Abstract
Normative models are a class of emerging statistical techniques useful for understanding the heterogeneous biology underlying psychiatric disorders at the level of the individual participant. Analogous to normative growth charts used in paediatric medicine for plotting child development in terms of height or weight as a function of age, normative models chart variation in clinical cohorts in terms of mappings between quantitative biological measures and clinically relevant variables. An emerging body of literature has demonstrated that such techniques are excellent tools for parsing the heterogeneity in clinical cohorts by providing statistical inferences at the level of the individual participant with respect to the normative range. Here, we provide a unifying review of the theory and application of normative modelling for understanding the biological and clinical heterogeneity underlying mental disorders. We first provide a statistically grounded yet non-technical overview of the conceptual underpinnings of normative modelling and propose a conceptual framework to link the many different methodological approaches that have been proposed for this purpose. We survey the literature employing these techniques, focusing principally on applications of normative modelling to quantitative neuroimaging-based biomarkers in psychiatry and, finally, we provide methodological considerations and recommendations to guide future applications of these techniques. We show that normative modelling provides a means by which the importance of modelling individual differences can be brought from theory to concrete data analysis procedures for understanding heterogeneous mental disorders and ultimately a promising route towards precision medicine in psychiatry.
Highlights
Normative models are a class of emerging statistical techniques useful for understanding the heterogeneous biology underlying psychiatric disorders at the level of the individual participant
Whilst early applications focused on brain development and ageing [16, 18], normative modelling has recently been shown to be highly promising for psychiatry [12, 14, 17, 19– 21]
To map variation related to brain development and ageing in psychiatric disorders, which is appealing given the neurodevelopmental basis of mental disorders [22]
Summary
Partial least squares No impairment and Alzheimer’s disease and quantile regression. Yes white matter volume, total with mild cognitive impairment and regression cerebrospinal fluid, MRI field dementia strength. Most large datasets include data from multiple study sites, which increases the risk of missing data and introduces the possibility that observed deviations could be related to site variance To address these concerns, careful stratification procedures during model fitting and cross-validation and explicitly modelling different sources of variance are important (e.g. using hierarchical models). Separate normative models can be estimated for different cognitive and biological mappings This has the effect of rescaling different variables to a common reference range (for example, Z-statistics reflecting the number of standard deviations each subject is from the population norm). This forms an ideal set of features for the application of clustering algorithms, in the spirit of precision medicine. Estimating normative models to link multiple phenotypic measurements with their multifaceted biological underpinnings is likely to be very important to: (i) understand disorders across multiple domains in the spirit of RDoC and ROAMER; (ii) identify different groups of patients with different atypical mechanisms; (iii) to better understand healthy variation and how this relates to the mechanisms of mental disorders and (iv) to move beyond simple dimensional theories of mental disorders [25]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
