Automatic morphometry in Alzheimer's disease and mild cognitive impairment

Rolf A Heckemann, Shiva Keihaninejad, Paul Aljabar, Katherine R Gray, Casper Nielsen, Daniel Rueckert, Joseph V Hajnal, Alexander Hammers, Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

104 Citations (Scopus)
485 Downloads (Pure)

Abstract

This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline images (1.5 T and 3 T) have been processed with the multi-atlas based MAPER procedure, resulting in labels for 83 regions covering the whole brain in 816 subjects. Selected segmentations were subjected to visual assessment. The segmentations are self-consistent, as evidenced by strong agreement between segmentations of paired images acquired at different field strengths (Jaccard coefficient: 0.802 +/- 0.0146). Morphometric comparisons between diagnostic groups (normal; stable mild cognitive impairment; mild cognitive impairment with progression to Alzheimer's disease; Alzheimer's disease) showed highly significant group differences for individual regions, the majority of which were located in the temporal lobe. Additionally, significant effects were seen in the parietal lobe. Increased left/right asymmetry was found in posterior cortical regions. An automatically derived white-matter hypointensities index was found to be a suitable means of quantifying white-matter disease. This repository of segmentations is a potentially valuable resource to researchers working with ADNI data. (C) 2011 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)2024-2037
Number of pages14
JournalNeuroImage
Volume56
Issue number4
DOIs
Publication statusPublished - 15 Jun 2011

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