Session Title: Effects of Cocaine Use
Session Date and Time: 10/22/2019 8:00:00 AM - 10/22/2019 11:00:00 AM
Location: Room S401 (All Nanosymposia will take place in McCormick Place)
Tuesday, October 22, 2019
6:30 –9:00 p.m.
Location: McCormick Place , N230b
Chair: Thomas Pingel
Director Sales & Marketing,
LaVision BioTec/Miltenyi Biotec, Germany
Mapping the mesoscale structural plasticity
of the brain using iDISCO+ and ClearMap
Group Leader, Laboratory of Structural Plasticity, ICM
Brain and Spine Institute Principal Investigator, Inserm,
Aggression reward and relapse:
from behavior to whole brain
Sam A. Golden
Assistant Professor, Department of Biological Structure,
University of Washington, Seattle, WA
Sfn 2019 Dynamic Poster 325.12 (Simon Nilsson): Automated analysis of prosocial and aggressive behaviours using computer vision and machine learning
*S. R. O. NILSSON, J. J. CHOONG, S. A. GOLDEN;
Biol. Structure, Univ. of Washington, Seattle, WA
S.R.O. Nilsson: None. J.J. Choong: None. S.A. Golden: None.
Background. Disrupted social behaviour is a fundamental shared symptom of many neuropsychiatric disorders, including drug addiction, depression and PTSD. However, freely behaving mice are seldom considered in the experimental design of preclinical models. This is predominately due to technical limitations preventing high-throughput, consistent, and unbiased scoring of freely-moving complex social interactions.
Method. We developed predictive classifiers of social and aggressive behaviors during mouse dyadic encounters. Single C57BL/6J mice were placed into the home-cage of a CD-1 mouse and interactions were recorded in variable lighting conditions and different resolutions/frame-rates. We used DeepLabCut (Mathis et al., 2018, Nat Neurosci) to generate a model that tracks eight body-parts on each of the two mice. We detected and reduced tracking inaccuracies and calculated a battery of diverse features (>100) based on body-part movements, distances, angles, sizes, and their deviations across rolling windows. We used the features in sklearn-based machine learning algorithms against multiple socially-relevant targets (e.g., aggressive events, anogenital sniffing, tail rattling, pursuit, lateral threat display) and we visualized the tracking and the predictions with OpenCV.
Results. Model predictions were in excellent or good agreement with manual human frame-by-frame scoring. For example, random forest implementations based on re-sampled data predicted aggressive and tail rattling events with more than 95% accuracy. The model generalized well to new recording conditions.
Conclusion. The data support that complex social behaviors can be readily quantified in an un-biased, fast, and automated way in unmarked individual mice using DeepLabCut for feature detection and our python modules for machine learning.
Session Type: Poster
Session Number: 592
Session Title: Behavioral Neuroendocrinology: Modulation of Defensive and Aggressive Behaviors
Date and Time: Tuesday Nov 6, 2018 1:00 PM - 5:00 PM
Location: San Diego Convention Center: SDCC Halls B-H
Abstract Control Number: 2812
Chaired by Dr. Brian Trainor. Co-chaired by Dr. Alexa Veenema.
Hosted by Dr. Rachel Wong.
Hosted Dr. Asa Magnusson.
Hosted by Dr. Vani Pariyadath.
Hosted by Dr. Nicolas Renier.