Generalizing post-stroke prognoses from research data to clinical data

Robert Loughnan, Diego L. Lorca-Puls, Andrea Gajardo-Vidal, Valeria Espejo-Videla, Céline R. Gillebert, Dante Mantini, Cathy J. Price, Thomas M.H. Hope

Research output: Contribution to journalArticlepeer-review

Abstract

Around a third of stroke survivors suffer from acquired language disorders (aphasia), but current medicine cannot predict whether or when they might recover. Prognostic research in this area increasingly draws on datasets associating structural brain imaging data with outcome scores for ever-larger samples of stroke patients. The aim is to learn brain-behaviour trends from these data, and generalize those trends to predict outcomes for new patients. The practical significance of this work depends on the expected breadth of that generalization. Here, we show that these models can generalize across countries and native languages (from British patients tested in English to Chilean patients tested in Spanish), across neuroimaging technology (from MRI to CT), and from scans collected months or years after stroke for research purposes, to scans collected days or weeks after stroke for clinical purposes.

Original languageEnglish
Article number102005
JournalNeuroImage: Clinical
Volume24
DOIs
Publication statusPublished - Jan 1 2019

Keywords

  • Aphasia
  • Lesion growth
  • Plasticity
  • Prognosis
  • Stroke

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience

Fingerprint Dive into the research topics of 'Generalizing post-stroke prognoses from research data to clinical data'. Together they form a unique fingerprint.

Cite this