Abstract Background Despite many successes, the case-control approach is problematic in biomedical science: It introduces an artificial symmetry whereby all clinical groups (e.g. patients and controls) are assumed to be well-defined, when biologically they are often highly heterogeneous. By definition, it also precludes inference over the validity of the diagnostic labels. In response, the NIMH Research Domain Criteria (RDoC) proposes to map relationships between symptom dimensions and broad behavioural and biological domains, cutting across diagnostic categories. To date, however, RDoC has prompted few methods to meaningfully stratify clinical cohorts. Methods We introduce normative modelling for parsing heterogeneity in clinical cohorts while allowing predictions at an individual subject level. This aims to map variation within the cohort and is distinct from, and complementary to, existing approaches that tackle heterogeneity by employing clustering techniques to fractionate cohorts. To demonstrate this approach, we map the relationship between trait impulsivity and reward-related brain activity in a large healthy cohort (N=491). Results We identify participants that are outliers within this distribution and show that the degree of deviation (outlier magnitude) relates to specific attention deficit hyperactivity disorder symptoms (hyperactivity, but not inattention) on the basis of individualized patterns of abnormality. Conclusions Normative modelling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort. Instead, disease can be considered as an extreme of the normal range or as – possibly idiosyncratic – deviation from normal functioning. It also enables inferences over the degree to which behavioural variables – including diagnostic labels – map onto biology.