Abstract
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 transport method to align them in 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.
Highlights
Alzheimer’s disease (AD), one of the most common cases of dementia, is an age related degenerative brain disease
Different kinds of measurements and biomarkers have been used in early detection and prediction of AD, e.g., structural brain Magnetic Resonance Imaging (MRI) (Frisoni et al, 2010), metabolic brain alterations measured by fluorodeoxyglucose positron emission tomography (FDG-PET) (De Santi et al, 2001), and pathological amyloid depositions measured from cerebrospinal fluid (CSF) (Leon et al, 2007; Mattsson et al, 2009)
We show the effect of normalization both on the original features, as well as on the dimensionality reduced version by kernel principle component analysis (KPCA) using a linear kernel
Summary
Alzheimer’s disease (AD), one of the most common cases of dementia, is an age related degenerative brain disease. Different kinds of measurements and biomarkers have been used in early detection and prediction of AD, e.g., structural brain MRI (Frisoni et al, 2010), metabolic brain alterations measured by fluorodeoxyglucose positron emission tomography (FDG-PET) (De Santi et al, 2001), and pathological amyloid depositions measured from cerebrospinal fluid (CSF) (Leon et al, 2007; Mattsson et al, 2009) Among all these measurements, Magnetic Resonance Imaging (MRI) plays an increasingly important role in early detection of Alzheimer’s disease because of its non-invasiveness, availability, and high sensitivity to change (Frisoni et al, 2010). It is commonly used as part of the clinical assessment for the diagnosis of AD
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