Abstract
Mood and anxiety disorders are ubiquitous but current treatment options are ineffective for many sufferers. Moreover, a number of promising pre-clinical interventions have failed to translate into clinical efficacy in humans. Improved treatments are unlikely without better animal-human translational pipelines. Here, we translate a rodent measure of negative affective bias into humans, exploring its relationship with (1) pathological mood and anxiety symptoms and (2) transient induced anxiety. Adult participants (age = 29 ± 11) who met criteria for mood or anxiety disorder symptomatology according to a face-to-face neuropsychiatric interview were included in the symptomatic group. Study 1 included N = 77 (47 = asymptomatic [female = 21]; 30 = symptomatic [female = 25]), study 2 included N = 47 asymptomatic participants (25 = female). Outcome measures were choice ratios, reaction times and parameters recovered from a computational model of reaction time - the drift diffusion model (DDM) - from a two-alternative-forced-choice task in which ambiguous and unambiguous auditory stimuli were paired with high and low rewards. Both groups showed over 93% accuracy on unambiguous tones indicating intact discrimination, but symptomatic individuals demonstrated increased negative affective bias on ambiguous tones [proportion high reward = 0.42 (s.d. = 0.14)] relative to asymptomatic individuals [0.53 (s.d. = 0.17)] as well as a significantly reduced DDM drift rate. No significant effects were observed for the within-subjects anxiety-induction. Humans with pathological anxiety symptoms directly mimic rodents undergoing anxiogenic manipulation. The lack of sensitivity to transient anxiety suggests the paradigm might be more sensitive to clinically relevant symptoms. Our results establish a direct translational pipeline (and candidate therapeutics screen) from negative affective bias in rodents to pathological mood and anxiety symptoms in humans.
Highlights
Mood and anxiety disorders are extremely prevalent worldwide, with huge psychological, economical and social costs (Beddington et al, 2008)
Affective bias was calculated by dividing the number of ‘high reward’ responses made to the mid-tone by the total number of key presses made to the mid-tone and compared across groups or conditions using paired sample t tests and Bayesian equivalents
In this study we directly translate a rodent measure of affective bias
Summary
Mood and anxiety disorders are extremely prevalent worldwide, with huge psychological, economical and social costs (Beddington et al, 2008). We explored the impact of two types of anxiety on a human version of this task: (a) pathological anxiety in mood and anxiety disorders, and (b) acute stress induced using threat of unpredictable shock The latter stress induction is a wellvalidated and reliable technique, translated from animal models (Robinson et al, 2011; Aylward and Robinson, 2017). Computational models can make specific predictions about the underlying mechanisms that drive behaviour and enable a more fine-grained view of decision-making and how it changes in pathological states (Robinson and Chase, 2017) One such model – the drift diffusion model (DDM) – has been applied to rodent data on this task (Hales et al, 2016). We predicted that negative bias in choice behaviour would be associated with alterations to drift diffusion parameters
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call