Journal article

Two dimensional CCA via penalized matrix decomposition for structure preserved fMRI data analysis

Muhammad Ali Qadar, Abdeldjalil Aissa-El-Bey, Abd-Krim Seghouane

Digital Signal Processing | Academic Press | Published : 2019

Abstract

Two dimensional canonical correlation analysis (2DCCA) is a data driven method that has been used to preserve the local spatial structure of functional magnetic resonance (fMR) images and to detect brain activation patterns. 2DCCA finds pairs of left and right linear transforms by directly operating on two dimensional data (i.e., image data) such that the correlation between their projections is maximized without neglecting the local spatial structure of the data. However, in the context of high dimensional data, the performance of 2DCCA suffers from interpretability of learned projection variables. In this study, to improve the interpretability of projection variables while preserving the l..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council


Awarded by NIH Blueprint for Neuroscience Research


Funding Acknowledgements

This work was supported in part by the Australian Research Council through Grant FT. 130101394.Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.