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

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          Abstract

          Objective: 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. Method: 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 voxelwise clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data-driven, clusters and 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 Mini-Mental State Examination (MMSE) scores, adjusting for age, sex and education. Results: Significant relationships emerged only in two ROIs, both of which were data-driven. Inputting all regions plus demographics into a feature selection routine resulted in selection of three ROIs (two data-driven, one pathology-driven) and education, which together explained 25% of variance in MMSE scores. These results generalized to other tests of global cognition. Interpretation: Our findings suggest that hypothesis-free, data-derived ROIs may offer enhanced clinical utility compared to theory-driven ROIs, by utilizing information specific to tau-PET signal.

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          Author and article information

          Journal
          bioRxiv
          January 11 2018
          Article
          10.1101/244574
          © 2018
          Product

          Molecular medicine, Neurosciences

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