Introduction
Panic attacks are characterised by brief episodes of intense arousal and fear that arise suddenly, often in the absence of a clear internal or external trigger. Cognitive–behavioural theories posit that panic attacks arise from a ‘vicious cycle’ between the bodily sensations associated with physiological arousal and a sense of perceived threat.1–4 Perhaps the most well known of these theories is David Clark’s cognitive model, which posits that panic attacks arise when a person misinterprets bodily sensations (eg, increased heart rate) as a sign of impending danger (eg, a heart attack).5 These ‘catastrophic misinterpretations’ lead to a perception of threat which, in turn, increases physiological arousal, thereby feeding into a positive feedback loop that culminates in a panic attack.
Recently, Robinaugh and colleagues6 formalised these ‘vicious cycle’ theories in a computational model of panic disorder: a syndrome characterised by recurrent panic attacks, persistent concern about those attacks and avoidance of situations in which they may occur. One of the key advantages of formalising theories in this way is that it allows researchers to simulate the behaviour implied by the theory, thereby providing a tool to evaluate what the theory can explain and what it cannot. The simulated behaviour of the computational panic disorder model suggests that it is able to explain key phenomena observed in panic disorder, including individual differences in the propensity to experience panic attacks and phenomenological characteristics of those attacks.
The computational model also makes several novel predictions. One such prediction is that the vulnerability of the system to panic attacks and panic disorder can be indexed by how it responds to perturbation. In particular, vulnerability (or, conversely, resilience) can be indexed by the duration of time to respond to a perturbation (an index known as ‘engineering resilience’ in the dynamical systems literature).7 In the panic disorder model, when the positive feedback loop between arousal and perceived threat is weak, perturbations to arousal cause only a brief and modest increase in arousal and perceived threat. However, when this positive feedback is strong, it takes longer to recover from perturbation. In other words, the time taken to respond to perturbations is an indicator of vulnerability to panic attacks.
This prediction is noteworthy because there is a large literature examining how individuals with panic disorder respond to just such a perturbation. These perturbations, known as ‘biological challenges’, entail the administration of standard procedures, typically the injection or inhalation of a substance (eg, lactate infusion8 or CO2 inhalation9), in order to induce arousal-related bodily sensations.10 11 That is, these challenges perturb the system by increasing arousal and then evaluate how the system responds.
The panic disorder model suggests that these biological challenges could thus serve two valuable purposes. First, the challenges can be used to evaluate the cognitive–behavioural theories embodied in the computational model. The model explicitly predicts that psychological and physiological reactivity during biological challenge procedures reflects vulnerability to panic attacks and, thus, should be predictive of subsequent spontaneous panic attacks and panic disorder. Second, if this model prediction is supported, it would suggest that biological challenges have clinical value as objective measures of vulnerability to panic attacks.12
In this paper, we reviewed empirical studies evaluating the relationship between response to a biological challenge and subsequent panic-related outcomes. We had two overarching aims. First, we aimed to identify the challenge procedures and measures of psychological and physiological reactivity that have been studied as predictors of subsequent panic attacks and panic disorder. Second, we aimed to evaluate whether response to a biological challenge is indeed predictive of subsequent panic attacks and panic disorder.