Journal
BRAIN
Volume 144, Issue -, Pages 2946-2953Publisher
OXFORD UNIV PRESS
DOI: 10.1093/brain/awab165
Keywords
normative modelling; heterogeneity; precision medicine; clustering; dementia
Categories
Funding
- EPSRC [EP/S021930/1]
- Department of Health's National Institute for Health Research funded University College London Hospitals Biomedical Research Centre
- Dutch Organization for Scientific Research via a VIDI fellowship [016.156.415]
- Alzheimers Research UK
- Brain Research UK
- Weston Brain Institute
- British Heart Foundation
- Medical Research Council
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Dementia is a highly heterogeneous condition with significant individual differences. Traditional statistical designs overlook this heterogeneity and rely on group average differences. New techniques, such as spatial normative modelling, offer the potential for individualized approaches in dementia research and treatment.
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.
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