Automatic morphometry in Alzheimer's disease and mild cognitive impairment

作者：Heckemann Rolf A^*; Keihaninejad Shiva; Aljabar Paul; Gray Katherine R; Nielsen Casper; Rueckert Daniel; Hajnal Joseph V; Hammers Alexander

来源：NeuroImage, 2011, 56(4): 2024-2037.

DOI：10.1016/j.neuroimage.2011.03.014

• Image segmentation
• Brain pathology
• Brain atlas
• Magnetic resonance imaging
• Alzheimer's disease
• Mild cognitive impairment

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.

• 出版日期2011-6-15