Contemporary Pharmacotherapeutics and the Management of Aggressive Behavior in an Adolescent Animal Model of Maladaptive Aggression

Valproate selectively suppresses adolescent anabolic/androgenic steroid-induced aggressive behavior: implications for a role of hypothalamic γ-aminobutyric acid neural signaling

Pubertal male Syrian hamsters (Mesocricetus auratus) treated with anabolic/androgenic steroids (AASs) during adolescence (P27-P56) display a highly intense aggressive phenotype that shares many behavioral similarities with pathological aggression in youth. Anticonvulsant drugs like valproate that enhance the activity of the γ-aminobutyric acid (GABA) neural system in the brain have recently gained acceptance as a primary treatment for pathological aggression. This study examined whether valproate would selectively suppress adolescent AAS-induced aggressive behavior and whether GABA neural signaling through GABAA subtype receptors in the latero-anterior hypothalamus (LAH; an area of convergence for developmental and neuroplastic changes that underlie aggression in hamsters) modulate the aggression-suppressing effect of this anticonvulsant medication. Valproate (1.0-10.0 mg/kg, intraperitoneal) selectively suppressed the aggressive phenotype in a dose-dependent fashion, with the effective anti-aggressive effects beginning at 5 mg/kg, intraperitoneally. Microinfusion of the GABAA receptor antagonist bicuculline (7.0-700 ng) into the LAH reversed valproate's suppression of AAS-induced aggression in a dose-dependent fashion. At the 70 ng dose of bicuculline, animals expressed the highly aggressive baseline phenotype normally observed in AAS-treated animals. These studies provide preclinical evidence that the anticonvulsant valproate selectively suppresses adolescent, AAS-induced aggression and that this suppression is modulated, in part, by GABA neural signaling within the LAH.

Prediction of Locomotor Activity by Infrared Motion Detector on Sleep-wake State in Mice

Objective

Behavioral assessments that effectively predict sleep-wake states were tried in animal research. This study aimed to examine the prediction power of an infrared locomotion detector on the sleep-wake states in ICR (Institute Cancer Research) mice. We also explored the influence of the durations and ways of data processing on the prediction power.

Methods

The locomotor activities of seven male mice in home cages were recorded by infrared detectors. Their sleep-wake states were assessed by video analysis. Using the receiver operating characteristic curve analysis, the cut-off score was determined, then the area under the curve (AUC) values of the infrared motion detector that predicted sleep-wake states were calculated. In order to improve the prediction power, the four ways of data processing on the prediction power were performed by Matlab 2013b.

Results

In the initial analysis of raw data, the AUC value was 0.785, but it gradually reached to 0.942 after data summation. The simple data averaging and summation among four different methods showed the maximal AUC value. The 10-minute data summation improved sensitivity (0.889) and specificity (0.901) significantly from the baseline value (sensitivity 0.615; specificity 0.936) (p < 0.001).

Conclusion

This study suggests that the locomotor activity measured by an infrared motion detector might be useful to predict the sleep-wake states in ICR mice. It also revealed that only simple data summation may improve the predictive power. Using daily locomotor activities measured by an infrared motion detector is expected to facilitate animal research related to sleep-wake states.