Automated analysis of activity, sleep, and rhythmic behaviour in various animal species with the Rtivity software

Flight to insight: maximizing the potential of Drosophila models of C9orf72-FTD

Advancements in understanding the pathogenesis of C9orf72-associated frontotemporal dementia (C9orf72-FTD) have highlighted the role of repeat-associated non-ATG (RAN) translation and dipeptide repeat proteins (DPRs), with Drosophila melanogaster models providing valuable insights. While studies have primarily focused on RAN translation and DPR toxicity, emerging areas of investigation in fly models have expanded to neuronal dysfunction, autophagy impairment, and synaptic dysfunction, providing potential directions for new therapeutic targets and mechanisms of neurodegeneration. Despite this progress, there are still significant gaps in Drosophila models of C9orf72-FTD, namely in the areas of metabolism and circadian rhythm. Metabolic dysregulation, particularly lipid metabolism, autophagy, and insulin signaling, has been implicated in disease progression with findings from animal models and human patients with C9orf72 repeat expansions. Moreover, circadian disruptions have been observed in C9of72-FTD, with alterations in rest-activity patterns and cellular circadian machinery, suggesting a potential role in disease pathophysiology. Drosophila models offer unique opportunities to explore these aspects of C9orf72-FTD and identify novel therapeutic targets aimed at mitigating neurodegeneration.

Imputing missing sleep data from wearables with neural networks in real-world settings

Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.

Development and validation of a sleep questionnaire, SNoRE 3.0, to evaluate sleep in companion dogs

Disturbances in the sleep-wake cycle are a debilitating, yet rather common condition not only in humans, but also in family dogs. While there is an emerging need for easy-to-use tools to document sleep alterations (in order to ultimately treat and/or prevent them), the veterinary tools which yield objective data (e.g. polysomnography, activity monitors) are both labor intensive and expensive. In this study, we developed a modified version of a previously used sleep questionnaire (SNoRE) and determined criterion validity in companion dogs against polysomnography and physical activity monitors (PAMs). Since a negative correlation between sleep time and cognitive performance in senior dogs has been demonstrated, we evaluated the correlation between the SNoRE scores and the Canine Dementia Scale (CADES, which includes a factor concerning sleep). There was a significant correlation between SNoRE 3.0 questionnaire scores and polysomnography data (latency to NREM sleep, ρ = 0.507, p < 0.001) as well as PAMs' data (activity between 1:00 and 3:00 AM, p < 0.05). There was a moderate positive correlation between the SNoRE 3.0 scores and the CADES scores (ρ = 0.625, p < 0.001). Additionally, the questionnaire structure was validated by a confirmatory factor analysis, and it also showed an adequate test-retest reliability. In conclusion the present paper describes a valid and reliable questionnaire tool, that can be used as a cost-effective way to monitor dog sleep in clinical settings.

A novel method for automatically analysing the activity of fast-moving animals: a case study of Callimico goeldii monkeys housed in a zoological garden

Behavioural indices are recognised as important criteria for assessing animal welfare. One of the basic animal behaviours included in ethograms is their activity. The assessment of fast-moving animals, performed by humans using the visual observation method, is difficult and not very objective. Therefore, the aim of the research was to develop a method of automated analysis of animal activity, particularly useful in the observation of quick and lively individuals, and to prove its suitability for assessing the behaviour of fast-moving animals. A method of automatically assessing animal activity was developed using digital image analysis, with the Python programming language and the OpenCV library being the foundational tools. The research model was Callimico goeldii monkeys housed in a zoological garden. This method has been proved to correlate well (Rs = 0.76) with the visual method of animal behaviour analysis. The developed automatic evaluation of animal behaviour is many times faster than visual analysis, and it enables precise assessment of the daily activity of fast-moving groups of animals. The use of this system makes it possible to obtain an activity index with sub-second resolution, which allows it to be used in online mode as a detector of abnormal animal activity, e.g. early detection of illnesses or sudden events that are manifested by increased or decreased activity in relation to the standard activity pattern.

Disruptions of circadian rhythms, sleep, and stress responses in zebrafish: New infrared-based activity monitoring assays for toxicity assessment

Behavioural disruptions are sensitive indicators of alterations to normal animal physiology and can be used for toxicity assessment. The small vertebrate zebrafish is a leading model organism for toxicological studies. The ability to continuously monitor the toxicity of drugs, pollutants, or environmental changes over several days in zebrafish can have high practical application. Although video-recordings can be used to monitor short-term zebrafish behaviour, it is challenging to videorecord prolonged experiments (e.g. circadian behaviour over several days) because of the darkness periods (nights) and the heavy data storage and image processing requirements. Alternatively, infrared-based activity monitors, widely used in invertebrate models such as drosophila, generate simple and low-storage data and could optimize large-scale prolonged behavioural experiments in zebrafish, thus favouring the implementation of high-throughput testing strategies. Here, we validate the use of a Locomotor Activity Monitor (LAM) to study the behaviour of zebrafish larvae, and we characterize the behavioural phenotypes induced by abnormal light conditions and by the Parkinsonian toxin MPP⁺. When zebrafish were deprived from daily light-cycle synchronization, the LAM detected various circadian disruptions, such as increased activity period, phase shifts, and decreased inter-daily stability. Zebrafish exposed to MPP⁺ (10, 100, 500 μM) showed a concentration-dependent decrease in activity, sleep disruptions, impaired habituation to repetitive startles (visual-motor responses), and a slower recovery to normal activity after the startle-associated stress. These phenotypes evidence the feasibility of using infrared-based LAM to assess multi-parameter behavioural disruptions in zebrafish. The procedures in this study have wide applicability and may yield standard methods for toxicity testing.