title: Eigenanatomy: sparse dimensionality reduction for multi-modal medical image analysis.
abstract: Rigorous statistical analysis of multimodal imaging datasets is challenging. Mass-univariate methods for extracting correlations between image voxels and outcome measurements are not ideal for multimodal datasets, as they do not account for interactions between the different modalities. The extremely high dimensionality of medical images necessitates dimensionality reduction, such as principal component analysis (PCA) or independent component analysis (ICA). These dimensionality reduction techniques, however, consist of contributions from every region in the brain and are therefore difficult to interpret. Recent advances in sparse dimensionality reduction have enabled construction of a set of image regions that explain the variance of the images while still maintaining anatomical interpretability. The projections of the original data on the sparse eigenvectors, however, are highly collinear and therefore difficult to incorporate into multi-modal image analysis pipelines. We propose here a method for clustering sparse eigenvectors and selecting a subset of the eigenvectors to make interpretable predictions from a multi-modal dataset. Evaluation on a publicly available dataset shows that the proposed method outperforms PCA and ICA-based regressions while still maintaining anatomical meaning. To facilitate reproducibility, the complete dataset used and all source code is publicly available.
authors.affiliation: Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States. Electronic address: bkandel@seas.upenn.edu.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Kandel
authors.affiliation: Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States.
authors.forename: Danny J J
authors.initials: DJ
authors.lastname: Wang
authors.affiliation: Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: James C
authors.initials: JC
authors.lastname: Gee
authors.affiliation: Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Brian B
authors.initials: BB
authors.lastname: Avants
mesh_terms: D000293:Adolescent; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D064847:Multimodal Imaging / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*
keywords: Magnetic resonance imaging; Multi-modal; Pediatric; Sparse