Profiling Proteins in the Hypothalamus and Hippocampus of a Rat Model of Premenstrual Syndrome Irritability

DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning

Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of laboratory animals, allowing animal behavior to be analyzed digitally. In vivo optical imaging and electrophysiological recording in freely behaving animals are now widely used to understand neural functions in circuits. However, it is always a challenge to accurately track the movement of an animal under certain complex conditions due to uneven environment illumination, variations in animal models, and interference from recording devices and experimenters. To overcome these challenges, we have developed a strategy to track the movement of an animal by combining a deep learning technique, the You Only Look Once (YOLO) algorithm, with a background subtraction algorithm, a method we label DeepBhvTracking. In our method, we first train the detector using manually labeled images and a pretrained deep-learning neural network combined with YOLO, then generate bounding boxes of the targets using the trained detector, and finally track the center of the targets by calculating their centroid in the bounding box using background subtraction. Using DeepBhvTracking, the movement of animals can be tracked accurately in complex environments and can be used in different behavior paradigms and for different animal models. Therefore, DeepBhvTracking can be broadly used in studies of neuroscience, medicine, and machine learning algorithms.

Gene Expression in the Hippocampus in a Rat Model of Premenstrual Dysphoric Disorder After Treatment With Baixiangdan Capsules

Objective: To explore the targets, signal regulatory networks and mechanisms involved in Baixiangdan (BXD) capsule regulation of premenstrual dysphoric disorder (PMDD) at the gene transcription level, since the etiology and pathogenesis of PMDD are not well understood.

Methods: The PMDD rat model was prepared using the resident-intruder paradigm. The rats were tested for aggressive behavior, and those with scores in the lowest 30% were used as controls, while rats with scores in the highest 30% were divided into a PMDD model group, BXD administration group and fluoxetine administration group, which were evaluated with open-field tests and aggressive behavior tests. We also analyzed gene expression profiles in the hippocampus for each group, and verified differential expression of genes by real-time PCR.

Results: Before and after BXD or fluoxetine administration, scores in the open-field test exhibited no significant differences. The aggressive behavior of the PMDD model rats was improved to a degree after administration of both substances. Gene chip data indicated that 715 genes were differentially expressed in the control and BXD groups. Other group-to-group comparisons exhibited smaller numbers of differentially expressed genes. The effective targets of both drugs included the Htr2c, Cdh3, Serpinb1a, Ace, Trpv4, Cacna1a, Mapk13, Mapk8, Cyp2c13, and Htr1a genes. The results of real-time PCR tests were in accordance with the gene chip data. Based on the target genes and signaling pathway network analysis, we have elaborated the impact and likely mechanism of BXD in treating PMDD and premenstrual irritability.

Conclusion: Our work contributes to the understanding of PMDD pathogenesis and the mechanisms of BXD treatment. We speculate that the differentially expressed genes could participate in neuroactive ligand-receptor interactions, mitogen-activated protein kinase, calcium, and gamma-aminobutyric acid signal transduction.