Translational Shifts in Preclinical Models of Depression: Implications for Biomarkers for Improved Treatments

Abstract

Understanding the neurobiology of major depressive disorder (MDD) remains one of the major challenges in neuroscience. The disease is heterogeneous in nature, and patients present with a varied symptom profile. Studies seeking to identify biomarkers for MDD diagnosis and treatment have not yet found any one candidate which achieves sufficient sensitivity and specificity. In this article, we consider whether neuropsychological impairments, specifically affective biases, could provide a behavioural biomarker. Affective biases are observed when emotional states influence cognitive function. These biases have been shown to influence a number of different cognitive domains with some specific deficits observed in MDD. It has also been possible to use these neuropsychological tests to inform the development of translational tasks for non-human species. The results from studies in rodents suggest that quantification of affective biases is feasible and may provide a reliable method to predict antidepressant efficacy as well as pro-depressant risk. Animal studies suggest that affective state-induced biases in learning and memory operate over a different time course to biases influencing decision-making. The implications for these differences in terms of task validity and future ideas relating to affective biases and MDD are discussed. We also describe our most recent studies which have shown that depression-like phenotypes share a common deficit in reward-related learning and memory which we refer to as a reward-induced positive bias. This deficit is dissociable from more typical measures of hedonic behaviour and motivation for reward and may represent an important and distinct form of reward deficit linked to MDD.

Trait sensitivity to negative feedback determines the intensity of compulsive alcohol seeking and taking in male rats

“…In humans, sensitivity to feedback can be investigated using a framework of neurophysiological [8][9][10] and neuropsy chological measures. [11][12][13][14][15] One measure that offers an effective and fully translational way of assessing an individual's sensi tivity to feedback is the probabilistic reversal learning test. 16 In this behavioural paradigm, participants are presented with 2 (and sometimes more) stimuli in each trial; using trialand error feedback after each response, they learn to select the stimulus that is usually correct (i.e., rewarded in more trials or unrewarded in fewer trials) and to avoid the stimulus that is usually incorrect (i.e., unrewarded in more trials or re warded in fewer trials).…”

A systematic review of studies investigating the acute effects of N-methyl-D-aspartate receptor antagonists on behavioural despair in normal animals suggests poor predictive validity

“…Concerns relating to the poor predictive validity, weak translational relevance alongside ethical concerns associated with use of the FST and TST have long been recognised within the field of depression research ( Molendijk and de Kloet, 2015 ; Reardon, 2019 ; Sewel et al, 2021 ; Slaney et al, 2018 ; Slattery and Cryan, 2012 ). Efforts have been made to translate the tasks used to assess depression symptomology in humans into rodents to provide tasks for use in animal research that show greater face and construct validity than the FST/TST.…”

Psychopharmacology: From serendipitous discoveries to rationale design, but what next?

“…Perhaps one of the most important areas for development is the need for robust translational biomarkers which can provide objective methods for diagnosis and to assess response to treatment (Jentsch et al, 2015; Slaney et al, 2018; Strawbridge et al, 2017). Biomarkers could also help with the current challenge of diagnosis and patient stratification.…”

“…However, if such developments could be made, then future clinical trials could benefit from a more symptom-based objective assessment. Our own research has attempted to develop a translational and objective approach to studying MDD based on these ideas (Robinson, 2018; Slaney et al, 2018).…”