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Publication Detail
Reclassifying stroke lesion anatomy
  • Publication Type:
    Journal article
  • Authors:
    Bonkhoff AK, Xu T, Nelson A, Gray R, Jha A, Cardoso J, Ourselin S, Rees G, Jäger HR, Nachev P
  • Publisher:
    Elsevier BV
  • Publication date:
  • Journal:
  • Status:
  • Language:
  • Keywords:
    Strokelesion anatomy, lesion–deficit prediction, dimensionality reduction, brain imaging
  • Notes:
    © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY 4.0 license Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)
Cognitive and behavioural outcomes in stroke reflect the interaction between two complex anatomically-distributed patterns: the functional organization of the brain and the structural distribution of ischaemic injury. Conventional outcome models—for individual prediction or population-level inference—commonly ignore this complexity, discarding anatomical variation beyond simple characteristics such as lesion volume. This sets a hard limit on the maximum fidelity such models can achieve. High-dimensional methods can overcome this problem, but only at prohibitively large data scales. Drawing on one of the largest published collections of anatomically-registered imaging of acute stroke—N=1333—here we use non-linear dimensionality reduction to derive a succinct latent representation of the anatomical patterns of ischaemic injury, agglomerated into 21 distinct intuitive categories. We compare the maximal predictive performance it enables against both simpler low-dimensional and more complex high-dimensional representations, employing multiple empirically-informed ground truth models of distributed structure-outcome relationships. We show our representation sets a substantially higher ceiling on predictive fidelity than conventional low-dimensional approaches, but lower than that achievable within a high-dimensional framework. Where descriptive simplicity is a necessity, such as within clinical care or research trials of modest size, the representation we propose arguably offers a favourable compromise of compactness and fidelity.
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