"Predictive Markers for AD in a Multi-Modality Framework: An Analysis of MCI Progression in the ADNI Population"
Chris Hinrichs, Vikas Singh, Guofan Xu, Sterling C. Johnson
This research was supported in part by NIH grants R21-AG034315
(Singh) and R01-AG021155 (Johnson). Hinrichs is funded via a
University of Wisconsin-Madison CIBM (Computation and Informatics
in Biology and Medicine) fellowship (National Library of Medicine
Award 5T15LM007359). Partial support for this research was also
provided by the University of Wisconsin-Madison UW ICTR through an
NIH Clinical and Translational Science Award (CTSA) 1UL1RR025011, a
Merit Review Grant from the Department of Veterans Affairs, the
Wisconsin Comprehensive Memory Program, and the Society for
Imaging Informatics in Medicine (SIIM).
Alzheimer's Disease (AD) and other neurodegenerative diseases affect over 20 million people world-wide, 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.
The Matlab code used in our experiments can be downloaded here, while the supplemental
materials can be found here. Please contact me at
email@example.com for the password. All rights reserved.