OCD on TV

A study published today provides an enormous amount of behavioural data, presented in a detailed videographic virtual library, that was used to explore obsessive-compulsive disorder (OCD) in an animal model. With OCD affecting between 1-3% of the general population, and some recent data suggesting that COVID-19 may promote OCD-like behaviours, such a large amount of behavioural data from a model organism serves as an incredible resource for use by the scientific community. This research was carried out by a team led by Henry Szechtman from McMaster University in Canada. This new data set is the culmination of a 15-year study and constitutes 11.1 TB of data from over 2 years of continuous recording, which is enormous given that the current availability of recorded sharable data is nearly zero. The article was published in the journal GigaScience, and the entire set of data is openly available in Federated Research Data Repository (FRDR) with the complete metadata and data links available in GigaScience journal’s affiliated database GigaDB.

Obsessive-Compulsive Disorder (OCD) is characterised by obsessive thoughts and compulsive behaviours, such as incessant washing and cleaning, that can completely interfere with regular daily activities to the point they become incapacitating. Over 50% of individuals with OCD experience severe impairment and 85% experience moderate to severe impairment. The complexity of the disease, given that there is both genetic and environmental components, makes animal models essential for gaining a deeper understanding of the neurobiology of this condition, as well as for providing insights into the mechanisms of the disease and potential treatments. The model used in this study involved testing rats with OCD behaviours by assessing their behavioural performance in a widely used apparatus — a large open field — and recording their behavioural activity in a standardised paradigm under regulated conditions. 

The library of data provided in this study includes the entire set of raw videos of each trial, as well as two video-derived raw data objects, namely, XY locomotion coordinates and plots of animal trajectory. The information from the raw data objects is especially informative, as treatment with drugs or brain lesions are shown to transform the pattern of locomotion of this animal OCD model in both space and time. Having these data openly available to the scientific community allows other researchers to use the data in ways that can go far beyond what the investigators tested in their original research studies.

First author Henry Szechtman, highlighted this, saying: “The re-use potential of the Virtual Library is even greater because of videography: the Virtual Library contains not only the digitised coordinates of locomotion in the open field (from which measures of compulsive checking and the amount of activity can be derived) but also the video record of rat’s activity in the open field. These video recordings constitute the full documentation of the animal behaviour in the predefined environment. As such, they have information beyond the paths of locomotion that were of interest in our experiments.”  

In the 43 experiments, the model behaviours were assessed using, singly or in combination, 9 different drugs as potential treatments for OCD. A subset of these experiments also incorporated a surgical procedure to lesion specific parts of the brain that have been implicated as being integral to the neurophysiology of OCD. Additional subsets of these experiments assessed behavioural changes in the rats when exposed to differing environmental factors, such as the influence of darkness, or the influence of a small ledge, or no objects, in the open field. All of these different tests provide a rich coverage of information for OCD research.

While the data in this study were assessed by humans, this resource can be used in the growing area of machine learning for complex analyses. Szechtman, expands on this, noting: “This vast number of records allows utilisation of Machine Learning/Artificial Intelligence algorithms. For example, these algorithms can be utilised to predict the escalation of compulsive checking in animals treated with different drugs, and the decline in exploratory behaviour of control animals, as well as to identify the range of unique patterns of escalation and decline across individual rats and types of treatments. Other questions can involve examination and prediction of activity patterns; evolution of the places of focus in the environment during development of compulsive checking or as a function of different treatments; identification of outlier cases and predictor variables.

Taken together, these data comprise one of the largest sets of documentation of OCD behavioural information in an animal model. The open availability of such detailed information on a range of analyses and tests serves as an invaluable resource to the community at large for understanding OCD type behaviours and the potential changes different medical treatments can provide.

Additional Information

Data:

Szechtman H; Dvorkin-Gheva A; Gomez-Marin A (2022): Supporting data for "A Virtual Library for Behavioral Performance in Standard Conditions – Rodent Spontaneous Activity in an Open Field during Repeated Testing and after Treatment with Drugs or Brain Lesions" GigaScience Database. http://dx.doi.org/10.5524/102261

Article:

Szechtman H; Dvorkin-Gheva A; Gomez-Marin A. (2022) A Virtual Library for Behavioral Performance in Standard Conditions – Rodent Spontaneous Activity in an Open Field during Repeated Testing and after Treatment with Drugs or Brain Lesions. GigaScience. https://doi.org/10.1093/gigascience/giac092

Media contacts:

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Scott Edmunds, Scott@gigasciencejournal.com, Office: +852 3610 3531 Cell: +852 92490853

Data Editor:

Chris Armit, ChrisA@gigasciencejournal.com 

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About GigaScience Press

GigaScience Press is BGI's Open Access Publishing division, which publishes scientific journals and data. Its publishing projects are carried out with international publishing partners and infrastructure providers, including Oxford University Press and River Valley Technologies. It currently publishes two award-winning data-centric journals: its premier journal GigaScience (launched in 2012), which won the 2018 American Publishers PROSE award for innovation in journal publishing, and its new journal GigaByte (launched 2020), which won the 2022 ALPSP Award for Innovation in Publishing. The press also publishes data, software, and other research objects via its GigaDB.org database. To encourage transparent reporting of scientific research and to enable future access and analyses, it is a requirement of manuscript submission to all GigaScience Press journals that all supporting data and source code be made openly available in GigaDB or in a community approved, publicly available repository.

About GigaScience

GigaScience is co-published by GigaScience Press and Oxford University Press. Winner of the 2018 PROSE award for Innovation in Journal Publishing (Multidisciplinary), the journal covers research that uses or produces 'big data' from the full spectrum of the biological and biomedical sciences. It also serves as a forum for discussing the difficulties of and unique needs for handling large-scale data from all areas of the life and medical sciences. The journal has a completely novel publication format -- one that integrates manuscript publication with complete data hosting, and analyses tool incorporation. To encourage transparent reporting of scientific research as well as enable future access and analyses, it is a requirement of manuscript submission to GigaScience that all supporting data and source code be made available in the GigaScience database, GigaDB, as well as in publicly available repositories. GigaScience will provide users access to associated online tools and workflows, and has integrated a data analysis platform, maximizing the potential utility and re-use of data.