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

• Published in: NeuroImage (Orlando, Fla.) Vol. 83; pp. 148 - 157
• Main Authors: Liu, Xin, Tosun, Duygu, Weiner, Michael W., Schuff, Norbert
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2013; Elsevier Limited
• 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; Statistical learning; MRI; Alzheimer's disease; Classification of AD; Locally linear embedding
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.06.033

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<0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p=0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: =0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: =0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD. •Locally linear embedding (LLE) is an unsupervised learning algorithm.•It was used to extract characteristic MR features of brain alternations.•It was used to classify normal aging subjects, MCI and AD patients from ADNI data.•The performance of predicting AD in MCIs was significantly improved by using LLE.•LLE benefitted various classifiers, such as SVM, LDA and regularized regressions.