Drug compulsion manifests in some but not all individuals and implicates multifaceted processes including failures in top‐down cognitive control as drivers for the hazardous pursuit of drug use in some individuals. As a closely related construct, impulsivity encompasses rash or risky behaviour without foresight and underlies most forms of drug taking behaviour, including drug use during adverse emotional states (i.e., negative urgency). While impulsive behavioural dimensions emerge from drug‐induced brain plasticity, burgeoning evidence suggests that impulsivity also predates the emergence of compulsive drug use. Although the neural substrates underlying the apparently causal relationship between trait impulsivity and drug compulsion are poorly understood, significant advances have come from the interrogation of defined limbic cortico‐striatal circuits involved in motivated behaviour and response inhibition, together with chemical neuromodulatory influences from the ascending neurotransmitter systems. We review what is presently known about the neurochemical mediation of impulsivity, in its various forms, and ask whether commonalities exist in the neurochemistry of compulsive drug‐motivated behaviours that might explain individual risk for addiction. image

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Cognitive flexibility refers to the ability to adjust to changes in the environment and is essential for adaptive behavior. It can be investigated using laboratory tests such as probabilistic reversal learning (PRL). In individuals with both Cocaine Use Disorder (CUD) and Gambling Disorder (GD), overall impairments in PRL flexibility are observed. However, it is poorly understood whether this impairment depends on the same brain mechanisms in cocaine and gambling addictions. Reinforcement learning (RL) is the process by which rewarding or punishing feedback from the environment is used to adjust behavior, to maximise reward and minimise punishment. Using RL models, a deeper mechanistic explanation of the latent processes underlying cognitive flexibility can be gained. Here, we report results from a re-analysis of PRL data from control participants (n=18) and individuals with either GD (n=18) or CUD (n=20) using a hierarchical Bayesian RL approach. We observed significantly reduced stimulus stickiness (i.e., stimulus-bound perseveration) in GD, which may reflect increased exploratory behavior that is insensitive to outcomes. RL parameters were unaffected in CUD. We relate the behavioral findings to their underlying neural substrates through an analysis of task-based fMRI data. We report differences in tracking reward and punishment expected values (EV) in individuals with GD compared to controls, with greater activity during reward EV tracking in the cingulate gyrus and amygdala. In CUD, we observed reduced responses to positive punishment prediction errors (PPE) and increased activity following negative PPEs in the superior frontal gyrus compared to controls. Thus, an RL framework serves to differentiate behavior in a probabilistic learning paradigm in two compulsive disorders, GD and CUD.

Only some individuals using drugs recreationally eventually become addicted, and persist in drug seeking and taking despite adverse consequences. The neurobehavioral determinants of this individual vulnerability have not been fully elucidated. We report that in drug naive rats the future tendency to develop compulsive cocaine seeking is characterised by behavioral stickiness-related functional hypoconnectivity between the prefrontal cortex and posterior dorsomedial striatum in combination with impulsivity-related structural alterations in the infralimbic cortex, anterior insula and nucleus accumbens. These findings show that the vulnerability to develop compulsive cocaine seeking behavior stems from pre-existing structural or functional changes in two distinct cortico-striatal systems that underlie deficits in impulse control and goal-directed behavior.

Behavioral and cognitive flexibility allow adaptation to a changing environment. Most tasks used to investigate flexibility require switching reactively in response to deterministic task-response rules. In daily life, flexibility often involves a volitional decision to change behavior. This can be instigated by environmental signals, but these are frequently unreliable. We report results from a novel “change your mind” task, which assesses volitional switching under uncertainty without the need for rule-based learning. Participants completed a two-alternative choice task, and following spurious feedback, were presented with the same stimulus again. Subjects had the opportunity to repeat or change their response. Forty healthy participants completed the task while undergoing a functional magnetic resonance imaging scan. Participants predominantly repeated their choice but changed more when their first response was incorrect or when the feedback was negative. Greater activations for changing were found in the inferior frontal junction, anterior insula (AI), anterior cingulate, and dorsolateral prefrontal cortex. Changing responses were also accompanied by reduced connectivity from the AI and orbitofrontal cortices to the occipital cortex. Using multivariate pattern analysis of brain activity, we predicted with 77% reliability whether participants would change their mind. These findings extend our understanding of cognitive flexibility in daily life by assessing volitional decision-making.

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Introduction Post-haemorrhagic hydrocephalus is common amongst premature infants and one of the leading indications for paediatric cerebrospinal fluid (CSF) diversion. Permanent CSF diversion is often delayed until the infant is older but there is no clear consensus on the timing for this. The outcomes for permanent shunting in this patient group are poor, with higher rates of failure and infection compared to other aetiologies of hydrocephalus. Methods We conduct a single-centre retrospective review of infants with post-haemorrhagic hydrocephalus requiring a permanent shunt insertion over a 5-year period. Demographic and clinical data from time of shunt insertion were collected and used to generate generalised linear models (GLMs) to predict shunt success at 12 months after insertion. Results Twenty-six infants underwent permanent shunting in this period for post-haemorrhagic hydrocephalus, with 10 suffering shunt failure within the first 12 months. The best-performing GLM was able to predict shunt success with a sensitivity of 1 and specificity of 0.90, with head circumference, weight, and corrected age at the time of shunt insertion being the most significantly associated variables for shunt success in this model. Conclusion Our proof-of-principle study suggests that highly accurate prediction of shunt success for infants with post-haemorrhagic hydrocephalus is possible using routinely available clinical variables. Further work is required to test this model in larger cohorts and validate whether pre-operative use can improve outcomes for this patient group.