Locally linear embedding (LLE) for MRI based Alzheimer's disease classification

Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily ex...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2013-12, Vol.83, p.148-157
Main Authors: Liu, Xin, Tosun, Duygu, Weiner, Michael W., Schuff, Norbert
Format: Article
Language:English
Subjects:
Accuracy
Aged
Algorithms
Alzheimer Disease - pathology
Alzheimer's disease
Artificial Intelligence
Brain - pathology
Brain research
Classification
Classification of AD
Computer Simulation
Discriminant analysis
Female
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Linear Models
Locally linear embedding
Magnetic Resonance Imaging - methods
Male
Medical imaging
Methods
Middle Aged
MRI
NMR
Nuclear magnetic resonance
Pattern Recognition, Automated - methods
Remote sensing
Reproducibility of Results
Sensitivity and Specificity
Statistical learning
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Summary:Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of local linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3years with the following diagnoses: cognitive normal (CN; n=137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found that classifications using embedded MRI features generally outperformed (p
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2013.06.033