Quantifying free behaviour in an open field using k-motif approach
| Autor: | Marein Könings, Katarzyna Kapusta, Jan K. Buitelaar, Mark Blokpoel, Jeffrey C. Glennon, Tom Claassen, Natalia Z. Bielczyk |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science Behavioural analysis lcsh:Medicine Open field Article Language in Interaction 03 medical and health sciences 0302 clinical medicine All institutes and research themes of the Radboud University Medical Center 111 000 Intention & Action lcsh:Science Multidisciplinary Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] business.industry Action intention and motor control lcsh:R Data Science Pattern recognition Scientific data Translational research Visualization 030104 developmental biology lcsh:Q Motif (music) Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports, 9, pp. 1-14 Scientific Reports, 9 Scientific Reports, 9, 1-14 Scientific Reports Scientific Reports, Vol 9, Iss 1, Pp 1-14 (2019) |
| ISSN: | 2045-2322 |
| Popis: | Contains fulltext : 219376.pdf (Publisher’s version ) (Open Access) 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. 14 p. |
| Databáze: | OpenAIRE |
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