Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.

Journal: EBioMedicine
PMID: 36972630

Abstract

BACKGROUND: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines.

Authors

  • Sebastian Moguilner
    Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Robert Whelan
    School of Psychology and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland. robert.whelan@tcd.ie.
  • Hieab Adams
    Departments of Radiology and Epidemiology, Erasmus MC - University Medical Center Rotterdam, The Netherlands.
  • Victor Valcour
    Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland.
  • Enzo Tagliazucchi
    Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA - CONICET), Pabellón I, Ciudad Universitaria (1428), Buenos Aires, Argentina.
  • Agustin Ibanez
    National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina. aibanez@ineco.org.ar.

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