Finding new antidepressant agents is of high clinical priority given that many cases of major depressive disorder (MDD) do not respond to conventional monoaminergic antidepressants such as the selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants, and monoamine oxidase inhibitors. Recent findings of effective fast-acting antidepressants indicate that there are biological substrates to be taken advantage of for fast relief of depression and that we may find further treatments in this category. In this vein, the cholinergic system may be a relatively overlooked target for antidepressant medications, given its major role in motivation and attention. Furthermore, the classically engaged monoaminergic neurotransmitter systems in depression treatment—serotonin, norepinephrine, and dopamine—interact directly at times with cholinergic signaling. Here we investigate in greater detail how the cholinergic system may impact depression-related behavior, by administering widely ranging doses of the cholinesterase inhibitor drug, donepezil, to C57BL/6J mice in the forced swim test. First, we confirm prior findings that this drug, which is thought to boost synaptic acetylcholine, promotes depression-like behavior at a high dose (2.0 mg/kg, i.p.). But we also find paradoxically that it has an antidepressant-like effect at lower doses (0.02 and 0.2 mg/kg). Further this antidepressant-like effect is not due to generalized hyperactivity, since we did not observe increased locomotor activity in the open field test. These data support a novel antidepressant-like role for donepezil at lower doses as part of an overall u-shaped dose-response curve. This raises the possibility that donepezil could have antidepressant properties in humans suffering from MDD.

When stress becomes chronic it can trigger lasting brain and behavioral changes including Major Depressive Disorder (MDD). There is conflicting evidence regarding whether acetylcholinesterase inhibitors (AChEIs) may have antidepressant properties. In a recent publication, we demonstrated a strong dose-dependency of the effect of AChEIs on antidepressant-related behavior in the mouse forced swim test: whereas the AChEI donepezil indeed promotes depression-like behavior at a high dose, it has antidepressant-like properties at lower doses in the same experiment. Our data therefore suggest a Janus-faced dose-response curve for donepezil in depression-related behavior. In this review, we investigate the mood-related properties of AChEIs in greater detail, focusing on both human and rodent studies. In fact, while there have been many studies showing pro-depressant activity by AChEIs and this is a major concept in the field, a variety of other studies in both humans and rodents show antidepressant effects. Our study was one of the first to systematically vary dose to include very low concentrations while measuring behavioral effects, potentially explaining the apparent disparate findings in the field. The possibility of antidepressant roles for AChEIs in rodents may provide hope for new depression treatments. Importantly, MDD is a psychosocial stress-linked disorder, and in rodents, stress is a major experimental manipulation for studying depression mechanisms, so an important future direction will be to determine the extent to which these depression-related effects are stress-sensitive. In sum, gaining a greater understanding of the potentially therapeutic mood-related effects of low dose AChEIs, both in rodent models and in human subjects, should be a prioritized topic in ongoing translational research.

Quantifying animal behavior is important for many branches of biology and biomedical research. Current computational tools for this purpose typically rely on a limited number of pre-defined metrics to identify a behavior and then use these metrics as measurements of the behavior. However, categorizing behaviors by pre-defined metrics has limitations in its applicability (e.g., behavior types or animal species) and accuracy. Here we report a new tool, LabGym, for quantifying animal behaviors without such limitations. LabGym combines multi-animal tracking and deep learning algorithms with new designs to achieve high-throughput and accurate quantifications of each user-defined animal behavior. It also provides users a way to generate visualizable benchmark datasets for these behaviors, which are valuable resources for research in neuroscience, behavioral science, and computer science. We demonstrate the utility of LabGym in various user-defined behaviors in animal species ranging from soft-bodied invertebrates to mammals.

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