TrackUSF, a novel tool for automated ultrasonic vocalization analysis, reveals modified calls in a rat model of autism

Extended performance analysis of deep-learning algorithms for mice vocalization segmentation

Ultrasonic vocalizations (USVs) analysis represents a fundamental tool to study animal communication. It can be used to perform a behavioral investigation of mice for ethological studies and in the field of neuroscience and neuropharmacology. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and then processed by specific software, which help the operator to identify and characterize different families of calls. Recently, many automated systems have been proposed for automatically performing both the detection and the classification of the USVs. Of course, the USV segmentation represents the crucial step for the general framework, since the quality of the call processing strictly depends on how accurately the call itself has been previously detected. In this paper, we investigate the performance of three supervised deep learning methods for automated USV segmentation: an Auto-Encoder Neural Network (AE), a U-NET Neural Network (UNET) and a Recurrent Neural Network (RNN). The proposed models receive as input the spectrogram associated with the recorded audio track and return as output the regions in which the USV calls have been detected. To evaluate the performance of the models, we have built a dataset by recording several audio tracks and manually segmenting the corresponding USV spectrograms generated with the Avisoft software, producing in this way the ground-truth (GT) used for training. All three proposed architectures demonstrated precision and recall scores exceeding [Formula: see text], with UNET and AE achieving values above [Formula: see text], surpassing other state-of-the-art methods that were considered for comparison in this study. Additionally, the evaluation was extended to an external dataset, where UNET once again exhibited the highest performance. We suggest that our experimental results may represent a valuable benchmark for future works.

Daily Brief Heat Therapy Reduces Seizures in A350V IQSEC2 Mice and Is Associated with Correction of AMPA Receptor-Mediated Synaptic Dysfunction

Purposeful induction of fever for healing, including the treatment of epilepsy, was used over 2000 years ago by Hippocrates. More recently, fever has been demonstrated to rescue behavioral abnormalities in children with autism. However, the mechanism of fever benefit has remained elusive due in large part to the lack of appropriate human disease models recapitulating the fever effect. Pathological mutations in the IQSEC2 gene are frequently seen in children presenting with intellectual disability, autism and epilepsy. We recently described a murine A350V IQSEC2 disease model, which recapitulates important aspects of the human A350V IQSEC2 disease phenotype and the favorable response to a prolonged and sustained rise in body core temperature in a child with the mutation. Our goal has been to use this system to understand the mechanism of fever benefit and then develop drugs that can mimic this effect and reduce IQSEC2-associated morbidity. In this study, we first demonstrate a reduction in seizures in the mouse model following brief periods of heat therapy, similar to what was observed in a child with the mutation. We then show that brief heat therapy is associated with the correction of synaptic dysfunction in neuronal cultures of A350V mice, likely mediated by Arf6-GTP.