title: Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population.
abstract: Alzheimer's Disease (AD) and other neurodegenerative diseases affect over 20 million people worldwide, and this number is projected to significantly increase in the coming decades. Proposed imaging-based markers have shown steadily improving levels of sensitivity/specificity in classifying individual subjects as AD or normal. Several of these efforts have utilized statistical machine learning techniques, using brain images as input, as means of deriving such AD-related markers. A common characteristic of this line of research is a focus on either (1) using a single imaging modality for classification, or (2) incorporating several modalities, but reporting separate results for each. One strategy to improve on the success of these methods is to leverage all available imaging modalities together in a single automated learning framework. The rationale is that some subjects may show signs of pathology in one modality but not in another-by combining all available images a clearer view of the progression of disease pathology will emerge. Our method is based on the Multi-Kernel Learning (MKL) framework, which allows the inclusion of an arbitrary number of views of the data in a maximum margin, kernel learning framework. The principal innovation behind MKL is that it learns an optimal combination of kernel (similarity) matrices while simultaneously training a classifier. In classification experiments MKL outperformed an SVM trained on all available features by 3%-4%. We are especially interested in whether such markers are capable of identifying early signs of the disease. To address this question, we have examined whether our multi-modal disease marker (MMDM) can predict conversion from Mild Cognitive Impairment (MCI) to AD. Our experiments reveal that this measure shows significant group differences between MCI subjects who progressed to AD, and those who remained stable for 3 years. These differences were most significant in MMDMs based on imaging data. We also discuss the relationship between our MMDM and an individual's conversion from MCI to AD.
authors.affiliation: Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA. hinrichs@cs.wisc.edu
authors.forename: Chris
authors.initials: C
authors.lastname: Hinrichs
authors.forename: Vikas
authors.initials: V
authors.lastname: Singh
authors.forename: Guofan
authors.initials: G
authors.lastname: Xu
authors.forename: Sterling C
authors.initials: SC
authors.lastname: Johnson
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis*; D019540:Area Under Curve; D001185:Artificial Intelligence; D015415:Biomarkers; D003072:Cognition Disorders / Q000175:diagnosis; D018450:Disease Progression; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D049268:Positron-Emission Tomography; D012372:ROC Curve
chemical_list: D015415:Biomarkers