Automated detection of the head-twitch response using wavelet scalograms and a deep convolutional neural network
Scientific Reports May 20, 2020 DOI: 10.1038/s41598-020-65264-x (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Methodological development and validation study Peer reviewed |
|---|---|
| Sample size | 237 |
| Population | Mice |
| Intervention | hallucinogens |
| Keywords | Artificial intelligence Pattern recognition psychology Convolutional neural network Support vector machine Wavelet Head geology Electroencephalography Speech recognition Computer vision |
| Citations | 45 |
| Key points | A deep learning method using scalograms and a CNN-SVM approach detects head-twitch responses in mice with 99.4% sensitivity and is insensitive to other behaviors. |
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.