Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI
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Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI. / Erleben, Lene Lillemark; Sørensen, Lauge; Mysling, Peter; Pai, Akshay Sadananda Uppinakudru; Dam, Erik B.; Nielsen, Mads.
Medical Imaging 2013: image processing . ed. / Sebastien Ourselin; David R. Haynor. SPIE - International Society for Optical Engineering, 2013. 866926 (Progress in Biomedical Optics and Imaging; No. 36, Vol. 14).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI
AU - Erleben, Lene Lillemark
AU - Sørensen, Lauge
AU - Mysling, Peter
AU - Pai, Akshay Sadananda Uppinakudru
AU - Dam, Erik B.
AU - Nielsen, Mads
PY - 2013
Y1 - 2013
N2 - This work investigates a novel way of looking at the regions in the brain and their relationship as possible markers to classify normal control (NC), mild cognitive impaired (MCI), and Alzheimer Disease (AD) subjects. MRI scans from a subset of 101 subjects from the ADNI study at baseline was used for this study. 40 regions in the brain including hippocampus, amygdala, thalamus, white, and gray matter were segmented using Free Surfer. From this data, we calculated the distance between the center of mass of each region, the normalized number of voxels and the percentage volume and surface connectivity shared between the regions. These markers were used for classification using a linear discriminant analysis in a leave-one-out manner. We found that the percentage of surface and volume connectivity between regions gave a significant classification between NC and AD and borderline significant between MCI and AD even after correction for whole brain volume at baseline. The results show that the morphometric connectivity markers include more information than whole brain volume or distance markers. This suggests that one can gain additional information by combining morphometric connectivity markers with traditional volume and shape markers.
AB - This work investigates a novel way of looking at the regions in the brain and their relationship as possible markers to classify normal control (NC), mild cognitive impaired (MCI), and Alzheimer Disease (AD) subjects. MRI scans from a subset of 101 subjects from the ADNI study at baseline was used for this study. 40 regions in the brain including hippocampus, amygdala, thalamus, white, and gray matter were segmented using Free Surfer. From this data, we calculated the distance between the center of mass of each region, the normalized number of voxels and the percentage volume and surface connectivity shared between the regions. These markers were used for classification using a linear discriminant analysis in a leave-one-out manner. We found that the percentage of surface and volume connectivity between regions gave a significant classification between NC and AD and borderline significant between MCI and AD even after correction for whole brain volume at baseline. The results show that the morphometric connectivity markers include more information than whole brain volume or distance markers. This suggests that one can gain additional information by combining morphometric connectivity markers with traditional volume and shape markers.
U2 - 10.1117/12.2007600
DO - 10.1117/12.2007600
M3 - Article in proceedings
SN - 9780819494436
T3 - Progress in Biomedical Optics and Imaging
BT - Medical Imaging 2013
A2 - Ourselin, Sebastien
A2 - Haynor, David R.
PB - SPIE - International Society for Optical Engineering
Y2 - 10 February 2013 through 12 February 2013
ER -
ID: 169383017