Abstract
Collecting multiple modalities of neuroimaging data on the same subject is increasingly becoming the norm in clinical practice and research. Fusing multiple modalities to find related patterns is a challenge in neuroimaging analysis. Canonical correlation analysis (CCA) is commonly used as a symmetric data fusion technique to find related patterns among multiple modalities. In CCA-based data fusion, principal component analysis (PCA) is frequently applied as a preprocessing step to reduce data dimension followed by CCA on dimension-reduced data. PCA, however, does not differentiate between informative voxels from non-informative voxels in the dimension reduction step. Sparse PCA (sPCA) extends traditional PCA by adding sparse regularization that assigns zero weights to non-informative voxels. In this study, sPCA is incorporated into CCA-based fusion analysis and applied on neuroimaging data. A cross-validation method is developed and validated to optimize the parameters in sPCA. Different simulations are carried out to evaluate the improvement by introducing sparsity constraint to PCA. Four fusion methods including sPCA+CCA, PCA+CCA, parallel ICA and sparse CCA were applied on structural and functional magnetic resonance imaging data of mild cognitive impairment subjects and normal controls. Our results indicate that sPCA significantly can reduce the impact of non-informative voxels and lead to improved statistical power in uncovering disease-related patterns by a fusion analysis.
Highlights
Collecting multiple modalities of neuroimaging data on the same subject is increasingly becoming the norm in clinical practice and research
The Sparse PCA (sPCA)+Canonical correlation analysis (CCA) has the best performance among these fusion methods
Results indicate that sPCA+CCA achieves correlations closest to the simulated correlations, and sparse CCA (sCCA) significantly overestimates the correlation while all other fusion methods underestimate the correlation
Summary
Collecting multiple modalities of neuroimaging data on the same subject is increasingly becoming the norm in clinical practice and research. Several techniques were proposed to utilize multiple imaging modalities, Sparse PCA in Data Fusion including data integration (Savopol and Armenakis, 2002; Calhoun and Adal, 2009), asymmetric data fusion (Filippi et al, 2001; Kim et al, 2003; Henson et al, 2010) and symmetric data fusion techniques (Correa et al, 2008; Groves et al, 2011; Sui et al, 2011; Le Floch et al, 2012; Lin et al, 2014; Mohammadi-Nejad et al, 2017). In the symmetric data fusion method, multiple imaging modalities are analyzed conjointly to optimize the information contributed by each modality.
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