Quantifying free behaviour in an open field using k-motif approach
Number of pages
SourceScientific Reports, 9, (2019), article 19873
Article / Letter to editor
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PI Group Intention and Action
SW OZ DCC CO
PI Group Memory and Emotion
Subject111 000 Intention & Action; Action, intention, and motor control; All institutes and research themes of the Radboud University Medical Center; Data Science; Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience; Language in Interaction
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.
NWO (Grant code:info:eu-repo/grantAgreement/NWO/Gravitation/024.001.006)
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