Verdi, S;
Marquand, AF;
Schott, JM;
Cole, JH;
(2021)
Beyond the average patient: how neuroimaging models can address heterogeneity in dementia.
Brain
10.1093/brain/awab165.
(In press).
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Abstract
Dementia is a highly heterogeneous condition, with pronounced individual differences in onset age, 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 the comparison of group average differences (e.g., patient versus control, 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 characterisation 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 review we outline the causes and consequences of heterogeneity in dementia and discuss recent research which aims to directly tackle heterogeneity, rather than assume 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 individualised 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.
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