Abstract
Quantification and parametrisation of movement are widely used in animal behavioural paradigms. In particular, free movement in controlled conditions (e.g., open field paradigm) is used as a “proxy for indices of baseline and drug-induced behavioural changes. However, the analysis of this is often time- and labour-intensive and existing algorithms do not always classify the behaviour correctly. Here, we propose a new approach to quantify behaviour in an unconstrained environment: searching for frequent patterns (k-motifs) in the time series representing the position of the subject over time. Validation of this method was performed using subchronic quinpirole-induced changes in open field experiment behaviours in rodents. Analysis of this data was performed using k-motifs as features to better classify subjects into experimental groups on the basis of behaviour in the open field. Our classifier using k-motifs gives as high as 94% accuracy in classifying repetitive behaviour versus controls which is a substantial improvement compared to currently available methods including using standard feature definitions (depending on the choice of feature set and classification strategy, accuracy up to 88%). Furthermore, visualisation of the movement/time patterns is highly predictive of these behaviours. By using machine learning, this can be applied to behavioural analysis across experimental paradigms.
Highlights
Quantification and parametrisation of movement are widely used in animal behavioural paradigms
The resulting footage was processed by a video tracking system Ethovision 3.0 from Noldus Information Technology BV, the Netherlands (Wageningen, The Netherlands)[6], which produced a multivariate time series for each 30-minute session, representing multiple variables (Supplementary Table 1, Supplementary Material 1: Experimental datasets)
Using the bin divisions based on Symbolic aggregate approximation (SAX) algorithm, the entropy is 2.63 (Fig. 4A)
Summary
Quantification and parametrisation of movement are widely used in animal behavioural paradigms. We propose a new approach to quantify behaviour in an unconstrained environment: searching for frequent patterns (k-motifs) in the time series representing the position of the subject over time Validation of this method was performed using subchronic quinpirole-induced changes in open field experiment behaviours in rodents. Analysis of this data was performed using k-motifs as features to better classify subjects into experimental groups on the basis of behaviour in the open field. Differences between groups in terms of observed open-field behaviour are assumed to result from the difference in intervention deployed These experimental interventions include changes to the objects in the open field, introduction of another animal, differences in the amount/ type of food available or administration of drugs expected to induce behavioural changes[7]
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