test (MoST): a quantitative computer automated analysis of behavior.

Rodents are the most commonly used preclinical model of human disease assessing the mechanism(s) involved as well as the role of genetics, epigenetics, and pharmacotherapy on this disease as well as identifying vulnerability factors and risk assessment for disease critical in the development of improved treatment strategies. Unfortunately, the majority of rodent preclinical studies utilize single housed approaches where animals are either entirely housed and tested in solitary environments or group housed but tested in solitary environments. This approach, however, ignores the important contribution of social interaction and social behavior. Social interaction in rodents is found to be a major criterion for the ethological validity of rodent species-specific behavioral characteristics (Zurn et al. 2007; Analysis 2011). It is also well established that there is significant and growing number of reports, which illustrates the important role of social environment and social interaction in all diseases, with particularly significance in all neuropsychiatric diseases. Thus, it is imperative that research studies be able to add large-scale evaluations of social interaction and behavior in mice and benefit from automated tracking of behaviors and measurements by removing user bias and by quantifying aspects of behaviors that cannot be assessed by a human observer. Single mouse setups have been used routinely, but cannot be easily extended to multiple-animal studies where social behavior is key, e.g., autism, depression, anxiety, substance and non-substance addictive disorders, aggression, sexual behavior, or parenting. While recent efforts are focusing on multiple-animal tracking alone, a significant limitation remains the lack of insightful measures of social interactions. We present a novel, non-invasive single camera-based automated tracking method described as Mouse Social Test (MoST) and set of measures designed for estimating the interactions of multiple mice at the same time in the same environment interacting freely. Our results show measurement of social interactions and designed to be adaptable and applicable to most existing home cage systems used in research, and provide a greater level of detailed analysis of social behavior than previously possible. The present study describes social behaviors assessed in a home cage environment setup containing six mice that interact freely over long periods of time, and we illustrate how these measures can be interpreted and combined to classify rodent social behaviors. In addition, we illustrate how these measures can be interpreted and combined to classify and analyze comprehensively rodent behaviors involved in several neuropsychiatric diseases as well as provide opportunity for the basic research of rodent behavior previously not possible.

CeleST: computer vision software for quantitative analysis of C. elegans swim behavior reveals novel features of locomotion.

In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim "gaits" in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes "graceful" from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible.

Tracking the swimming motions of C. elegans worms with applications in aging studies.

Quantitative analysis of the swimming motions of C. elegans worms are of critical importance for many gene-related studies on aging. However no automated methods are currently in use. We present a novel training-based method that automatically tracks and segments multiple swimming worms, in challenging imaging conditions. The position of each worm is predicted by comparing its latest motion with a set of previous observations, and then adjusted laterally and longitudinally to fit the image. After segmentation, a variety of measures can be used to assess the evolution of swimming patterns over time, allowing a quantitative comparison of worm populations over their lifetime. The complete software is being evaluated for mass processing in biology laboratories.

Revisiting the evaluation of segmentation results: introducing confidence maps.

We introduce a novel framework, called Confidence Maps Estimating True Segmentations (Comets), to store segmentation references for medical images, combine multiple references, and measure the discrepancy between a segmented object and a reference. The core feature is the use of efficiently encoded confidence maps, which reflect the local variations of blur and the presence of nearby objects. Local confidence values are defined from expert user input, and used to define a new discrepancy error measure, aimed to be directly interpreted quantitatively and qualitatively. We illustrate the use of this framework to compare different segmentation methods and tune a method's parameters.