Nonlinear dimensionality reduction combining MR imaging with non-imaging information

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• 作者：Robin Wolz^a ; ^{r.wolz@imperial.ac.uk} ; Paul Aljabar^a ; Joseph V. Hajnal^b ; Jyrki Lö ; tjö ; nen^c ; Daniel Rueckert^a ; The Alzheimer&rsquo ; s Disease Neuroimaging Initiative¹
• 关键词：Manifold learning ; Laplacian Eigenmaps ; Classification ; Metadata ; Alzheimer&rsquo ; s disease
• 刊名：Medical Image Analysis
• 出版年：2012
• 出版时间：May, 2012
• 年：2012
• 卷：16
• 期：4
• 页码：819-830
• 全文大小：787 K

文摘

We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter-subject brain variation. Manifold coordinates of each image capture information about structural shape and appearance and, when a phenotype exists, about the subject¡¯s clinical state. Our framework incorporates subject meta-information into the manifold learning step. Apart from gender and age, information such as genotype or a derived biomarker is often available in clinical studies and can inform the classification of a query subject. Such information, whether discrete or continuous, is used as an additional input to manifold learning, extending the Laplacian Eigenmap objective function and enriching a similarity measure derived from pairwise image similarities. The biomarkers identified with the proposed method are data-driven in contrast to a priori defined biomarkers derived from, e.g., manual or automated segmentations. They form a unified representation of both the imaging and non-imaging measurements, providing a natural use for data analysis and visualization. We test the method to classify subjects with Alzheimer¡¯s Disease (AD), mild cognitive impairment (MCI) and healthy controls enrolled in the ADNI study. Non-imaging metadata used are ApoE genotype, a risk factor associated with AD, and the CSF-concentration of , an established biomarker for AD. In addition, we use hippocampal volume as a derived imaging-biomarker to enrich the learned manifold. Our classification results compare favorably to what has been reported in a recent meta-analysis using established neuroimaging methods on the same database.