The relevance of depression in the clinical realm is well known, as it is one of the most common mental disorders in the United States. Clinical depression is the leading cause of disease for women worldwide. The sex difference in depression and anxiety has guided the research of not just recent studies but older studies as well, supporting the theory that gonadal hormones are associated with the mechanisms of emotional cognition. The scientific literature points towards a clear correlative relationship between gonadal hormones, especially estrogens, and emotion regulation. This thesis investigates the neural pathways that have been indicated to regulate mood and anxiety. Currently, the research points to the hypothalamic-pituitary-adrenal axis, which regulates the stress response through its ultimate secretion of cortisol through the adrenal cortex, and its modulated response when exposed to higher levels of estrogen. Another mechanism that has been investigated is the interaction of estrogen and the serotonergic system, which is noteworthy because the serotonergic system is known for its importance in mood regulation. However, it is important to note that the research seeking to determine the neurobiological underpinnings of estrogen and the serotonergic system is not expansive. Future research should focus on determining the direct relationship between cortisol hypersecretion and estrogens, the specific neurobiological effects of serotonergic receptor subtypes on the antidepressant actions of estrogens, and the simultaneous effects of the stress and serotonergic systems on depressive symptoms.

Critical flicker fusion thresholds (CFFTs) describe when quick amplitude modulations of a light source become undetectable as the frequency of the modulation increases and are thought to underlie a number of visual processing skills, including reading. Here, we compare the impact of two vision-training approaches, one involving contrast sensitivity training and the other directional dot-motion training, compared to an active control group trained on Sudoku. The three training paradigms were compared on their effectiveness for altering CFFT. Directional dot-motion and contrast sensitivity training resulted in significant improvement in CFFT, while the Sudoku group did not yield significant improvement. This finding indicates that dot-motion and contrast sensitivity training similarly transfer to effect changes in CFFT. The results, combined with prior research linking CFFT to high-order cognitive processes such as reading ability, and studies showing positive impact of both dot-motion and contrast sensitivity training in reading, provide a possible mechanistic link of how these different training approaches impact reading abilities.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.