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Automated quantification of head-twitch response in mice via ear tag reporter coupled with biphasic detection.

ABSTRACT: BACKGROUND:Head-twitch response (HTR) is a manifestation of the serotonergic system behavioral pharmacology commonly used as a proxy of psychedelic drug action in rodents. NEW METHOD:We developed a minimally invasive magnetic ear tag reporter and designed a detection system that performs a comprehensive characterization of each potential HTR event on an electromagnetic readout. RESULTS:Magnetic ear tags were easy to install and generally well tolerated by the animals. On the low-threshold first phase of detection, the tags' signal recorded in a magnetometer was filtered and screened for potential HTR events. On the second phase, the detector performed a comprehensive spectral analysis evaluation of each event and identified the HTR characteristic distribution of power density. Our system delivered satisfactory performance in the identification of pharmacologically-induced HTR and discrimination power against common non-HTR behaviors. COMPARISON WITH EXISTING METHODS:Our system offers a high-throughput solution for studying HTR in mice employing minimally invasive procedures and superior standalone discriminative power compared to our previously reported fully-automated approach. CONCLUSIONS:High-throughput identification of HTR utilizing magnetic ear-tagging and biphasic detection delivers satisfactory detection and discrimination power employing less invasive procedures.

SUBMITTER: de la Fuente Revenga M

PROVIDER: S-EPMC7363508 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVE:Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind-the-ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind-the-ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels. METHODS:Fifty-four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video-EEG at University Hospital Leuven. In addition, extra behind-the-ear EEG channels were recorded. First, a neurologist was asked to annotate behind-the-ear EEG segments containing selected seizure and nonseizure fragments. Second, a data-driven algorithm was developed using only behind-the-ear EEG. This algorithm was trained using data from other patients (patient-independent model) or from the same patient (patient-specific model). RESULTS:The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false-positive detections (FPs)/24 hours with the patient-independent model. The patient-specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. SIGNIFICANCE:Visual recognition of ictal EEG patterns using only behind-the-ear EEG is possible in a significant number of patients with TLE. A patient-specific seizure detection algorithm using only behind-the-ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.

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Automated quantification of head-twitch response in mice via ear tag reporter coupled with biphasic detection.

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