Neuroscience has now truly transitioned into the era of big data and is witnessing an explosion of the number and types of biological measures that can -and are- measured in clinical populations. This has given rise to the emerging field of population neuroscience, which holds enormous potential to improve prediction of disease states in many clinical conditions. However, achieving this objective places increasing demands on the statistical and machine learning methodology that are used to analyse these cohorts, in particular, finding methods to understand population variation in  imaging derived markers and understanding their relationship between environmental and behavioural factors remain an enormous challenge. In this talk I will describe conceptual innovations that enable us to make progress in this domain, including normative modelling or ‘brain growth charting’ techniques that allow us to chart variability at the level of each individual,  techniques rooted in the statistics of extremes that allow us to reconceptualise pathology as extreme deviations from an expected pattern and statistical approaches to predict clinical variables on the basis of smartphone based digital phenotyping data. I will illustrate this discussion by showing applications of these methods to cross-diagnostic psychiatric cohorts and I will argue that these innovations provide a principled method to move beyond simple statements about group averages and can instead provide a way to dissect the inherent heterogeneity in mental disorders, ultimately enabling more accurate prediction of the onset, course and outcome of brain disorders and paving the way to earlier and personalised interventions.