Quantifying unusual neurological movement phenotypes in collective movement phenotypes

Abstract

AbstractBuilding models of anomalous behaviour in animals is important for monitoring animal welfare as well as assessing the efficacy of therapeutic interventions in preclinical trials. In this paper, we describe methods that allow for the automatic discrimination of sheep with a genetic mutation that causes Batten disease from an age-matched control group, using GPS movement traces as input. Batten disease is an autosomal recessive lysosomal storage abnormality with symptoms that are likely to affect the way that those with it move and socialise, including loss of vision and dementia. The sheep in this study displayed a full range of symptoms and during the experiment, the sheep were mixed with a large group of younger animals. We used data obtained from bespoke raw data GPS sensors carried by all animals, with a sampling rate of 1 sample/second and a positional accuracy of around 30cm. The distance covered in each ten minute period and, more specifically, outliers in each period, were used as the basis for estimating the abnormal behaviour. Our results show that, despite the variability in the sample, the bulk of the outliers during the period of observation across six days came from the sheep with Batten disease. Our results point towards the potential of using relatively simple movement metrics in identifying the onset of a phenotype in symptomatically similar conditions.