Data-driven approaches for tau-PET imaging biomarkers in Alzheimer's disease

Research output: Contribution to journalArticle

Abstract

Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published “pathology-driven” ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18F]AV1451 scans. We performed linear models comparing [18F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

Details

Authors
Organisations
External organisations
  • VU University Medical Center
  • Skåne University Hospital
  • University of Gothenburg
  • University of Montreal, Canada
  • McGill University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Gerontology, specializing in Medical and Health Sciences

Keywords

  • Alzheimer's disease, AV1451, cognition, data-driven, tau-PET
Original languageEnglish
Pages (from-to)638-651
JournalHuman Brain Mapping
Volume40
Issue number2
Early online date2018 Jan 1
Publication statusPublished - 2019 Feb 1
Publication categoryResearch
Peer-reviewedYes