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

Journal article
Authors Jacob W Vogel
Niklas Mattsson
Yasser Iturria-Medina
Olof T Strandberg
Michael Schöll
Christian Dansereau
Sylvia Villeneuve
Wiesje M van der Flier
Philip Scheltens
Pierre Bellec
Alan C Evans
Oskar Hansson
Rik Ossenkoppele
Published in Human brain mapping
Volume 40
Issue 2
Pages 638-651
ISSN 1097-0193
Publication year 2019
Published at Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Wallenberg Centre for Molecular and Translational Medicine
Pages 638-651
Language en
Subject categories Computer and Information Science, Neurosciences


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 [18 F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18 F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18 F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18 F]AV1451 scans. We performed linear models comparing [18 F]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 [18 F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

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