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O4‐07‐03: Longitudinal Changes in Regional Tau Deposition in Alzheimer's Disease and other Tauopathies Measured by [ 18 F]‐TKH5317 Pet in a Multi‐Tracer Design
Author(s) -
Nordberg Agneta,
Chiotis Konstantinos,
Saint-Aubert Laure,
Savitcheva Irina,
Jelic Vesna,
Andersen Pia,
Almkvist Ove,
Wall Anders,
Antoni Gunnar
Publication year - 2016
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2016.06.645
Subject(s) - pittsburgh compound b , dementia , pathology , pathological , medicine , grey matter , alzheimer's disease , neurodegeneration , psychology , nuclear medicine , disease , neuroscience , white matter , magnetic resonance imaging , radiology
healthy connectomes from (Raj, 2015), and tested on ADNI imaging data (Table 1). Table 2 shows the sites of initial pathology as predicted by the model, which closely matches observed pathology. Fig 2 shows the spatiotemporal evolution of the model, which closely recapitulates expected progression of tau and amyloid pathology. Model predictions significantly match empirical ADNI data (Fig 3). Conclusions:Presented theoretical network model successfully recapitulated Braak and Thal stages, respectively, having made no assumptions beyond network spread and healthy regional metabolism patterns. It correctly predicts both the early sites of tau and amyloid pathology, as well as subsequent spread into wider circuits. The model could help explain why amyloid and tau appear to accumulate in certain brain sites, why they spread in such stereotyped fashion, and why the two appear to have different spatiotemporal patterns of spread, even though amyloid drives tau accumulation. The clinical utility of this model is in multi-modal modeling of tau, amyloid and atrophy in neuroimaging data.