New Horizons for Phenotyping Behavior in Rodents: The Example of Depressive-Like Behavior

The evolution of the field of behavioral neuroscience is significantly dependent on innovative disruption triggered by our ability to model and phenotype animal models of neuropsychiatric disorders. The ability to adequately elicit and measure behavioral parameters are the fundaments on which the behavioral neuroscience community establishes the pathophysiological mechanisms of neuropsychiatric disorders as well as contributes to the development of treatment strategies for those conditions. Herein, we review how mood disorders, in particular depression, are currently modeled in rodents, focusing on the limitations of these models and particularly on the analyses of the data obtained with different behavioral tests. Finally, we propose the use of new paradigms to study behavior using multidimensional strategies that better encompasses the complexity of psychiatric conditions, namely depression; these paradigms provide holistic phenotyping that is applicable to other conditions, thus promoting the emergence of novel findings that will leverage this field.

Social Housing Status Impacts Rhesus Monkeys' Affective Responding in Classic Threat Processing Tasks

The established literature clearly demonstrates that whether or not monkeys are socially reared has long term consequences for their affective behavior. Yet, in the context of behavioral neuroscience and pharmacological studies, social context of adult animals is often ignored. When social context has been studied in adult monkeys, such studies have typically focused on welfare-related issues, as social isolation often leads to the development of abnormal behavior, rather than the impact on outcomes in behavioral neuroscience studies. Variation in social housing conditions for adult animals could have an impact on affective responding and may have significant implications for the interpretation of data from biopsychiatry and behavioral neuroscience studies. We evaluated the affective reactivity of rhesus monkeys (Macaca mulatta) maintained in one of four housing conditions (individually-housed, grate-paired, intermittently-paired, and continuously-paired) using two classic threat processing tasks: a test of responsivity to objects and the Human Intruder Test. Individually-housed monkeys exhibited consistently blunted sensitivity to ostensibly threatening stimuli as compared to socially-housed monkeys. Within the three socially-housed conditions, intermittently- and continuously-paired monkeys behaved similarly to each other and grate-paired monkeys exhibited relatively enhanced sensitivity to threatening stimuli. These findings suggest that the adult housing conditions of monkeys can robustly modulate affective responding in a way that may be consistent with behavioral phenotypes observed in human psychiatric conditions. Results are considered in the context of the broad behavioral and psychiatric neuroscience literatures, which have historically used individually-housed animals, pointing to the potential need to reconsider inferences drawn from those studies.

Reproducibility and efficiency in handling complex neurophysiological data

Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.