ArticleScientific reports2020
Automated detection of the head-twitch response using wavelet scalograms and a deep convolutional neural network.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 45 citations in OpenAlex.
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- Next-Generation MDMA Analogue SDMA: Pharmacological and Metabolic Insights.ACS chemical neuroscience · 2025Article
- Discovery of Highly Selective 5-HTJournal of medicinal chemistry · 2025Article
- Rapid, Open-Source, and Automated Quantification of the Head Twitch Response in C57BL/6J Mice Using DeepLabCut and Simple Behavioral Analysis.ACS pharmacology & translational science · 2025Article
- Analytical and Pharmacological Characterization of 1-(Furan-2-Carbonyl)-LSD (1F-LSD) and Comparison With 1-(Thiophene-2-Carbonyl)-LSD (1T-LSD).Drug testing and analysis · 2025Article
- Are "mystical experiences" essential for antidepressant actions of ketamine and the classic psychedelics?European archives of psychiatry and clinical neuroscience · 2025Review
- Rapid, open-source, and automated quantification of the head twitch response in C57BL/6J mice using DeepLabCut and Simple Behavioral Analysis.bioRxiv : the preprint server for biology · 2025Article
- Assessing the effects of 5-HTPharmacological reports : PR · 2025Article
- Serotonin-2 Receptor Agonists Produce Anti-inflammatory Effects through Functionally Selective Mechanisms That Involve the Suppression of Disease-Induced Arginase 1 Expression.ACS pharmacology & translational science · 2024Article
- Identification of 5-HTNature communications · 2023Article
- Article
- Use of the head-twitch response to investigate the structure-activity relationships of 4-thio-substituted 2,5-dimethoxyphenylalkylamines.Psychopharmacology · 2023Article
- Role of 5-HT2A, 5-HT2C, 5-HT1A and TAAR1 Receptors in the Head Twitch Response Induced by 5-Hydroxytryptophan and Psilocybin: Translational Implications.International journal of molecular sciences · 2022Article
- Tolerance and Cross-Tolerance among Psychedelic and Nonpsychedelic 5-HTACS chemical neuroscience · 2022Article
- Analytical profile, in vitro metabolism and behavioral properties of the lysergamide 1P-AL-LAD.Drug testing and analysis · 2022Article
- Sex-specific effects of psychedelics on prepulse inhibition of startle in 129S6/SvEv mice.Psychopharmacology · 2022Article
- Automated Computer Software Assessment of 5-Hydroxytryptamine 2A Receptor-Mediated Head Twitch Responses from Video Recordings of Mice.ACS pharmacology & translational science · 2022Article
- Return of the lysergamides. Part VII: Analytical and behavioural characterization of 1-valeroyl-d-lysergic acid diethylamide (1V-LSD).Drug testing and analysis · 2022Article
- Representing the dynamics of high-dimensional data with non-redundant wavelets.Patterns (New York, N.Y.) · 2022Article
- Improving Wearable-Based Activity Recognition Using Image Representations.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Hallucinogens induce the head-twitch response (HTR), a rapid reciprocal head movement, in mice. Although head twitches are usually identified by direct observation, they can also be assessed using a head-mounted magnet and a magnetometer. Procedures have been developed to automate the analysis of magnetometer recordings by detecting events that match the frequency, duration, and amplitude of the HTR. However, there is considerable variability in the features of head twitches, and behaviors such as jumping have similar characteristics, reducing the reliability of these methods. We have developed an automated method that can detect head twitches unambiguously, without relying on features in the amplitude-time domain. To detect the behavior, events are transformed into a visual representation in the time-frequency domain (a scalogram), deep features are extracted using the pretrained convolutional neural network (CNN) ResNet-50, and then the images are classified using a Support Vector Machine (SVM) algorithm. These procedures were used to analyze recordings from 237 mice containing 11,312 HTR. After transformation to scalograms, the multistage CNN-SVM approach detected 11,244 (99.4%) of the HTR. The procedures were insensitive to other behaviors, including jumping and seizures. Deep learning based on scalograms can be used to automate HTR detection with robust sensitivity and reliability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.