Big behavior: challenges and opportunities in a new era of deep behavior profiling

Abstract

The assessment of rodent behavior forms a cornerstone of preclinical assessment in neuroscience research. Nonetheless, the true and almost limitless potential of behavioral analysis has been inaccessible to scientists until very recently. Now, in the age of machine vision and deep learning, it is possible to extract and quantify almost infinite numbers of behavioral variables, to break behaviors down into subcategories and even into small behavioral units, syllables or motifs. However, the rapidly growing field of behavioral neuroethology is experiencing birthing pains. The community has not yet consolidated its methods, and new algorithms transfer poorly between labs. Benchmarking experiments as well as the large, well-annotated behavior datasets required are missing. Meanwhile, big data problems have started arising and we currently lack platforms for sharing large datasets—akin to sequencing repositories in genomics. Additionally, the average behavioral research lab does not have access to the latest tools to extract and analyze behavior, as their implementation requires advanced computational skills. Even so, the field is brimming with excitement and boundless opportunity. This review aims to highlight the potential of recent developments in the field of behavioral analysis, whilst trying to guide a consensus on practical issues concerning data collection and data sharing.

A machine-vision approach for automated pain measurement at millisecond timescales

“…Demonstrating the robustness of the platform, we identify an outlier mouse strain that displays heightened pain sensitivity, and we accurately measure a heightened pain state when we simultaneously activate pain in the periphery and brain using chemogenetic and natural stimuli. Behavioral neuroscience in model organisms is undergoing a renaissance with the emergence of new tools to automatically track and measure behavior [11,[24][25][26][27][28][29][30]. This renaissance is coincident with many in the research community questioning the robustness of rodent models of pain, addiction, depression, anxiety, and other neuropsychiatric disorders.…”

“…Behavioral neuroscience in model organisms is undergoing a renaissance with the emergence of new tools to automatically track and measure behavior ( Datta et al, 2019 ; Fried et al, 2020 ; von Ziegler et al, 2020 ; Günel et al, 2019 ; Graving et al, 2019 ; Itskovits et al, 2017 ; Kabra et al, 2013 ; Klibaite et al, 2017 ). This renaissance is coincident with many in the research community questioning the robustness of rodent models of pain, addiction, depression, anxiety, and other neuropsychiatric disorders.…”

Real-time, low-latency closed-loop feedback using markerless posture tracking

“…One such state-of-the-art animal pose estimation package, DeepLabCut (DLC; Mathis et al, 2018b ), provides tailored networks that predict the posture of animals of interest based on video frames, and can run swiftly in offline batch processing modes (up to 2500 FPS on standard GPUs; Mathis et al, 2020a ; Mathis and Warren, 2018 ). This high-throughput analysis has proven to be an invaluable tool to probe the neural mechanisms of behavior ( Mathis and Mathis, 2020 ; von Ziegler et al, 2021 ; Mathis et al, 2020b ). The ability to apply these behavioral analysis tools to provide feedback to animals in real time is crucial for causally testing the behavioral functions of specific neural circuits.…”

Real-time, low-latency closed-loop feedback using markerless posture tracking

“…One such state-of-the-art animal pose estimation package, DeepLabCut (DLC, 4), provides tailored networks that predict the posture of animals of interest based on video frames, and can run swiftly in offline batch processing modes (up to 2,500 FPS on standard GPUs; 9, 10). This high throughput analysis has proven to be an invaluable tool to probe the neural mechanisms of behavior (8,11).…”