Automated detection of the head-twitch response using wavelet scalograms and a deep convolutional neural network.
Animals
Behavior Observation Techniques
/ instrumentation
Behavior, Animal
/ drug effects
Drug Evaluation, Preclinical
/ instrumentation
Hallucinogens
/ pharmacology
Head Movements
/ drug effects
Magnetometry
/ instrumentation
Magnets
Male
Mice
Models, Animal
Reproducibility of Results
Sensitivity and Specificity
Support Vector Machine
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 05 2020
20 05 2020
Historique:
received:
19
11
2019
accepted:
30
04
2020
entrez:
21
5
2020
pubmed:
21
5
2020
medline:
15
12
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32433580
doi: 10.1038/s41598-020-65264-x
pii: 10.1038/s41598-020-65264-x
pmc: PMC7239849
doi:
Substances chimiques
Hallucinogens
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
8344Subventions
Organisme : NIDA NIH HHS
ID : R01 DA041336
Pays : United States
Références
Kometer, M., Schmidt, A., Jancke, L. & Vollenweider, F. X. Activation of serotonin 2A receptors underlies the psilocybin-induced effects on alpha oscillations, N170 visual-evoked potentials, and visual hallucinations. J. Neurosci. 33, 10544–10551 (2013).
pubmed: 23785166
pmcid: 6618596
doi: 10.1523/JNEUROSCI.3007-12.2013
Preller, K. H. et al. The Fabric of Meaning and Subjective Effects in LSD-Induced States Depend on Serotonin 2A Receptor Activation. Curr. Biol. 27, 451–457 (2017).
pubmed: 28132813
doi: 10.1016/j.cub.2016.12.030
Vollenweider, F. X., Vollenweider-Scherpenhuyzen, M. F., Babler, A., Vogel, H. & Hell, D. Psilocybin induces schizophrenia-like psychosis in humans via a serotonin-2 agonist action. Neuroreport 9, 3897–3902 (1998).
pubmed: 9875725
doi: 10.1097/00001756-199812010-00024
Madsen, M. K. et al. Psychedelic effects of psilocybin correlate with serotonin 2A receptor occupancy and plasma psilocin levels. Neuropsychopharmacology 44, 1328–1334 (2019).
doi: 10.1038/s41386-019-0324-9
Grob, C. S. et al. Pilot study of psilocybin treatment for anxiety in patients with advanced-stage cancer. Arch. Gen. Psychiatry 68, 71–78 (2011).
pubmed: 20819978
doi: 10.1001/archgenpsychiatry.2010.116
Gasser, P. et al. Safety and efficacy of lysergic acid diethylamide-assisted psychotherapy for anxiety associated with life-threatening diseases. J. Nerv. Ment. Dis. 202, 513–520 (2014).
pubmed: 24594678
pmcid: 4086777
doi: 10.1097/NMD.0000000000000113
Griffiths, R. R. et al. Psilocybin produces substantial and sustained decreases in depression and anxiety in patients with life-threatening cancer: A randomized double-blind trial. J. Psychopharmacol. 30, 1181–1197 (2016).
pubmed: 27909165
pmcid: 5367557
doi: 10.1177/0269881116675513
Ross, S. et al. Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: a randomized controlled trial. J. Psychopharmacol. 30, 1165–1180 (2016).
pubmed: 27909164
pmcid: 5367551
doi: 10.1177/0269881116675512
Halberstadt, A. L., Koedood, L., Powell, S. B. & Geyer, M. A. Differential contributions of serotonin receptors to the behavioral effects of indoleamine hallucinogens in mice. J. Psychopharmacol. 25, 1548–1561 (2011).
pubmed: 21148021
doi: 10.1177/0269881110388326
Canal, C. E. & Morgan, D. Head-twitch response in rodents induced by the hallucinogen 2,5-dimethoxy-4-iodoamphetamine: a comprehensive history, a re-evaluation of mechanisms, and its utility as a model. Drug. Test. Anal. 4, 556–576 (2012).
pubmed: 22517680
pmcid: 3722587
doi: 10.1002/dta.1333
Fantegrossi, W. E. et al. Hallucinogen-like effects of N,N-dipropyltryptamine (DPT): possible mediation by serotonin 5-HT1A and 5-HT2A receptors in rodents. Pharmacol. Biochem. Behav. 88, 358–365 (2008).
pubmed: 17905422
doi: 10.1016/j.pbb.2007.09.007
Fantegrossi, W. E. et al. Hallucinogen-like actions of 2,5-dimethoxy-4-(n)-propylthiophenethylamine (2C-T-7) in mice and rats. Psychopharmacology 181, 496–503 (2005).
doi: 10.1007/s00213-005-0009-4
Carbonaro, T. M. et al. The role of 5-HT2A, 5-HT 2C and mGlu2 receptors in the behavioral effects of tryptamine hallucinogens N,N-dimethyltryptamine and N,N-diisopropyltryptamine in rats and mice. Psychopharmacology 232, 275–284 (2015).
pubmed: 24985890
doi: 10.1007/s00213-014-3658-3
Schreiber, R. et al. 1-(2,5-dimethoxy-4 iodophenyl)−2-aminopropane)-induced head-twitches in the rat are mediated by 5-hydroxytryptamine (5-HT) 2A receptors: modulation by novel 5-HT2A/2C antagonists, D1 antagonists and 5-HT1A agonists. J. Pharmacol. Exp. Ther. 273, 101–112 (1995).
pubmed: 7714755
Darmani, N. A., Martin, B. R., Pandey, U. & Glennon, R. A. Do functional relationships exist between 5-HT1A and 5-HT2 receptors? Pharmacol. Biochem. Behav. 36, 901–906 (1990).
pubmed: 2145593
doi: 10.1016/0091-3057(90)90098-3
Gonzalez-Maeso, J. et al. Hallucinogens recruit specific cortical 5-HT(2A) receptor-mediated signaling pathways to affect behavior. Neuron 53, 439–452 (2007).
pubmed: 17270739
doi: 10.1016/j.neuron.2007.01.008
Halberstadt, A. L. & Geyer, M. A. Characterization of the head-twitch response induced by hallucinogens in mice: detection of the behavior based on the dynamics of head movement. Psychopharmacology 227, 727–739 (2013).
pubmed: 23407781
doi: 10.1007/s00213-013-3006-z
Halberstadt, A. L., Chatha, M., Klein, A. K., Wallach, J. & Brandt, S. D. Correlation between the potency of hallucinogens in the mouse head-twitch response assay and their behavioral and subjective effects in other species. Neuropharmacology 167, 107933 (2020).
Halberstadt, A. L. & Geyer, M. A. Effects of the hallucinogen 2,5-dimethoxy-4-iodophenethylamine (2C-I) and superpotent N-benzyl derivatives on the head twitch response. Neuropharmacology 77, 200–207 (2014).
doi: 10.1016/j.neuropharm.2013.08.025
Nichols, D. E. et al. N-Benzyl-5-methoxytryptamines as Potent Serotonin 5-HT2 Receptor Family Agonists and Comparison with a Series of Phenethylamine Analogues. ACS Chem. Neurosci. 6, 1165–1175 (2015).
pubmed: 25547199
doi: 10.1021/cn500292d
Halberstadt, A. L., Chatha, M., Stratford, A., Grill, M. & Brandt, S. D. Comparison of the behavioral responses induced by phenylalkylamine hallucinogens and their tetrahydrobenzodifuran (“FLY”) and benzodifuran (“DragonFLY”) analogs. Neuropharmacology 144, 368–376 (2019).
doi: 10.1016/j.neuropharm.2018.10.037
Halberstadt, A. L., Chatha, M., Chapman, S. J. & Brandt, S. D. Comparison of the behavioral effects of mescaline analogs using the head twitch response in mice. J. Psychopharmacol. 33, 406–414 (2019).
pubmed: 30789291
pmcid: 6848748
doi: 10.1177/0269881119826610
Brandt, S. D. et al. Return of the lysergamides. Part I: Analytical and behavioural characterization of 1-propionyl-d-lysergic acid diethylamide (1P-LSD). Drug. Test. Anal. 8, 891–902 (2016).
pubmed: 26456305
doi: 10.1002/dta.1884
Brandt, S. D. et al. Return of the lysergamides. Part V: Analytical and behavioural characterization of 1-butanoyl-d-lysergic acid diethylamide (1B-LSD). Drug. Test. Anal. 11, 1122–1133 (2019).
pubmed: 31083768
doi: 10.1002/dta.2613
Klein, L. M., Cozzi, N. V., Daley, P. F., Brandt, S. D. & Halberstadt, A. L. Receptor binding profiles and behavioral pharmacology of ring-substituted N,N-diallyltryptamine analogs. Neuropharmacology 142, 231–239 (2018).
pubmed: 6230509
pmcid: 6230509
doi: 10.1016/j.neuropharm.2018.02.028
Halberstadt, A. L. et al. Pharmacological characterization of the LSD analog N-ethyl-N-cyclopropyl lysergamide (ECPLA). Psychopharmacology 236, 799–808 (2019).
pubmed: 30298278
doi: 10.1007/s00213-018-5055-9
Siegel, R. K., Lee, M. A. & Jarvik, M. E. A device for analyzing drug-induced responses in freely moving mice. J. Exp. Anal. Behav. 18, 415–418 (1972).
pubmed: 4661252
pmcid: 1334028
doi: 10.1901/jeab.1972.18-415
de la Fuente Revenga, M. et al. Fully automated head-twitch detection system for the study of 5-HT2A receptor pharmacology in vivo. Sci. Rep. 9, 14247 (2019).
pubmed: 31582824
pmcid: 6776537
doi: 10.1038/s41598-019-49913-4
Byeon, Y. H., Pan, S. B. & Kwak, K. C. Intelligent deep models based on scalograms of electrocardiogram signals for biometrics. Sensors 19, 935 (2019).
doi: 10.3390/s19040935
Turk, O. & Ozerdem, M. S. Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sci 9 (2019).
Smith, A. A. & Kristensen, D. Deep learning to extract laboratory mouse ultrasonic vocalizations from scalograms. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, November 13–16, 2017, pp. 1972–1979 (2017).
Alaskar, H. Deep learning of EMG time-frequency representations for identifying normal and agressive actions. Int. J. Computer Sci. Netw. Security 18, 16–25 (2018).
Amiriparian, S. et al. Snore sound classification using image-based deep spectrum features. Interspeech 2017. Stockholm, Sweden (2017).
Copiaco, A., Ritz, C., Fasciani, S. & Abdulaziz, N. Scalogram neural network activations with machine learning for domestic multi-channel audio classification. 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). Ajman, United Arab Emirates. pp. 1–6 (2019).
Kaya, D. The mRMR-CNN based influential support decision system approach to classify EEG signals. Measurement 156, 107602 (2020).
Er, M. B. & Aydilek, I. B. Music emotion recognition by using chroma spectrogram and deep visual features. Int. J. Computational Intell. Syst. 12, 1622–1624 (2019).
Bajaj, V., Taran, S., Tanyildizi, E. & Sengur, A. Robust approach based on convolutional neural networks for identification of focal EEG signals. IEEE Sens. Lett. 3, 7000604 (2019).
doi: 10.1109/LSENS.2019.2909119
Razavian, A. S., Azizpour, H., Sullivan, J. & Carlsson, S. CNN features off-the-shelf: an astounding baseline for recognition. 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus, OH. pp. 512–519 (2014).
Almabdy, S. & Elrefaei, L. Deep convolutional neural network-based approaches for face recognition. Appl. Sci. 9, 4397 (2019).
doi: 10.3390/app9204397
Bousetouane, F. & Morris, B. Off-the-shelf CNN features for fine-grained classification of vessels in a maritime environment. 11th International Symposium, ISVC 2015. Las Vegas, NV, December 14–16, 2015. pp. 379–388 (2015).
He, K., Zhang, X., Ren, S. & Sun, J. C. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, June 27–30, 2016, pp. 770–778 (2016).
Donahue, J. et al. DeCAF: A deep convolutional activation feature for generic visual recognition. Proceedings of the 31st International Conference on Machine Learning. Beijing, China (2014).
Rifkin, R. & Klautau, A. In defense of one-vs-all classification. J. Mach. Learn. Res. 5, 101–141 (2004).
Dietterich, T. & Bakiri, G. Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–282 (1995).
doi: 10.1613/jair.105
Raehal, K. M. et al. In vivo characterization of 6beta-naltrexol, an opioid ligand with less inverse agonist activity compared with naltrexone and naloxone in opioid-dependent mice. J. Pharmacol. Exp. Ther. 313, 1150–1162 (2005).
pubmed: 15716384
doi: 10.1124/jpet.104.082966
Shiosaki, K. et al. Hyperactivity and behavioral seizures in rodents following treatment with the dopamine D1 receptor agonists A-86929 and ABT-431. Eur. J. Pharmacol. 317, 183–190 (1996).
pubmed: 8997599
doi: 10.1016/S0014-2999(96)00718-2
Behrendt, H. J., Germann, T., Gillen, C., Hatt, H. & Jostock, R. Characterization of the mouse cold-menthol receptor TRPM8 and vanilloid receptor type-1 VR1 using a fluorometric imaging plate reader (FLIPR) assay. Br. J. Pharmacol. 141, 737–745 (2004).
pubmed: 14757700
pmcid: 1574235
doi: 10.1038/sj.bjp.0705652
Wei, E. T. Chemical stimulants of shaking behaviour. J. Pharm. Pharmacol. 28, 722–723 (1976).
pubmed: 10386
doi: 10.1111/j.2042-7158.1976.tb02849.x
de la Fuente Revenga, M., Vohra, H. Z. & Gonzalez-Maeso, J. Automated quantification of head-twitch response in mice via ear tag reporter coupled with biphasic detection. J. Neurosci. Methods 334, 108595 (2020).
pubmed: 31954738
doi: 10.1016/j.jneumeth.2020.108595
Preece, M. A., Dalley, J. W., Theobald, D. E., Robbins, T. W. & Reynolds, G. P. Region specific changes in forebrain 5-hydroxytryptamine1A and 5-hydroxytryptamine2A receptors in isolation-reared rats: an in vitro autoradiography study. Neuroscience 123, 725–732 (2004).
pubmed: 14706784
doi: 10.1016/j.neuroscience.2003.10.008
Schiller, L., Jahkel, M., Kretzschmar, M., Brust, P. & Oehler, J. Autoradiographic analyses of 5-HT1A and 5-HT2A receptors after social isolation in mice. Brain Res. 980, 169–178 (2003).
pubmed: 12867255
doi: 10.1016/S0006-8993(03)02832-4
Gunther, L., Liebscher, S., Jahkel, M. & Oehler, J. Effects of chronic citalopram treatment on 5-HT1A and 5-HT2A receptors in group- and isolation-housed mice. Eur. J. Pharmacol. 593, 49–61 (2008).
pubmed: 18657534
doi: 10.1016/j.ejphar.2008.07.011
Sakaue, M. et al. Modulation by 5-HT2A receptors of aggressive behavior in isolated mice. Jpn. J. Pharmacol. 89, 89–92 (2002).
pubmed: 12083749
doi: 10.1254/jjp.89.89
Boulton, C. S. & Handley, S. L. Factors modifying the head-twitch response to 5-hydroxytryptophan. Psychopharmacologia 31, 205–214 (1973).
pubmed: 4542469
doi: 10.1007/BF00422511
Brotto, L. A., Gorzalka, B. B. & Hanson, L. A. Effects of housing conditions and 5-HT2A activation on male rat sexual behavior. Physiol. Behav. 63, 475–479 (1998).
pubmed: 9523886
doi: 10.1016/S0031-9384(97)00482-4
Sherwood, A. M. et al. Synthesis and Biological Evaluation of Tryptamines Found in Hallucinogenic Mushrooms: Norbaeocystin, Baeocystin, Norpsilocin, and Aeruginascin. J. Nat. Prod. 83, 461–467 (2020).
pubmed: 32077284
doi: 10.1021/acs.jnatprod.9b01061