Date of Award

7-28-2009

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biology

First Advisor

H. Elliott Albers - Chair

Second Advisor

Anne Murphy

Third Advisor

Timothy Bartness

Fourth Advisor

Larry Young

Abstract

Aggression is a feature of many clinical disorders including autism, Alzheimer’s disease, bipolar disorder, and schizophrenia. The available treatment options act to prevent impulsive aggression through modulation of GABAergic and dopaminergic pathways which come with metabolic and dyskinetic side effects. The mechanism underlying aggressive motivation, however, has not been elucidated. In addition, previous studies have been heavily biased towards males of various species. Mimicking changes in day length, or photoperiod, in the laboratory is a natural manipulation used to examine seasonal changes in aggressive behavior in many species. In response to the reduction in the duration of light exposure, animals undergo gonadal regression and become reproductively quiescent. During this non-breeding season in male photoperiod-responsive animals, aggressive behavior increases significantly. Although studies have shown offensive aggression remains elevated in female rodents, seasonal regulation of this behavior in females has not been thoroughly studied. The neuropeptide arginine-vasopressin (AVP) has been implicated in the facilitation of aggressive behavior in male rodents and fishes; therefore, it is useful to examine AVP as a modulator of seasonal aggression in females. Because the actions of AVP in female social behavior may be hormonally-dependent, we investigated the hormonal mechanisms that regulate the expression of AVP receptors and the behavioral actions of AVP on aggression. In addition to changes in gonadal steroid hormones during the non-breeding season, we identified photoperiod-dependent alterations in adrenal hormone secretion as AVP plays a role in regulation of hypothalamic-pituitary-adrenal axis (HPA) activity and anxiety-like behaviors in animal models.

DOI

https://doi.org/10.57709/1063898

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