Aylward, J. | Search bioRxiv

bioRxiv2017

Back-Translating A Rodent Measure Of Negative Bias Into Humans: The Impact Of Induced Anxiety And Unmedicated Mood And Anxiety Disorders

BackgroundMood and anxiety disorders are ubiquitous but current treatment options are ineffective for large numbers of sufferers. Moreover, recent years have seen a number of promising pre-clinical interventions fail to translate into clinical efficacy in humans. Improved treatments are unlikely without better animal-human translational pipelines. Here, we directly adapt-i.e. back-translate - a rodent measure of negative affective bias into humans, and explore its relationship with a)pathological mood and anxiety symptoms (study one) and b)transient induced anxiety (study two).\n\nMethodParticipants who met criteria for mood or anxiety disorder symptomatology according to a face-to-face neuropsychiatric interview were included in the symptomatic group. N = 77(47 asymptomatic; Female = 21; 30 symptomatic; Female = 25) participants completed study one and N = 47 asymptomatic participants (25 female) completed study two. Outcome measures were choice ratios, reaction times and parameters recovered from a computational model of reaction time; the drift diffusion model (DDM).\n\nResultsSymptomatic individuals demonstrated increased negative affective bias relative to asymptomatic individuals (proportion high reward = 0.42(SD = 0.14), and 0.53(SD = 0.17), respectively) as well as reduced DDM drift rate (p = 0.004). No significant effects were observed for the within-subjects anxiety-induction in study 2.\n\nConclusionHumans with pathological anxiety symptoms directly mimic rodents undergoing anxiogenic manipulation. The lack of sensitivity to transient anxiety suggests the paradigm may, moreover, be primarily 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, and link it to a computational model of reaction time.

animal behavior and cognition↗

bioRxiv2016

Modelling avoidance in pathologically anxious humans using reinforcement-learning

Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behaviour -avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms, the biological mechanisms underlying avoidance remain unclear. Reinforcement-learning models provide formal and testable characterizations of the mechanisms of decision-making; here, we examine avoidance in these terms. One hundred and one healthy and pathologically anxious individuals completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the anxious group on a parameter of our reinforcement-learning model that characterizes a prepotent (Pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the anxious individuals were under stress. This formal description of avoidance within the reinforcement-learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of pathological anxiety.

animal behavior and cognition↗