A novel naïve Bayes approach to identifying grooming behaviors in the force-plate actometric platform.
animal models
force-plate actometer
grooming
naïve Bayes classifier
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
27 Nov 2023
27 Nov 2023
Historique:
received:
09
07
2023
revised:
27
10
2023
accepted:
24
11
2023
medline:
30
11
2023
pubmed:
30
11
2023
entrez:
29
11
2023
Statut:
aheadofprint
Résumé
Self-grooming behavior in rodents serves as a valuable behavioral index for investigating stereotyped and perseverative responses. Most current grooming analyses rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer. Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are input into a naïve Bayes classifier, trained with manual video observations. The effectiveness of this method was tested using CIN-d mice, an animal model developed through early-life depletion of striatal cholinergic interneurons (CIN-d) and featuring prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording. The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations than controls. However, this elevation was not correlated with increases in grooming force. Notably, the dopaminergic antagonist haloperidol reduced grooming force and duration. In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force. Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of neuropsychiatric disorders.
Sections du résumé
BACKGROUND
BACKGROUND
Self-grooming behavior in rodents serves as a valuable behavioral index for investigating stereotyped and perseverative responses. Most current grooming analyses rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer.
NEW METHOD
METHODS
Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are input into a naïve Bayes classifier, trained with manual video observations. The effectiveness of this method was tested using CIN-d mice, an animal model developed through early-life depletion of striatal cholinergic interneurons (CIN-d) and featuring prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording.
RESULTS
RESULTS
The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations than controls. However, this elevation was not correlated with increases in grooming force. Notably, the dopaminergic antagonist haloperidol reduced grooming force and duration.
COMPARISON WITH EXISTING METHODS
METHODS
In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force.
CONCLUSIONS
CONCLUSIONS
Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of neuropsychiatric disorders.
Identifiants
pubmed: 38029972
pii: S0165-0270(23)00245-5
doi: 10.1016/j.jneumeth.2023.110026
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
110026Commentaires et corrections
Type : UpdateOf
Informations de copyright
Copyright © 2023. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marco Bortolato reports financial support was provided by National Institutes of Health. Christopher Pittenger reports financial support was provided by National Institutes of Health. Stefan Pulst reports financial support was provided by National Institutes of Health.