Many clinical and research efforts aim to develop antidepressant drugs for those suffering from major depressive disorder (MDD). Yet even today, the available treatments are suboptimal and unpredictable, with a significant proportion of patients enduring multiple drug attempts and adverse side effects before a successful response; and for many patients, no response at all. Thus, a clearer understanding of the mechanisms underlying MDD is necessary. In the “Brain development and disease” class of our Master’s program in Cognitive Sciences, we ask students to collect data about the expression of a gene whose altered expression and/or function is related to a brain disorder. The students’ final exam assignment consists of writing a research article in which the collected data are discussed in relation to the relevant disorder. In the course of one of these students’ assignments, we identified the FKBP5 gene as a key player uniting two major hypotheses of MDD pathogenesis and treatment response. FKBP5 is involved in biological processes including immunoregulation and glucocorticoid function, both of which are separately implicated in the development and prognosis of MDD. Gene expression analysis from the human and non-human primate Allen Brain Atlases revealed that FKBP5 is expressed in brain regions involved in MDD, particularly at periods of development when early-life stressors would have a great effect. Future works should focus on causal mechanisms of FKBP5 for antidepressant response. Our experience shows that classes engaging students in data collection and analysis projects may effectively result in novel data-driven hypotheses.

The proboscis extension response (PER) has been widely used for decades to evaluate honeybees' (Apis mellifera) learning and memory abilities. This classical conditioning paradigm is traditionally administered manually, and produces a binary score for each subject depending on the presence or absence of the proboscis extension in response to a stimulus - typically an odor which has been associated with a sucrose reward - to classify whether or not the bee has learned the association. Here we present a fully automated PER system which delivers stimuli in a more controlled manner, and thus standardizes the protocol within and between labs; further, the AI-facilitated behavioral scoring reduces human error and allows us to extract a richer meaning from the outcome. The automated frame-by-frame assessment goes beyond the binary classification of "learned" or "not learned", expanding the possibilities for many other measures. Using this method, we investigate the real-time decision-making processes of honeybees faced with difficult learning tasks. When posed with a quantitative (rather than qualitative, as in the case of different odors) PER association, honeybees show a pattern of rapid generalization to both the rewarded and non-rewarded stimuli, followed by a slowly acquired discrimination between the two. Our work lays the foundation for deeper exploration of the honeybee cognitive processes when posed with complex learning challenges.