Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

Abbas, Waseem; Rodó, David Masip

doi:10.3390/s19153274

Cited by 15 publications

(9 citation statements)

References 145 publications

(208 reference statements)

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“…Different approaches are possible for motion tracking (Abbas and Masip Rodo 2019). We chose temporal differencing, which computes the pixel-by-pixel difference in consecutive video frames.…”

Section: Resultsmentioning

confidence: 99%

A python-based package for long-lasting video acquisition and semi-automated detection of convulsive seizures in rodents

DIAZ-ARCE

GHOUMA

SCALMANI

et al. 2022

Preprint

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Background: Epilepsy is a very invalidating pathology characterized by the unpredictable appearance of abnormal cerebral activity leading to seizures and co-morbidities. The ability to detect and even predict seizures is a major challenge and many research laboratories are using rodents' models of epilepsy to unravel possible mechanisms. The gold standard to record and detect seizures is electroencephalography, but it is very invasive. For rodents used in research, video analysis is a very interesting approach but the major disadvantages are that it is time consuming, prone to human error, and not very reproducible. Commercial solutions for detailed phenotyping analysis on humans or rodents exist but they are costly. Some open source software programs are also available, they provide very interesting and precise behavior data, but none of them are made for high throughput analysis of a large number of video files generated by long lasting recordings. New method: We developed an open-source python-based package of two software programs that enable automated video acquisition and simple motion analysis associated with a spectral power analysis, which enable a semi-automated identification of convulsive seizures. The method needs cheap webcams and a computer or a server. Results: Using two murine epilepsy models (Nav1.1 mutations), we have compared our motion analysis software to human visual inspection and found an 88.8% accuracy in convulsive seizures detection. We then compare our method to the gold standard electrocorticogram analysis and found a 93.2% accuracy. The motion analysis is also interesting to get a readout of the animal activity without the invasiveness of electromyogram recordings. Conclusions: This new method is easy to use, cost-effective and allows: 1) detection of convulsive seizures in a noninvasive way, 2) high speed analysis of a large number of video files with a good accuracy, and 3) automated acquisition and semi-automated analysis of a very large number of files.

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Section: Resultsmentioning

confidence: 99%

A python-based package for long-lasting video acquisition and semi-automated detection of convulsive seizures in rodents

DIAZ-ARCE

GHOUMA

SCALMANI

et al. 2022

Preprint

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“…Thus, a key advantage of our model is that instead of relying on individual variables to discriminate behavior, it integrates behavior across a number of highly discriminating variables, each of them providing a different weight to the decision-making embedded in the prediction process. Similar approaches have been successfully applied in recent studies on laboratory animals to link specific behaviors and traits from high-density locomotor data to neural substrates [77][78][79] .…”

Section: Discussionmentioning

confidence: 99%

Locomotion in virtual environments predicts cardiovascular responsiveness to subsequent stressful challenges

et al. 2020

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Individuals differ in their physiological responsiveness to stressful challenges, and stress potentiates the development of many diseases. Heart rate variability (HRV), a measure of cardiac vagal break, is emerging as a strong index of physiological stress vulnerability. Thus, it is important to develop tools that identify predictive markers of individual differences in HRV responsiveness without exposing subjects to high stress. Here, using machine learning approaches, we show the strong predictive power of high-dimensional locomotor responses during novelty exploration to predict HRV responsiveness during stress exposure. Locomotor responses are collected in two ecologically valid virtual reality scenarios inspired by the animal literature and stress is elicited and measured in a third threatening virtual scenario. Our model’s predictions generalize to other stressful challenges and outperforms other stress prediction instruments, such as anxiety questionnaires. Our study paves the way for the development of behavioral digital phenotyping tools for early detection of stress-vulnerable individuals.

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“…These marker-based methods have also been used in animals, including mice (Bélanger et al, 1996; Leblond et al, 2003; Abbas and Masip Rodo, 2019). Markers have been used to track mouse whiskers off-line in 3D using multiple high-speed cameras (Snigdha et al, 2011), or to track mouse reaching behavior in real time at 10-ms latency (Becker and Person, 2018).…”

Section: Discussionmentioning

confidence: 99%

Fast, Flexible Closed-Loop Feedback: Tracking Movement in “Real-Millisecond-Time”

Sehara

Bahr²,

Mitchinson

et al. 2019

eNeuro

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One of the principal functions of the brain is to control movement and rapidly adapt behavior to a changing external environment. Over the last decades our ability to monitor activity in the brain, manipulate it while also manipulating the environment the animal moves through, has been tackled with increasing sophistication. However, our ability to track the movement of the animal in real time has not kept pace. Here, we use a dynamic vision sensor (DVS) based event-driven neuromorphic camera system to implement real-time, low-latency tracking of a single whisker that mice can move at ∼25 Hz. The customized DVS system described here converts whisker motion into a series of events that can be used to estimate the position of the whisker and to trigger a position-based output interactively within 2 ms. This neuromorphic chip-based closed-loop system provides feedback rapidly and flexibly. With this system, it becomes possible to use the movement of whiskers or in principal, movement of any part of the body to reward, punish, in a rapidly reconfigurable way. These methods can be used to manipulate behavior, and the neural circuits that help animals adapt to changing values of a sequence of motor actions.

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Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

Cited by 15 publications

References 145 publications

A python-based package for long-lasting video acquisition and semi-automated detection of convulsive seizures in rodents

A python-based package for long-lasting video acquisition and semi-automated detection of convulsive seizures in rodents

Locomotion in virtual environments predicts cardiovascular responsiveness to subsequent stressful challenges

Fast, Flexible Closed-Loop Feedback: Tracking Movement in “Real-Millisecond-Time”

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