Beyond the average patient: how neuroimaging models can address heterogeneity in dementia

Abstract

Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.

Keywords: clustering; dementia; heterogeneity; normative modelling; precision medicine.

Figure 1

Differences between case-control and data-driven subtype approaches. (A) The conventional case-control approach. Despite underlying neurobiological heterogeneity, as illustrated by the red, green and blue (RGB) profiles, all patients are analysed together to calculate the group average. This is used to compare with healthy controls to highlight differences between the two groups. Here, the average patient (an average of RGB profiles, circled) assumes neurobiological homogeneity, potentially masking underlying subtypes or individual differences. (B) Data-driven neuroimaging approach. The cases present neurobiological heterogeneity and are subtyped according to their different neurobiological patterns. This informs the division of the case population into its respective subtypes (distinguished RGB profiles). These subtypes can then inform stratification for further investigation, such as clinical interventions, longitudinal monitoring or genome-wide association studies.

Figure 2

Overview of spatial normative modelling. (A) The spatial normative model maps centiles of variation across dementia patients and healthy controls. The Gaussian distribution curve (right) illustrates the statistical inference at the level of a dementia patient (red highlighted subject) with respect to the normative model. (B) An example individual z-score map for the left hemisphere based on cortical thickness. Red indicates thinner cortices, relative to the norm, and blue indicates thicker cortices. (C) Spatial inferences. Spatial normative models are estimated for each sampled brain location in regional space. This can be understood as a set of functions y = f(x), which uses covariates (x) to predict the regional neurobiological variable (y), derived from neuroimaging. (D) Procedural summary of spatial normative modelling. The spatial normative model is estimated using a healthy reference cohort. Next this is validated on withheld data (e.g. using cross-validation techniques), to ensure the accuracy of the model. The model then can be applied to a dementia cohort. (E) Detecting individual trajectories relative to the norm in three different brain regions. Longitudinal data can be used to observe how spatial differences change over time. (F) An example of a single spatial trajectory before and after an intervention in a clinical trial. Red = dementia group; grey = healthy subjects. For the graphs in C, E and F, unlabelled axis x = covariates, y = neurobiological variable.