Ontology highlight
ABSTRACT: Objective
To determine the frequency of high-convexity tight sulci (HCTS) in a population-based sample and whether the presence of HCTS and related features influenced participants' cognitive status and classification within the new Alzheimer-biomarker framework.Methods
We analyzed 684 participants ≥50 years of age who were enrolled in the prospective population-based Mayo Clinic Study of Aging and underwent structural MRI, amyloid PET imaging, and tau PET imaging. A fully automated machine-learning algorithm that had been developed previously in house was used to detect neuroimaging features of HCTS. On the basis of PET and MRI measures, participants were classified as having normal (A-) or abnormal (A+) amyloid, normal (T-) or abnormal (T<
SUBMITTER: Graff-Radford J
PROVIDER: S-EPMC6937493 | biostudies-literature | 2019 Dec
REPOSITORIES: biostudies-literature