Till sidans topp

Sidansvarig: Webbredaktion
Sidan uppdaterades: 2012-09-11 15:12

Tipsa en vän
Utskriftsversion

Data-driven approaches fo… - Göteborgs universitet Till startsida
Webbkarta
Till innehåll Läs mer om hur kakor används på gu.se

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

Artikel i vetenskaplig tidskrift
Författare 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
Publicerad i Human brain mapping
Volym 40
Nummer/häfte 2
Sidor 638-651
ISSN 1097-0193
Publiceringsår 2019
Publicerad vid Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi
Wallenberg Centre for Molecular and Translational Medicine
Sidor 638-651
Språk en
Länkar dx.doi.org/10.1002/hbm.24401
www.ncbi.nlm.nih.gov/entrez/query.f...
Ämneskategorier Data- och informationsvetenskap, Neurovetenskaper

Sammanfattning

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.

Sidansvarig: Webbredaktion|Sidan uppdaterades: 2012-09-11
Dela:

På Göteborgs universitet använder vi kakor (cookies) för att webbplatsen ska fungera på ett bra sätt för dig. Genom att surfa vidare godkänner du att vi använder kakor.  Vad är kakor?