Back-translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders

Aylward, Jessica; Hales, Claire A.; Robinson, Emma; Robinson, Oliver J.

doi:10.1101/143453

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Another study looking at models of economic decision making under uncertainty (Figure 1C) suggested that anxiety was associated with risk but not with loss aversion (Charpentier et al, 2016). Finally, there is evidence that high trait anxiety is associated with increased boundary separation and nondecision time on the drift diffusion model (White et al, 2010) rather than the reduced drift rate seen in anhedonia (although see Aylward, Hales, Robinson, & Robinson, 2017, for potential evidence of drift rate changes in anxiety disorders).…”

Section: Five Questions For Future Researchmentioning

confidence: 99%

Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia

Robinson

Chase

2017

Computational Psychiatry

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Computational approaches are increasingly being used to model behavioral and neural processes in mood and anxiety disorders. Here we explore the extent to which the parameters of popular learning and decision-making models are implicated in anhedonic symptoms of major depression. We first highlight the parameters of reinforcement learning that have been implicated in anhedonia, focusing, in particular, on the role that choice variability (i.e., “temperature”) may play in explaining heterogeneity across previous findings. We then turn to neuroimaging findings implicating attenuated ventral striatum response in anhedonic responses and discuss possible causes of the heterogeneity in the literature. Taken together, the reviewed findings highlight the potential of the computational approach in teasing apart the observed heterogeneity in both behavioral and functional imaging results. Nevertheless, considerable challenges remain, and we conclude with five unresolved questions that seek to address issues highlighted by the reviewed data.

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Section: Five Questions For Future Researchmentioning

confidence: 99%

Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia

Robinson

Chase

2017

Computational Psychiatry

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“…A novel version of this task has also been developed which utilizes rodent natural investigative behaviours rather than lever pressing, which recapitulate similar effects of affective state manipulations on judgement bias [108]. Further, recent studies have evaluated translational human versions of this task, which link negative biases with pathological anxiety [95,109,110].…”

Section: Rodent Behavioural Assays Of Affective Biasmentioning

confidence: 99%

Affective biases and their interaction with other reward-related deficits in rodent models of psychiatric disorders

Lewis

Benn

Dwyer

et al. 2019

Behavioural Brain Research

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Major depressive disorder (MDD) is one of the leading global causes of disability. Symptoms of MDD can vary person to person, and current treatments often fail to alleviate the poor quality of life that patients experience. One of the two, core diagnostic criteria for MDD is the loss of interest in previously pleasurable activities, which suggests a link between the disease aetiology and reward processing. Cognitive impairments are also common in patients with MDD, and more recently, emotional processing deficits known as affective biases have been recognised as a key feature of the disorder. Studies in animals have found similar affective biases related to reward. In this review we consider these affective biases in the context of other reward-related deficits and examine how affective biases associated with learning and memory may interact with the wider behavioural symptoms seen in MDD. We discuss recent developments in how analogues of affective biases and other aspects of reward processing can be assessed in rodents, as well as how these behaviours are influenced in models of MDD. We subsequently discuss evidence for the neurobiological mechanisms contributing to one or more reward-related deficits in preclinical models of MDD, identified using these behavioural assays. We consider how the relationships between these selective behavioural assays and the neurobiological mechanisms for affective bias and reward processing could be used to identify potential treatment strategies.

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

Slaney¹,

Hinchcliffe²,

Robinson³

2018

Biomarkers in Psychiatry

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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.

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Back-translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders

Cited by 3 publications

References 48 publications

Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia

Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia

Affective biases and their interaction with other reward-related deficits in rodent models of psychiatric disorders

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

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