Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A machine learning approach to immobility detection in mice during the tail suspension test for depressive-type behavior analysis

A machine learning approach to immobility detection in mice during the tail suspension test for... PurposeThe tail suspension test (TST) is a widely used technique for assessing antidepressant-like activity of new compounds and medicine. The objective of this work, therefore, was the development of a novel computerized approach, based on artificial intelligence and video analysis of the experimentation procedure, for the standardization of the TST.MethodsVideos of the TST were acquired in a controlled environment. A convolutional neural network (CNN) was used to infer the bounding-boxes of the rear paws in the videos. Other machine learning techniques were used and compared to classify the movement status of the rodent: support vector machines (SVMs), decision trees (DTs), random forests (RFs), multi-layer perceptrons (MLPs), and k-nearest neighbours (kNNs). pre-processing techniques, attribute selection and post-processing steps were performed to provide data correction, improve results and to provide a response more similar to that of humans.ResultsThe CNN achieved 87.7% of success in the paw identification problem. In the movement classification, DTs achieved the smallest mean inference time (1 ms). Comparing our results with the analysis of human researchers, we obtained approximately 95% accuracy in detecting the animal’s mobility states.ConclusionThe proposed approach opens a window of possibilities for point-of-need devices and their applications, particularly in neuroscience and neuroimmunology and may allow reduction in the number of animals and drugs used during the experiment due to the precision and reliability of the system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

A machine learning approach to immobility detection in mice during the tail suspension test for depressive-type behavior analysis

Download PDF
12 pages

Loading next page...
 
/lp/springer-journals/a-machine-learning-approach-to-immobility-detection-in-mice-during-the-WahcPd0N0P
Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-022-00246-8
Publisher site
See Article on Publisher Site

Abstract

PurposeThe tail suspension test (TST) is a widely used technique for assessing antidepressant-like activity of new compounds and medicine. The objective of this work, therefore, was the development of a novel computerized approach, based on artificial intelligence and video analysis of the experimentation procedure, for the standardization of the TST.MethodsVideos of the TST were acquired in a controlled environment. A convolutional neural network (CNN) was used to infer the bounding-boxes of the rear paws in the videos. Other machine learning techniques were used and compared to classify the movement status of the rodent: support vector machines (SVMs), decision trees (DTs), random forests (RFs), multi-layer perceptrons (MLPs), and k-nearest neighbours (kNNs). pre-processing techniques, attribute selection and post-processing steps were performed to provide data correction, improve results and to provide a response more similar to that of humans.ResultsThe CNN achieved 87.7% of success in the paw identification problem. In the movement classification, DTs achieved the smallest mean inference time (1 ms). Comparing our results with the analysis of human researchers, we obtained approximately 95% accuracy in detecting the animal’s mobility states.ConclusionThe proposed approach opens a window of possibilities for point-of-need devices and their applications, particularly in neuroscience and neuroimmunology and may allow reduction in the number of animals and drugs used during the experiment due to the precision and reliability of the system.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 1, 2023

Keywords: Tail suspension test; Neural network; Machine learning; Depression

References

  • Computer-assisted behavioral assessment of pavlovian fear conditioning in mice
    Anagnostaras, SG; Josselyn, SA; Frankland, PW
  • Artificial intelligence for drug toxicity and safety
    Basile, AO; Yahi, A; Tatonetti, NP
  • Random forests
    Breiman, L
  • Support-vector networks
    Cortes, C; Vapnik, V
  • Nearest neighbor pattern classification
    Cover, T; Hart, P
  • Risk factors for suicide in individuals with depression: a systematic review
    Hawton, K; i Comabella, CC; Haw, C
  • Automated experimental system capturing three behavioral components during murine forced swim test
    Hayashi, E; Shimamura, M; Kuratani, K
  • The usage of video analysis system for detection of immobility in the tail suspension test in mice
    Juszczak, GR; Sliwa, AT; Wolak, P
  • A system for the real-time tracking of operant behavior as an application of 3d camera
    Kuroda, T
  • Quantitative traits for the tail suspension test: automation, optimization, and bxd ri mapping
    Lad, HV; Liu, L; Payá-Cano, JL
  • Deepdsc: a deep learning method to predict drug sensitivity of cancer cell lines
    Li, M; Wang, Y; Zheng, R
  • Antidepressant-like effect of pramipexole in an inflammatory model of depression
    Lieberknecht, V; Cunha, MP; Junqueira, SC
  • Classification and regression trees
    Loh, WY
  • Intelligent image-activated cell sorting
    Nitta, N; Sugimura, T; Isozaki, A
  • Scikit-learn: machine learning in Python
    Pedregosa, F; Varoquaux, G; Gramfort, A
  • The tail suspension test: a new method for screening antidepressants in mice
    Steru, L; Chermat, R; Thierry, B
  • Antidepressant-like effect of terpineol in an inflammatory model of depression: involvement of the cannabinoid system and d2 dopamine receptor
    Vieira, G; Cavalli, J; Gonçalves, ECD
  • Andrographolide exerts significant antidepressant-like effects involving the hippocampal bdnf system in mice
    Zhang, JJ; Gao, TT; Wang, Y