TY - JOUR
T1 - Detection of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using Longitudinal Brain MRI
AU - Sun, Zhuo
AU - van de Giessen, Martijn
AU - Lelieveldt, Boudewijn P.F.
AU - Staring, Marius
PY - 2017
Y1 - 2017
N2 - Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer’s disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer’s Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures. In this study we propose to exploit the anatomical development within the entire brain, as found by a non-rigid registration approach. Specifically, this anatomical development is represented by the Stationary Velocity Field (SVF) from registration between the baseline and follow-up images. To make the SVFs comparable among subjects, we use the parallel transportmethod to align themin a common space. The normalized SVF together with derived features are then used to distinguish between MCIc and MCIs subjects. This novel feature space is reduced using a Kernel Principal Component Analysis method, and a linear support vector machine is used as a classifier. Extensive comparative experiments are performed to inspect the influence of several aspects of our method on classification performance, specifically the feature choice, the smoothing parameter in the registration and the use of dimensionality reduction. The optimal result from a 10-fold cross-validation using 36 month follow-up data shows competitive results: accuracy 92%, sensitivity 95%, specificity 90%, and AUC 94%. Based on the same dataset, the proposed approach outperforms two alternative ones that either depends on the baseline image only, or uses longitudinal information from larger brain areas. Good results were also obtained when scans at 6, 12, or 24 months were used for training the classifier. Besides the classification power, the proposed method can quantitatively compare brain regions that have a significant difference in development between the MCIc and MCIs groups.
AB - Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer’s disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer’s Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures. In this study we propose to exploit the anatomical development within the entire brain, as found by a non-rigid registration approach. Specifically, this anatomical development is represented by the Stationary Velocity Field (SVF) from registration between the baseline and follow-up images. To make the SVFs comparable among subjects, we use the parallel transportmethod to align themin a common space. The normalized SVF together with derived features are then used to distinguish between MCIc and MCIs subjects. This novel feature space is reduced using a Kernel Principal Component Analysis method, and a linear support vector machine is used as a classifier. Extensive comparative experiments are performed to inspect the influence of several aspects of our method on classification performance, specifically the feature choice, the smoothing parameter in the registration and the use of dimensionality reduction. The optimal result from a 10-fold cross-validation using 36 month follow-up data shows competitive results: accuracy 92%, sensitivity 95%, specificity 90%, and AUC 94%. Based on the same dataset, the proposed approach outperforms two alternative ones that either depends on the baseline image only, or uses longitudinal information from larger brain areas. Good results were also obtained when scans at 6, 12, or 24 months were used for training the classifier. Besides the classification power, the proposed method can quantitatively compare brain regions that have a significant difference in development between the MCIc and MCIs groups.
KW - Alzheimer’s disease
KW - Conversion
KW - Mild cognitive impairment (MCI)
KW - MRI
KW - Non-rigid registration
KW - Parallel transport
KW - Stationary velocity field (SVF)
KW - SVM classification
UR - http://www.scopus.com/inward/record.url?scp=85015441259&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:ab9f68e7-4a52-44e3-b343-0c917de50d85
U2 - 10.3389/fninf.2017.00016
DO - 10.3389/fninf.2017.00016
M3 - Article
AN - SCOPUS:85015441259
SN - 1662-5196
VL - 11
SP - 1
EP - 16
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 16
ER -