Malte Lorbach1, Elisavet I Kyriakou2, Ronald Poppe3, Elsbeth A van Dam4, Lucas P J J Noldus4, Remco C Veltkamp3. 1. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands; Noldus Information Technology BV, Nieuwe Kanaal 5, 6709 PA Wageningen, The Netherlands. Electronic address: m.t.lorbach@uu.nl. 2. Department of Cognitive Neuroscience, Radboud University Medical Centre, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Noldus Information Technology BV, Nieuwe Kanaal 5, 6709 PA Wageningen, The Netherlands. 3. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands. 4. Noldus Information Technology BV, Nieuwe Kanaal 5, 6709 PA Wageningen, The Netherlands.
Abstract
BACKGROUND: Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. NEW METHOD: To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. RESULTS: We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. COMPARISON WITH EXISTING METHODS: Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. CONCLUSIONS: With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings.
BACKGROUND: Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. NEW METHOD: To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. RESULTS: We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. COMPARISON WITH EXISTING METHODS: Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. CONCLUSIONS: With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings.
Authors: Maria Gulinello; Heather A Mitchell; Qiang Chang; W Timothy O'Brien; Zhaolan Zhou; Ted Abel; Li Wang; Joshua G Corbin; Surabi Veeraragavan; Rodney C Samaco; Nick A Andrews; Michela Fagiolini; Toby B Cole; Thomas M Burbacher; Jacqueline N Crawley Journal: Neurobiol Learn Mem Date: 2018-01-04 Impact factor: 2.877
Authors: Fabrizio Grieco; Briana J Bernstein; Barbara Biemans; Lior Bikovski; C Joseph Burnett; Jesse D Cushman; Elsbeth A van Dam; Sydney A Fry; Bar Richmond-Hacham; Judith R Homberg; Martien J H Kas; Helmut W Kessels; Bastijn Koopmans; Michael J Krashes; Vaishnav Krishnan; Sreemathi Logan; Maarten Loos; Katharine E McCann; Qendresa Parduzi; Chaim G Pick; Thomas D Prevot; Gernot Riedel; Lianne Robinson; Mina Sadighi; August B Smit; William Sonntag; Reinko F Roelofs; Ruud A J Tegelenbosch; Lucas P J J Noldus Journal: Front Behav Neurosci Date: 2021-09-24 Impact factor: 3.617