Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry

Hum Brain Mapp. 2015 Feb;36(2):463-74. doi: 10.1002/hbm.22640. Epub 2014 Sep 18.

Abstract

Growing interest has developed in hippocampal subfield volumetry over the past few years and an increasing number of studies use the automatic segmentation algorithm implemented in FreeSurfer. However, this approach has not been validated on standard resolution T1-weighted magnetic resonance (MR) as used in most studies. We aimed at comparing hippocampal subfield segmentation using FreeSurfer on standard T1-weighted images versus manual delineation on dedicated high-resolution hippocampal scans. Hippocampal subfields were segmented in 133 individuals including 98 cognitively normal controls aged 19-84 years, 17 mild cognitive impairment and 18 Alzheimer's disease (AD) patients using both methods. Intraclass correlation coefficients (ICC) and Bland-Altman plots were computed to assess the consistency between both methods, and the effects of age and diagnosis were assessed from both measures. Low to moderate ICC (0.31-0.74) were found for the subiculum and other subfields as well as for the whole hippocampus, and the correlations were very low for cornu ammonis (CA)1 (<0.1). FreeSurfer CA1 volume estimates were found to be much lower than those obtained from manual segmentation, and this bias was proportional to the volume of this structure so that no effect of age or AD could be detected on FreeSurfer CA1 volumes. This study points to the differences in the anatomic definition of the subfields between FreeSurfer and manual delineation, especially for CA1, and provides clue for improvement of this automatic technique for potential clinical application on standard T1-weighted MR.

Keywords: Alzheimer's disease; FreeSurfer segmentation; aging; hippocampal subfields; magnetic resonance imaging; manual segmentation.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Aging / pathology*
  • Algorithms
  • Alzheimer Disease / pathology*
  • Cognitive Dysfunction / pathology*
  • Hippocampus / pathology*
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging / methods
  • Middle Aged
  • Organ Size
  • Pattern Recognition, Automated
  • Software
  • Young Adult