Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls

TY - JOUR

T1 - Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls

AU - Westman, Eric

AU - Simmons, Andrew

AU - Zhang, Yi

AU - Muehlboeck, J-Sebastian

AU - Tunnard, Catherine

AU - Liu, Yawu

AU - Collins, Louis

AU - Evans, Alan

AU - Mecocci, Patrizia

AU - Vellas, Bruno

AU - Tsolaki, Magda

AU - Kloszewska, Iwona

AU - Soininen, Hilkka

AU - Lovestone, Simon

AU - Spenger, Christian

AU - Wahlund, Lars-Olof

PY - 2011/1/15

Y1 - 2011/1/15

N2 - We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD. (C) 2010 Elsevier Inc. All rights reserved.

AB - We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD. (C) 2010 Elsevier Inc. All rights reserved.

U2 - 10.1016/j.neuroimage.2010.08.044

DO - 10.1016/j.neuroimage.2010.08.044

M3 - Article

SN - 1095-9572

VL - 54

SP - 1178

EP - 1187

JO - NeuroImage

JF - NeuroImage

IS - 2

ER -