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

, ; , ; Vogel, Jacob W.; Mattsson, Niklas LU ; Iturria-Medina, Yasser; Strandberg, Olof T. LU ; Schöll, Michael LU ; Dansereau, Christian; Villeneuve, Sylvia and van der Flier, Wiesje M., et al. (2019) In Human Brain Mapping 40(2). p.638-651
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... (More)

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.

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Contribution to journal
publication status
published
subject
keywords
Alzheimer's disease, AV1451, cognition, data-driven, tau-PET
in
Human Brain Mapping
volume
40
issue
2
pages
638 - 651
publisher
Wiley-Blackwell
external identifiers
  • scopus:85055686414
ISSN
1065-9471
DOI
10.1002/hbm.24401
language
English
LU publication?
yes
id
b7e60b40-ddcd-42bd-aa8b-ef5312ec4566
date added to LUP
2018-11-26 08:04:18
date last changed
2019-06-25 03:46:15
@article{b7e60b40-ddcd-42bd-aa8b-ef5312ec4566,
  abstract     = {<p>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 [<sup>18</sup>F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [<sup>18</sup>F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [<sup>18</sup>F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [<sup>18</sup>F]AV1451 scans. We performed linear models comparing [<sup>18</sup>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 [<sup>18</sup>F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.</p>},
  author       = {,  and ,  and Vogel, Jacob W. and Mattsson, Niklas and Iturria-Medina, Yasser and Strandberg, Olof T. and Schöll, Michael and Dansereau, Christian and Villeneuve, Sylvia and van der Flier, Wiesje M. and Scheltens, Philip and Bellec, Pierre and Evans, Alan C. and Hansson, Oskar and Ossenkoppele, Rik},
  issn         = {1065-9471},
  keyword      = {Alzheimer's disease,AV1451,cognition,data-driven,tau-PET},
  language     = {eng},
  month        = {02},
  number       = {2},
  pages        = {638--651},
  publisher    = {Wiley-Blackwell},
  series       = {Human Brain Mapping},
  title        = {Data-driven approaches for tau-PET imaging biomarkers in Alzheimer's disease},
  url          = {http://dx.doi.org/10.1002/hbm.24401},
  volume       = {40},
  year         = {2019},
}