title: An automated tool for cortical feature analysis: Application to differences on 7 Tesla T2*-weighted images between young and older healthy subjects.
abstract: OBJECTIVE
  High field T2*-weighted MR images of the cerebral cortex are increasingly used to study tissue susceptibility changes related to aging or pathologies. This paper presents a novel automated method for the computation of quantitative cortical measures and group-wise comparison using 7 Tesla T2*-weighted magnitude and phase images.
  
  
  METHODS
  The cerebral cortex was segmented using a combination of T2*-weighted magnitude and phase information and subsequently was parcellated based on an anatomical atlas. Local gray matter (GM)/white matter (WM) contrast and cortical profiles, which depict the magnitude or phase variation across the cortex, were computed from the magnitude and phase images in each parcellated region and further used for group-wise comparison. Differences in local GM/WM contrast were assessed using linear regression analysis. Regional cortical profiles were compared both globally and locally using permutation testing. The method was applied to compare a group of 10 young volunteers with a group of 15 older subjects.
  
  
  RESULTS
  Using local GM/WM contrast, significant differences were revealed in at least 13 of 17 studied regions. Highly significant differences between cortical profiles were shown in all regions.
  
  
  CONCLUSIONS
  The proposed method can be a useful tool for studying cortical changes in normal aging and potentially in neurodegenerative diseases. Magn Reson Med 74:240-248, 2015. © 2014 Wiley Periodicals, Inc.
authors.affiliation: Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Nhat Trung
authors.initials: NT
authors.lastname: Doan
authors.affiliation: C.J. Gorter Center for High-field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.|Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Sanneke
authors.initials: S
authors.lastname: van Rooden
authors.affiliation: C.J. Gorter Center for High-field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.|Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Maarten J
authors.initials: MJ
authors.lastname: Versluis
authors.affiliation: Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Mathijs
authors.initials: M
authors.lastname: Buijs
authors.affiliation: C.J. Gorter Center for High-field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.|Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Andrew G
authors.initials: AG
authors.lastname: Webb
authors.affiliation: Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Jeroen
authors.initials: J
authors.lastname: van der Grond
authors.affiliation: C.J. Gorter Center for High-field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.|Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Mark A
authors.initials: MA
authors.lastname: van Buchem
authors.affiliation: Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Johan H C
authors.initials: JHC
authors.lastname: Reiber
authors.affiliation: Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Julien
authors.initials: J
authors.lastname: Milles
keywords: MR phase; aging; cerebral cortex; iron; myelin; neurodegenerative diseases