Of mice and mates:Automated classification and modelling of mouse behaviour in groups using a single model across cages

Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual’s behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour

Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage

This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modifications to increase performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. To investigate the efficacy of this approach, models were evaluated by dissociating the streams and training/testing in the same rigorous manner as the main classifiers. Using an annotated, publicly available dataset of a singly-housed mice, we achieve prediction accuracy of 86.47% using an ensemble of a Inception-based network and an attention-based network, both of which utilize this feature sharing. We also demonstrate through ablation studies that for all models, the feature-sharing architectures consistently perform better than conventional ones having separate streams. The best performing models were further evaluated on other activity datasets, both mouse and human. Future work will investigate the effectiveness of feature sharing to behavioural classification in the unsupervised anomaly detection domain

Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders

This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual stream, 3D convolutional autoencoder (with residual connections) and a dual stream, 2D convolutional autoencoder. The publicly available dataset used here contains twelve videos of a single home-caged mice alongside frame level annotations. Under the pretext that the autoencoder only sees normal events, the video data was handcrafted to treat each behaviour as a pseudo-anomaly thereby eliminating them from the others during training. The results are presented for one conspicuous behaviour (hang) and one inconspicuous behaviour (groom). The performance of these models is compared to a single stream autoencoder and a supervised learning model, which are both based on the custom CAE encoder. Both models are also tested on the CUHK Avenue dataset were found to perform as well as some state-of-the-art architectures

Mutation in the FUS nuclear localisation signal domain causes neurodevelopmental and systemic metabolic alterations

Variants in the ubiquitously expressed DNA/RNA-binding protein FUS cause aggressive juvenile forms of amyotrophic lateral sclerosis (ALS). Most FUS mutation studies have focused on motor neuron degeneration; little is known about wider systemic or developmental effects. We studied pleiotropic phenotypes in a physiological knock-in mouse model carrying the pathogenic FUSDelta14 mutation in homozygosity. RNA sequencing of multiple organs aimed to identify pathways altered by the mutant protein in the systemic transcriptome, including metabolic tissues, given the link between ALS-frontotemporal dementia and altered metabolism. Few genes were commonly altered across all tissues, and most genes and pathways affected were generally tissue specific. Phenotypic assessment of mice revealed systemic metabolic alterations related to the pathway changes identified. Magnetic resonance imaging brain scans and histological characterisation revealed that homozygous FUSDelta14 brains were smaller than heterozygous and wild-type brains and displayed significant morphological alterations, including a thinner cortex, reduced neuronal number and increased gliosis, which correlated with early cognitive impairment and fatal seizures. These findings show that the disease aetiology of FUS variants can include both neurodevelopmental and systemic alterations

Disruption of the homeodomain transcription factor orthopedia homeobox (Otp) is associated with obesity and anxiety.

OBJECTIVE: Genetic studies in obese rodents and humans can provide novel insights into the mechanisms involved in energy homeostasis. METHODS: In this study, we genetically mapped the chromosomal region underlying the development of severe obesity in a mouse line identified as part of a dominant N-ethyl-N-nitrosourea (ENU) mutagenesis screen. We characterized the metabolic and behavioral phenotype of obese mutant mice and examined changes in hypothalamic gene expression. In humans, we examined genetic data from people with severe early onset obesity. RESULTS: We identified an obese mouse heterozygous for a missense mutation (pR108W) in orthopedia homeobox (Otp), a homeodomain containing transcription factor required for the development of neuroendocrine cell lineages in the hypothalamus, a region of the brain important in the regulation of energy homeostasis. OtpR108W/+ mice exhibit increased food intake, weight gain, and anxiety when in novel environments or singly housed, phenotypes that may be partially explained by reduced hypothalamic expression of oxytocin and arginine vasopressin. R108W affects the highly conserved homeodomain, impairs DNA binding, and alters transcriptional activity in cells. We sequenced OTP in 2548 people with severe early-onset obesity and found a rare heterozygous loss of function variant in the homeodomain (Q153R) in a patient who also had features of attention deficit disorder. CONCLUSIONS: OTP is involved in mammalian energy homeostasis and behavior and appears to be necessary for the development of hypothalamic neural circuits. Further studies will be needed to investigate the contribution of rare variants in OTP to human energy homeostasis

Persistent Object Identification Leveraging Non-Visual Markers

Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion, making standard visual tracking approaches unusable. However, a coarse estimate of each mouse's location is available from a unique RFID implant, so there is the potential to optimally combine information from (weak) tracking with coarse information on identity. To achieve our objective, we make the following key contributions: (a) the formulation of the object identification problem as an assignment problem (solved using Integer Linear Programming), and (b) a novel probabilistic model of the affinity between tracklets and RFID data. The latter is a crucial part of the model, as it provides a principled probabilistic treatment of object detections given coarse localisation. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden

Persistent animal identification leveraging non-visual markers: Tracking and Identification Dataset - Identifications Subset (TIDe-I)

This DataShare dataset pertains to Identification of group-housed mice as documented in the Thesis "Automated Identification and Behaviour Classification for Modelling Social Dynamics in Group-Housed Mice" (2023) by Michael Camilleri. It grew out of a collaboration with the Mary Lyon Centre at MRC Harwell, with the need to automatically identify group-housed mice using only position cues obtained from RFID tags. This sets the problem apart from the usual re-identification challenge, due to the mice have no visual markers to identify them. The challenge is compounded by the multiple mice which must be tracked/identified and the level of occlusion. We provide herein an annotated dataset containing mouse videos, pre-generated bounding-boxes and annotations of identifications at 4s intervals. The dataset can be used to train and evaluate identification methods. Further details are available at https://github.com/michael-camilleri/TIDe. The paper describing our work appears as: M. P. J. Camilleri, L. Zhang, R. S. Bains, A. Zisserman, and C. K. I. Williams, Persistent Animal Identification Leveraging Non-Visual Markers, CoRR (arXiv), cs.CV (2112.06809), Dec. 2021. [Available on arXiv](https://arxiv.org/pdf/2112.06809.pdf)

Persistent animal identification leveraging non-visual markers: Tracking and Identification Dataset - Detections Subset (TIDe-D)

This is the Detections subset of the Tracking and Identification Dataset (TIDe) as described in our paper "Persistent Animal Identification Leveraging Non-Visual Markers" [1] and used in the PhD Thesis "Automated Identification and Behaviour Classification for Modelling Social Dynamics in Group-Housed Mice" 2023 [2]. It grew out of a collaboration with the Mary Lyon Centre at MRC Harwell, with the need to automatically detect mice in single-channel infra-red videos of the home-cage. The challenge lies in the level of occlusion due to the group-housed mice in the enriched home-cage. We provide herein an annotated dataset containing video-frames (as jpeg images) and annotations of mouse and tunnel detections (in CoCo format). The dataset can be used to train and evaluate object detectors. Further details are available at https://github.com/michael-camilleri/TIDe. [1] M. P. J. Camilleri, L. Zhang, R. S. Bains, A. Zisserman, and C. K. I. Williams, “Persistent Animal Identification Leveraging Non-Visual Markers,” CoRR, vol. cs.CV, no. 2112.06809, Dec. 2021. [2] M. P. J. Camilleri, “Automated Identification and Behaviour Classification for Modelling Social Dynamics in Group-Housed Mice,” PhD Thesis, University of Edinburgh, 2023. It is aimed at training and evaluating mouse detectors. Further details are provided at https://github.com/michael-camilleri/TIDe