Track-Control, an automatic video-based real-time closed-loop behavioral control toolbox

Approaches of optogenetic manipulation of neuronal activity have boosted our understanding of the functional architecture of brain circuits underlying various behaviors. In the meantime, rapid development in computer vision greatly accelerates the automation of behavioral analysis. Real-time and event-triggered interference is often necessary for establishing a tight correlation between neuronal activity and behavioral outcome. However, it is time consuming and easily causes variations when performed manually by experimenters. Here, we describe our Track-Control toolbox, a fully automated system with real-time object detection and low latency closed-loop hardware feedback. We demonstrate that the toolbox can be applied in a broad spectrum of behavioral assays commonly used in the neuroscience field, including open field, plus maze, Morris water maze, real-time place preference, social interaction, and sensory-induced defensive behavior tests. The Track-Control toolbox has proved an efficient and easy-to-use method with excellent flexibility for functional extension. Moreover, the toolbox is free, open source, graphic processing unit (GPU)-independent, and compatible across operating system (OS) platforms. Each lab can easily integrate Track-Control into their existing systems to achieve automation.

[1]  Shriya S Srinivasan,et al.  Closed-loop functional optogenetic stimulation , 2018, Nature Communications.

[2]  Benjamin R. Arenkiel,et al.  Identification of a neurocircuit underlying regulation of feeding by stress-related emotional responses , 2019, Nature Communications.

[3]  Karl Deisseroth,et al.  Closed-Loop and Activity-Guided Optogenetic Control , 2015, Neuron.

[4]  Timothy H Lucas,et al.  Learning active sensing strategies using a sensory brain–machine interface , 2019, Proceedings of the National Academy of Sciences.

[5]  Michael G. Garelick,et al.  Activation of Dopamine Neurons is Critical for Aversive Conditioning and Prevention of Generalized Anxiety , 2011, Nature Neuroscience.

[6]  Matthias Bethge,et al.  Using DeepLabCut for 3D markerless pose estimation across species and behaviors , 2018, Nature Protocols.

[7]  K. Tye,et al.  Resolving the neural circuits of anxiety , 2015, Nature Neuroscience.

[8]  Matthias Bethge,et al.  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning , 2018, Nature Neuroscience.

[9]  Olavo B. Amaral,et al.  A simple webcam-based approach for the measurement of rodent locomotion and other behavioural parameters , 2006, Journal of Neuroscience Methods.

[10]  Kamran Khodakhah,et al.  Cerebellar modulation of the reward circuitry and social behavior , 2019, Science.

[11]  M. Jelen,et al.  Closed-loop optogenetic activation of peripheral or central neurons modulates feeding in freely moving Drosophila , 2019, eLife.

[12]  Kevin T. Beier,et al.  Gating of social reward by oxytocin in the ventral tegmental area , 2017, Science.

[13]  Liqun Luo,et al.  Circuit Architecture of VTA Dopamine Neurons Revealed by Systematic Input-Output Mapping , 2015, Cell.

[14]  Dennis Goldschmidt,et al.  optoPAD, a closed-loop optogenetics system to study the circuit basis of feeding behaviors , 2019, eLife.

[15]  Andrew P. Davison,et al.  A Commitment to Open Source in Neuroscience , 2017, Neuron.

[16]  J. Roeper Dissecting the diversity of midbrain dopamine neurons , 2013, Trends in Neurosciences.

[17]  Ovidiu Lungu,et al.  Consolidation alters motor sequence-specific distributed representations , 2018, bioRxiv.

[18]  S. Amir,et al.  Recording and analysis of circadian rhythms in running-wheel activity in rodents. , 2013, Journal of visualized experiments : JoVE.

[19]  O. Hikosaka,et al.  Two types of dopamine neuron distinctly convey positive and negative motivational signals , 2009, Nature.

[20]  Li I. Zhang,et al.  Transforming Sensory Cues into Aversive Emotion via Septal-Habenular Pathway , 2018, Neuron.

[21]  A. Lüthi,et al.  Neuronal circuits for fear and anxiety , 2015, Nature Reviews Neuroscience.

[22]  T. Branco,et al.  A synaptic threshold mechanism for computing escape decisions , 2018, Nature.

[23]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[24]  Jean-Christophe Olivo-Marin,et al.  Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning , 2019, Nature Biomedical Engineering.

[25]  Alice M Stamatakis,et al.  Activation of lateral habenula inputs to the ventral midbrain promotes behavioral avoidance , 2012, Nature Neuroscience.

[26]  L. Paninski,et al.  Anxiety Cells in a Hippocampal-Hypothalamic Circuit , 2018, Neuron.

[27]  Joseph Redmon,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[28]  Lining Ju,et al.  MouseMove: an open source program for semi-automated analysis of movement and cognitive testing in rodents , 2015, Scientific Reports.

[29]  M. Meister,et al.  Rapid Innate Defensive Responses of Mice to Looming Visual Stimuli , 2013, Current Biology.

[30]  Pankaj Gupta,et al.  Real-time markerless video tracking of body parts in mice using deep neural networks , 2018, bioRxiv.

[31]  Lief E. Fenno,et al.  Amygdala circuitry mediating reversible and bidirectional control of anxiety , 2011, Nature.